Carbon footprint management system and method based on big data analysis

Abstract

The invention belongs to the technical field of carbon footprint management, and relates to a carbon footprint management system and method based on big data analysis, and the method comprises the steps: defining all stages and boundaries of the life cycle of a battery based on the full life cycle record of the battery; collecting carbon footprint related data of each stage of the life cycle of the battery according to the boundary of each stage of the life cycle of the battery; processing the carbon footprint related data of each stage of the battery life cycle to obtain carbon footprint features of each stage of the battery life cycle, and generating a carbon footprint data set; establishing a carbon footprint evaluation model based on the carbon footprint data set, analyzing the carbon footprint of the battery in the whole life cycle, and evaluating the performance of the carbon footprint in each stage of the life cycle of the battery; according to the method, the whole life cycle of the battery is comprehensively considered, an enterprise is helped to perform targeted management on the carbon footprint of the batteries in the same batch, and an effective data source is provided for the carbon footprint in the digital passport of the battery.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon footprint management, and in particular relates to a carbon footprint management system and method based on big data analysis. Background Art

[0002] As the concept of sustainable development is promoted, the new EU battery law stipulates that most batteries exported to Europe need to provide carbon footprint declarations and labels. The implementation of this regulation has had a significant impact on the exports of Chinese battery companies, forcing battery companies to begin to deeply sort out every carbon emission link in the supply chain.

[0003] Existing technologies usually calculate the carbon footprint of power batteries based on life cycle assessment theory. However, the life of power batteries is usually long, and the recycling and treatment process of power batteries has a significant impact on the carbon footprint; and due to the dynamic nature of time, there is uncertainty in the performance of power batteries throughout their life cycle.

[0004] Therefore, the present invention intends to propose a carbon footprint management system and method based on big data analysis, to improve the life cycle assessment method of power batteries and realize carbon footprint management of power batteries. Summary of the invention

[0005] The purpose of the present invention is to provide a carbon footprint management system and method based on big data analysis to solve the problems raised in the above background technology.

[0006] In order to achieve the purpose of the present invention, the present invention will be implemented by adopting the following technical solutions.

[0007] A carbon footprint management system based on big data analysis, the system comprising:

[0008] Battery data collection module, used to collect records of the entire life cycle of the target battery;

[0009] The multi-source data processing module is used to process the data obtained by the battery data set acquisition module; establish a carbon footprint data set, recorded as data_CF = {Cf i |i∈[1,N]}, where Cf i Represents the carbon footprint characteristics of the i-th stage of the battery life cycle;

[0010] Carbon footprint assessment model building module, used to build a carbon footprint assessment model to assess the carbon footprint of batteries at various stages of their life cycle;

[0011] Carbon footprint analysis module, used to analyze the carbon footprint of the battery based on the carbon footprint assessment results of the battery at each stage of its life cycle;

[0012] The life cycle management module is used to evaluate the carbon footprint performance of the battery at each stage of its life cycle based on the analysis results of the carbon footprint analysis module.

[0013] Furthermore, the carbon footprint assessment model quantifies the carbon footprint performance of each stage of the battery, and converts the carbon footprint performance of N batteries in the same batch that have completed the Nth stage of their life cycle into a linear equation system according to the following formula:

[0014] a 11 Cf1+a 12 Cf1+...+a 1N Cf N = b1;

[0015] a 21 Cf1+a 22 Cf1+...+a 2N Cf N = b2; ...

[0017] a N1 Cf1+a N2 Cf1+...+a NN Cf N =b N ;

[0018] Among them, a nm represents the coefficient of the equation system, b m represents the carbon emissions of the mth battery in the same batch, n∈[1,N], m∈[1,N];

[0019] The carbon footprint performance of the batteries in the same batch that have completed the Nth stage of their life cycle is predicted based on the carbon footprint performance of the batteries in the same batch that have not completed the Nth stage of their life cycle. The carbon footprint characteristics of the mth stage of the k+1th battery in the same batch are calculated using the following formula:

[0020]

[0021] In the formula, Cf m (k+1) represents the carbon footprint characteristics of the mth stage of the k+1th battery in the same batch; Cf n (k) represents the carbon footprint characteristics of the k-th battery in the same batch at the n-th stage; a nn represents the diagonal elements in the nth row and nth column of the linear equation system, a mn Represents the element in the mth row and nth column of the linear system.

[0022] Further, the carbon footprint analysis module includes a battery status acquisition unit, a carbon footprint performance prediction unit and a carbon emission analysis unit;

[0023] The battery status acquisition unit is used to acquire the battery morphology of batteries of the same batch of the target battery, and to capture the stage of the life cycle of the batteries of the same batch;

[0024] The carbon footprint performance prediction unit is used to predict the carbon footprint performance of batteries in the same batch that have not completed their full life cycle in the remaining unfinished stage according to the carbon footprint performance of batteries in the same batch that have completed their full life cycle;

[0025] The carbon emission analysis unit is used to analyze the carbon emissions of the same batch of batteries according to the output of the carbon footprint performance prediction unit and the relevant data collected by the battery data collection module.

[0026] Further, the life cycle management module includes a battery data monitoring unit and an evaluation feedback unit;

[0027] The battery data monitoring unit is used to mark the battery and its over-value stage that exceed the preset standard value according to the output of the carbon footprint analysis module;

[0028] The evaluation feedback unit is used to provide feedback to relevant personnel on batteries with a risk of exceeding carbon emissions and their exceeding value stages according to the markings of the battery data monitoring unit.

[0029] A carbon footprint management method based on big data analysis includes the following steps:

[0030] Step S1: defining each stage of the battery life cycle based on the full life cycle record of the battery, and defining the boundaries of each stage of the battery life cycle according to the morphological variables of the battery during the life cycle;

[0031] Step S2: Collecting data related to the carbon footprint of each stage of the battery life cycle according to the boundaries of the battery at each stage of the life cycle;

[0032] Step S3: According to the data characteristics of the carbon footprint related data, the carbon footprint related data of each stage of the battery life cycle are processed respectively to obtain the carbon footprint characteristics of each stage of the battery life cycle and generate a carbon footprint data set;

[0033] Step S4: Establishing a carbon footprint assessment model based on the carbon footprint data set to analyze the carbon footprint of the battery throughout its life cycle;

[0034] Step S5: Evaluate the carbon footprint performance of each stage of the battery life cycle based on the output results of the carbon footprint assessment model in step S4.

[0035] Furthermore, the implementation process of step S1 includes the following steps:

[0036] S1-1: Obtain a full life cycle record of the sample battery, wherein the full life cycle record includes a timeline and a battery form, and each form of the battery is defined as a stage of the battery life cycle; if the sample battery can be recycled and remanufactured after the end of its initial service life, it is considered that the battery life cycle has N stages; if the sample battery cannot be recycled and remanufactured after the end of its initial service life, it is considered that the battery life cycle has N-1 stages;

[0037] S1-2: According to the retention time of the battery morphology on the time axis, the time points when the battery morphology changes are marked as the boundaries of each stage of the battery life cycle; the time points marked on the time axis are integrated and recorded as the boundary set BT = {(t j , t j+1 )|j∈[1,N]}, where t j Indicates the starting time of the jth stage of the battery life cycle, t j+1 represents the end time of the jth stage of the battery life cycle, and N represents the total number of stages in the battery life cycle.

[0038] Furthermore, in step S2, according to the start time and end time of each stage of the battery life cycle, the carbon footprint related data of the battery life cycle in the time period corresponding to each stage is collected, including emission factors and energy consumption.

[0039] Furthermore, the implementation process of step S3 includes the following steps:

[0040] S3-1: According to the data characteristics of carbon footprint-related data, the carbon footprint-related data of each stage of the battery life cycle are processed separately, and data of different units are converted into a unified unit for easy comparison and calculation. The data are converted to the same scale to avoid the impact of different dimensions. For data with extremely skewed distribution, logarithmic transformation is used to improve the data distribution;

[0041] S3-2: Summarize the data processed by S3-1 by unit time through time series aggregation, generate the carbon footprint characteristics of each stage of the battery life cycle through data fusion technology, and establish a carbon footprint data set, recorded as data_CF = {Cf i |i∈[1,N]}, where Cf i Represents the carbon footprint characteristics of the i-th stage of the battery life cycle.

[0042] Furthermore, the implementation process of step S4 includes the following steps:

[0043] S4-1: A carbon footprint assessment model is established based on the carbon footprint data set. The carbon footprint assessment model quantifies the carbon footprint performance of each stage of the battery and converts the carbon footprint performance of N batteries in the same batch that have completed the Nth stage of their life cycle into a linear equation system according to the following formula:

[0044] a 11 Cf1+a 12 Cf1+...+a 1N Cf N = b1;

[0045] a 21 Cf1+a 22 Cf1+...+a 2N Cf N = b2; ...

[0047] a N1 Cf1+a N2 Cf1+...+a NN Cf N =b N ;

[0048] Among them, a nm represents the coefficient of the equation system, b m represents the carbon emissions of the mth battery in the same batch, n∈[1,N], m∈[1,N];

[0049] S4-2: The carbon footprint performance of the batteries in the same batch that have completed the Nth stage of their life cycle is predicted based on the carbon footprint performance of the batteries in the same batch that have not completed the Nth stage of their life cycle. The carbon footprint characteristics of the mth stage of the k+1th battery in the same batch are calculated using the following formula:

[0050]

[0051] In the formula, Cf m (k+1) represents the carbon footprint characteristics of the mth stage of the k+1th battery in the same batch; Cf n (k) represents the carbon footprint characteristics of the k-th battery in the same batch at the n-th stage; a nn represents the diagonal elements in the nth row and nth column of the linear equation system, a mn Represents the element in the mth row and nth column of the linear equation system;

[0052] S4-3: When analyzing the carbon footprint of the battery throughout its life cycle based on the carbon footprint assessment model, the carbon footprint performance of all batteries in the same batch that have not completed the Nth stage of their life cycle is predicted according to step S4-2, and the calculated Cf m(k+1) Replace Cf in the next calculation m (k) , repeat step S4-2 until all unfinished stages of all batteries in the same batch that have not completed the Nth stage of their life cycle are predicted;

[0053] The carbon emissions of batteries in the same batch that have not completed the Nth stage of their life cycle are predicted according to the following formula:

[0054]

[0055] In the formula, CP i (k+N) represents the carbon emissions of the i-th stage of the k+N-th battery in the same batch; E represents the total number of batteries in the same batch, P[Cf i (k) ] represents the carbon emission of the i-th stage of the k-th battery in the same batch;

[0056] According to the above method, batteries from the same batch are integrated by constructing a carbon footprint assessment model, and the carbon footprint performance of the batteries at each stage of their life cycle is quantified. The carbon footprint characteristics of each stage of the battery life cycle are combined with the carbon emissions to generate a set of linear equations. Then, the carbon footprint performance of batteries in the same batch that have not completed the Nth stage of their life cycle is predicted based on the batteries that have completed the Nth stage of their life cycle. The data is continuously updated, and the newly collected data and the newly predicted data are added to the cycle to output the carbon emissions of batteries in the same batch that have not completed the Nth stage of their life cycle at each stage, thereby realizing the carbon footprint analysis of batteries from the same batch in the same dimension, and more accurately counting the carbon footprint of batteries from the same batch, providing an effective data source for the carbon footprint in the digital passport of the battery.

[0057] Furthermore, in step S5, based on the output result of the carbon footprint assessment model in step S4, when the carbon emissions of the i-th stage of the k+N-th battery in the same batch exceed the preset standard value, the carbon emission behavior of the unfinished Ni stages of the battery is prompted; and the carbon footprint performance of other batteries in the same batch that have not completed the i-th stage of their life cycle is marked, prompting relevant personnel to supervise the carbon emission behavior of the batteries in the i-th stage of their life cycle to avoid the situation where the carbon emissions exceed the preset standard value, thereby further ensuring the compliance of the battery.

[0058] Beneficial effects:

[0059] (1) The present invention defines the stage boundaries through morphological variables, which can more finely divide the various stages of the battery life cycle, comprehensively consider all aspects of the battery from raw materials to waste treatment, and ensure the integrity of the carbon footprint assessment.

[0060] (2) The present invention provides a standardized data basis for carbon footprint assessment of batteries by generating a carbon footprint data set, establishes a carbon footprint assessment model based on the actual collected data, improves the transparency of the carbon footprint of the battery life cycle, and makes the carbon footprints of different batteries in the same batch or at different stages of the same battery more comparable and synergistic.

[0061] (3) The present invention identifies the high carbon emission stages in the same batch of batteries and in the life cycle of the same battery, evaluates the carbon footprint performance at each stage of the battery life cycle, helps enterprises to carry out targeted management of the same batch of batteries, and helps to formulate more effective carbon emission reduction strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flow chart of a carbon footprint management method based on big data analysis of the present invention;

[0063] Figure 2 It is a structural schematic diagram of a carbon footprint management system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0064] The technical solution of the present invention will be clearly and completely described below in combination with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] As an embodiment of the present invention, Figure 1 As shown, the present invention provides a carbon footprint management method based on big data analysis, comprising the following steps:

[0066] Step S1: defining each stage of the battery life cycle based on the full life cycle record of the battery, and defining the boundaries of each stage of the battery life cycle according to the morphological variables of the battery during the life cycle;

[0067] S1-1: Obtain a full life cycle record of the sample battery, wherein the full life cycle record includes a timeline and a battery form, and each form of the battery is defined as a stage of the battery life cycle; if the sample battery can be recycled and remanufactured after the end of its initial service life, it is considered that the battery life cycle has N stages; if the sample battery cannot be recycled and remanufactured after the end of its initial service life, it is considered that the battery life cycle has N-1 stages;

[0068] S1-2: According to the retention time of the battery morphology on the time axis, the time points when the battery morphology changes are marked as the boundaries of each stage of the battery life cycle; the time points marked on the time axis are integrated and recorded as the boundary set BT = {(t j , t j+1 )|j∈[1,N]}, where t j Indicates the starting time of the jth stage of the battery life cycle, t j+1 represents the end time of the jth stage of the battery life cycle, and N represents the total number of stages in the battery life cycle.

[0069] Exemplarily, when defining the stages of the battery life cycle according to the form of the battery, the battery in the raw material form is defined as the first stage, the raw materials are processed into the battery component form is defined as the second stage, the battery component is assembled into the battery cell form is defined as the third stage, the battery cell is loaded into the equipment for use form is defined as the fourth stage, the battery cell separation equipment form is defined as the fifth stage, the battery cell separation equipment form is defined as the sixth stage, the disassembled form of the battery cell is defined as the seventh stage, the recycled form of the disassembled battery cell generated by remanufacturing is defined as the seventh stage, and the waste form generated by the disassembled battery cell through disposal is defined as the eighth stage.

[0070] Step S2: According to the start time and end time of each stage of the battery life cycle, collect the carbon footprint related data of the battery life cycle in the time period corresponding to each stage, including emission factors and energy consumption.

[0071] Step S3: According to the data characteristics of the carbon footprint related data, the carbon footprint related data of each stage of the battery life cycle are processed respectively to obtain the carbon footprint characteristics of each stage of the battery life cycle and generate a carbon footprint data set;

[0072] S3-1: According to the data characteristics of carbon footprint-related data, the carbon footprint-related data of each stage of the battery life cycle are processed separately, and data of different units are converted into a unified unit for easy comparison and calculation. The data are converted to the same scale to avoid the impact of different dimensions. For data with extremely skewed distribution, logarithmic transformation is used to improve the data distribution;

[0073] S3-2: Summarize the data processed by S3-1 by unit time through time series aggregation, generate the carbon footprint characteristics of each stage of the battery life cycle through Bayesian fusion, and establish a carbon footprint data set, recorded as data_CF = {Cf i |i∈[1,N]}, where Cf i Represents the carbon footprint characteristics of the i-th stage of the battery life cycle.

[0074] Step S4: Establishing a carbon footprint assessment model based on the carbon footprint data set to analyze the carbon footprint of the battery throughout its life cycle;

[0075] S4-1: A carbon footprint assessment model is established based on the carbon footprint data set. The carbon footprint assessment model quantifies the carbon footprint performance of each stage of the battery and converts the carbon footprint performance of N batteries in the same batch that have completed the Nth stage of their life cycle into a linear equation system according to the following formula:

[0076] a 11 Cf1+a 12 Cf1+...+a 1N Cf N = b1;

[0077] a 21 Cf1+a 22 Cf1+...+a 2N Cf N = b2; ...

[0079] a N1 Cf1+a N2 Cf1+...+a NN Cf N =b N ;

[0080] Among them, a nm represents the coefficient of the equation system, b m represents the carbon emissions of the mth battery in the same batch, n∈[1,N], m∈[1,N];

[0081] S4-2: The carbon footprint performance of the batteries in the same batch that have completed the Nth stage of their life cycle is predicted based on the carbon footprint performance of the batteries in the same batch that have not completed the Nth stage of their life cycle. The carbon footprint characteristics of the mth stage of the k+1th battery in the same batch are calculated using the following formula:

[0082]

[0083] In the formula, Cf m (k+1) represents the carbon footprint characteristics of the mth stage of the k+1th battery in the same batch; Cf n (k) represents the carbon footprint characteristics of the k-th battery in the same batch at the n-th stage; a nn represents the diagonal elements in the nth row and nth column of the linear equation system, a mn Represents the element in the mth row and nth column of the linear equation system;

[0084] S4-3: When analyzing the carbon footprint of the battery throughout its life cycle based on the carbon footprint assessment model, the carbon footprint performance of all batteries in the same batch that have not completed the Nth stage of their life cycle is predicted according to step S4-2, and the calculated Cf m (k+1) Replace Cf in the next calculation m (k) , repeat step S4-2 until all unfinished stages of all batteries in the same batch that have not completed the Nth stage of their life cycle are predicted;

[0085] The carbon emissions of batteries in the same batch that have not completed the Nth stage of their life cycle are predicted according to the following formula:

[0086]

[0087] In the formula, CP i (k+N) represents the carbon emissions of the i-th stage of the k+N-th battery in the same batch; E represents the total number of batteries in the same batch, P[Cf i (k) ] represents the carbon emission of the i-th stage of the k-th battery in the same batch;

[0088] According to the above method, batteries from the same batch are integrated by constructing a carbon footprint assessment model, and the carbon footprint performance of the batteries at each stage of their life cycle is quantified. The carbon footprint characteristics of each stage of the battery life cycle are combined with the carbon emissions to generate a set of linear equations. Then, the carbon footprint performance of batteries in the same batch that have not completed the Nth stage of their life cycle is predicted based on the batteries that have completed the Nth stage of their life cycle. The data is continuously updated, and the newly collected data and the newly predicted data are added to the cycle to output the carbon emissions of batteries in the same batch that have not completed the Nth stage of their life cycle at each stage, thereby realizing the carbon footprint analysis of batteries from the same batch in the same dimension, and more accurately counting the carbon footprint of batteries from the same batch, providing an effective data source for the carbon footprint in the digital passport of the battery.

[0089] Step S5: According to the output results of the carbon footprint assessment model in step S4, when the carbon emissions of the i-th stage of the k+N-th battery in the same batch exceed the preset standard value, the carbon emission behavior of the unfinished Ni stages of the battery will be prompted; and the carbon footprint performance of other batteries in the same batch that have not completed the i-th stage of their life cycle will be marked, prompting relevant personnel to supervise the carbon emission behavior of the batteries in the i-th stage of their life cycle to avoid the situation where the carbon emissions exceed the preset standard value, thereby further ensuring the compliance of the battery.

[0090] As an embodiment of the present invention, Figure 2As shown, a carbon footprint management system based on big data analysis is provided, the system comprising:

[0091] Battery data collection module, used to collect records of the entire life cycle of the target battery;

[0092] The multi-source data processing module is used to process the data obtained by the battery data set acquisition module; establish a carbon footprint data set, recorded as data_CF = {Cf i |i∈[1,N]}, where Cf i Represents the carbon footprint characteristics of the i-th stage of the battery life cycle;

[0093] The carbon footprint assessment model establishment module is used to establish a carbon footprint assessment model to assess the carbon footprint of batteries at various stages of their life cycle. The carbon footprint assessment model quantifies the carbon footprint performance of batteries at various stages and converts the carbon footprint performance of N batteries in the same batch that have completed the Nth stage of their life cycle into a linear equation system according to the following formula:

[0094] a 11 Cf1+a 12 Cf1+...+a 1N Cf N = b1;

[0095] a 21 Cf1+a 22 Cf1+...+a 2N Cf N = b2; ...

[0097] a N1 Cf1+a N2 Cf1+...+a NN Cf N =b N ;

[0098] Among them, a nm represents the coefficient of the equation system, b m represents the carbon emissions of the mth battery in the same batch, n∈[1,N], m∈[1,N];

[0099] The carbon footprint performance of the batteries in the same batch that have completed the Nth stage of their life cycle is predicted based on the carbon footprint performance of the batteries in the same batch that have not completed the Nth stage of their life cycle. The carbon footprint characteristics of the mth stage of the k+1th battery in the same batch are calculated using the following formula:

[0100]

[0101] In the formula, Cf m (k+1)represents the carbon footprint characteristics of the mth stage of the k+1th battery in the same batch; Cf n (k) represents the carbon footprint characteristics of the k-th battery in the same batch at the n-th stage; a nn represents the diagonal elements in the nth row and nth column of the linear equation system, a mn Represents the element in the mth row and nth column of the linear system.

[0102] A carbon footprint analysis module is used to analyze the carbon footprint of the battery according to the carbon footprint assessment results of the battery at each stage of its life cycle; wherein the carbon footprint analysis module includes a battery status acquisition unit, a carbon footprint performance prediction unit and a carbon emission analysis unit;

[0103] The battery status acquisition unit is used to acquire the battery morphology of batteries of the same batch of the target battery, and to capture the life cycle stage of the batteries of the same batch;

[0104] The carbon footprint performance prediction unit is used to predict the carbon footprint performance of batteries in the same batch that have not completed their full life cycle in the remaining unfinished stage based on the carbon footprint performance of batteries in the same batch that have completed their full life cycle;

[0105] The carbon emission analysis unit is used to analyze the carbon emissions of the same batch of batteries based on the output of the carbon footprint performance prediction unit and the relevant data collected by the battery data acquisition module.

[0106] A life cycle management module, which is used to evaluate the carbon footprint performance of each stage of the battery's life cycle based on the analysis results of the carbon footprint analysis module; wherein the life cycle management module includes a battery data monitoring unit and an evaluation feedback unit;

[0107] The battery data monitoring unit is used to mark the battery and its over-value stage that exceed the preset standard value according to the output of the carbon footprint analysis module;

[0108] The evaluation feedback unit is used to provide feedback to relevant personnel on batteries that have a risk of exceeding carbon emissions and their exceeding value stages based on the markings of the battery data monitoring unit.

[0109] The technical solution of the present invention is described in detail above in conjunction with the embodiments / drawings, but the present invention is not limited to the above technical solution. For ordinary technicians in this technical field, after knowing the contents recorded in the present invention, they can make several equivalent changes and substitutions without departing from the principle of the present invention. These equivalent changes and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. A carbon footprint management system based on big data analysis, characterized in that: The system comprises: Battery data collection module, used to collect records of the entire life cycle of the target battery; The multi-source data processing module is used to process the data obtained by the battery data set acquisition module; establish a carbon footprint data set, recorded as data_CF = {Cf i |i∈[1,N]}, where Cf i Represents the carbon footprint characteristics of the i-th stage of the battery life cycle; Carbon footprint assessment model building module, used to build a carbon footprint assessment model to assess the carbon footprint of batteries at various stages of their life cycle; Carbon footprint analysis module, used to analyze the carbon footprint of the battery based on the carbon footprint assessment results of the battery at each stage of its life cycle; The life cycle management module is used to evaluate the carbon footprint performance of the battery at each stage of its life cycle based on the analysis results of the carbon footprint analysis module.

2. A carbon footprint management system based on big data analysis according to claim 1, characterized in that: The carbon footprint assessment model quantifies the carbon footprint performance of each stage of the battery, and converts the carbon footprint performance of N batteries in the same batch that have completed the Nth stage of their life cycle into a linear equation system according to the following formula: a 11 Cf1+a 12 Cf1+...+a 1N Cf N =b1; a 21 Cf1+a 22 Cf1+...+a 2N Cf N =b2; ... a N1 Cf1+a N2 Cf1+...+a NN Cf N =b N ; Among them, a nm represents the coefficient of the equation system, b m represents the carbon emissions of the mth battery in the same batch, n∈[1,N], m∈[1,N]; The carbon footprint performance of the batteries in the same batch that have completed the Nth stage of their life cycle is predicted based on the carbon footprint performance of the batteries in the same batch that have not completed the Nth stage of their life cycle. The carbon footprint characteristics of the mth stage of the k+1th battery in the same batch are calculated using the following formula: In the formula, Cf m (k+1) represents the carbon footprint characteristics of the mth stage of the k+1th battery in the same batch; Cf n (k) represents the carbon footprint characteristics of the k-th battery in the same batch at the n-th stage; a nn represents the diagonal elements in the nth row and nth column of the linear equation system, a mn Represents the element in the mth row and nth column of the linear system.

3. The carbon footprint management system based on big data analysis according to claim 1 is characterized in that: The carbon footprint analysis module includes a battery status acquisition unit, a carbon footprint performance prediction unit and a carbon emission analysis unit; The battery status acquisition unit is used to acquire the battery morphology of batteries of the same batch of the target battery, and to capture the stage of the life cycle of the batteries of the same batch; The carbon footprint performance prediction unit is used to predict the carbon footprint performance of batteries in the same batch that have not completed their full life cycle in the remaining unfinished stage according to the carbon footprint performance of batteries in the same batch that have completed their full life cycle; The carbon emission analysis unit is used to analyze the carbon emissions of the same batch of batteries according to the output of the carbon footprint performance prediction unit and the relevant data collected by the battery data collection module.

4. The carbon footprint management system based on big data analysis according to claim 1, characterized in that: The life cycle management module includes a battery data monitoring unit and an evaluation feedback unit; The battery data monitoring unit is used to mark the battery and its over-value stage that exceed the preset standard value according to the output of the carbon footprint analysis module; The evaluation feedback unit is used to provide feedback to relevant personnel on batteries with a risk of exceeding carbon emissions and their exceeding value stages according to the markings of the battery data monitoring unit.

5. A carbon footprint management method based on big data analysis, characterized in that: The following steps are involved: Step S1: defining each stage of the battery life cycle based on the full life cycle record of the battery, and defining the boundaries of each stage of the battery life cycle according to the morphological variables of the battery during the life cycle; Step S2: Collecting data related to the carbon footprint of each stage of the battery life cycle according to the boundaries of the battery at each stage of the life cycle; Step S3: According to the data characteristics of the carbon footprint related data, the carbon footprint related data of each stage of the battery life cycle are processed respectively to obtain the carbon footprint characteristics of each stage of the battery life cycle and generate a carbon footprint data set; Step S4: Establishing a carbon footprint assessment model based on the carbon footprint data set to analyze the carbon footprint of the battery throughout its life cycle; Step S5: Evaluate the carbon footprint performance of each stage of the battery life cycle based on the output results of the carbon footprint assessment model in step S4.

6. The carbon footprint management method based on big data analysis according to claim 5 is characterized by: The implementation process of step S1 includes the following steps: S1-1: Obtain a full life cycle record of the sample battery, wherein the full life cycle record includes a timeline and a battery form, and each form of the battery is defined as a stage of the battery life cycle; S1-2: According to the retention time of the battery morphology on the time axis, the time points when the battery morphology changes are marked as the boundaries of each stage of the battery life cycle; the time points marked on the time axis are integrated and recorded as the boundary set BT = {(t j , t j+1 )|j∈[1,N]}, where t j Indicates the starting time of the jth stage of the battery life cycle, t j+1 represents the end time of the jth stage of the battery life cycle, and N represents the total number of stages in the battery life cycle.

7. The carbon footprint management method based on big data analysis according to claim 5 is characterized by: In step S2, according to the start time and end time of each stage of the battery life cycle, the carbon footprint related data of the battery life cycle in the time period corresponding to each stage is collected, including emission factors and energy consumption.

8. The carbon footprint management method based on big data analysis according to claim 5 is characterized by: The implementation process of step S3 includes the following steps: S3-1: According to the data characteristics of carbon footprint related data, process the carbon footprint related data of each stage of the battery life cycle separately, and convert the data in different units into a unified unit; S3-2: Summarize the data processed by S3-1 by unit time through time series aggregation, generate the carbon footprint characteristics of each stage of the battery life cycle through data fusion technology, and establish a carbon footprint data set, recorded as data_CF = {Cf i |i∈[1,N]}, where Cf i Represents the carbon footprint characteristics of the i-th stage of the battery life cycle.

9. The carbon footprint management method based on big data analysis according to claim 5, characterized in that: The implementation process of step S4 includes the following steps: S4-1: A carbon footprint assessment model is established based on the carbon footprint data set. The carbon footprint assessment model quantifies the carbon footprint performance of each stage of the battery and converts the carbon footprint performance of N batteries in the same batch that have completed the Nth stage of their life cycle into a linear equation system according to the following formula: a 11 Cf1+a 12 Cf1+...+a 1N Cf N =b1; a 21 Cf1+a 22 Cf1+...+a 2N Cf N =b2; ... a N1 Cf1+a N2 Cf1+...+a NN Cf N =b N ; Among them, a nm represents the coefficient of the equation system, b m represents the carbon emissions of the mth battery in the same batch, n∈[1,N], m∈[1,N]; S4-2: The carbon footprint performance of the batteries in the same batch that have completed the Nth stage of their life cycle is predicted based on the carbon footprint performance of the batteries in the same batch that have not completed the Nth stage of their life cycle. The carbon footprint characteristics of the mth stage of the k+1th battery in the same batch are calculated using the following formula: In the formula, Cf m (k+1) represents the carbon footprint characteristics of the mth stage of the k+1th battery in the same batch; Cf n (k) represents the carbon footprint characteristics of the k-th battery in the same batch at the n-th stage; a nn represents the diagonal elements in the nth row and nth column of the linear equation system, a mn Represents the element in the mth row and nth column of the linear equation system; S4-3: When analyzing the carbon footprint of the battery throughout its life cycle based on the carbon footprint assessment model, the carbon footprint performance of all batteries in the same batch that have not completed the Nth stage of their life cycle is predicted according to step S4-2, and the calculated Cf m (k+1) Replace Cf in the next calculation m (k) , repeat step S4-2 until all unfinished stages of all batteries in the same batch that have not completed the Nth stage of their life cycle are predicted; The carbon emissions of batteries in the same batch that have not completed the Nth stage of their life cycle are predicted according to the following formula: In the formula, CP i (k+N) represents the carbon emissions of the i-th stage of the k+N-th battery in the same batch; E represents the total number of batteries in the same batch, P[Cf i (k) ] represents the carbon emissions of the i-th stage of the k-th battery in the same batch.

10. The carbon footprint management method based on big data analysis according to claim 9, characterized in that: Step S5: according to the output result of the carbon footprint assessment model in step S4, when the carbon emission of the i-th stage of the k+N-th battery in the same batch exceeds the preset standard value, the carbon emission behavior of the unfinished Ni stages of the battery is prompted; The carbon footprint performance of other batteries in the same batch that have not completed the i-th stage of their life cycle will be marked, prompting relevant personnel to supervise the carbon emissions of batteries in the i-th stage of their life cycle.

Images