Method and system for quantitative analysis of carbon emissions based on power battery recycling

Abstract

The present invention relates to the technical field of carbon emissions, and in particular relates to a method and a system for quantitative analysis of carbon emissions based on power battery recycling. The method comprises: sampling battery recovery processing data of each stage of power battery recycling at specific sampling intervals during a sampling period; extracting the battery recovery processing data at each stage of power battery recycling, to obtain a corresponding time-quantized feature; conducting an influencing factor analysis on the time-quantized features, to obtain a key influencing factor for carbon emissions; and obtaining quantitative information of power battery carbon emissions according to the key influencing factor for carbon emissions and time-quantized features. In the invention, by means of performing quantitative processing on a battery recycling processing data at various stages of power battery regeneration utilization, a comprehensive and accurate analysis of carbon emission in the regeneration utilization process is achieved. Furthermore, quantitative information on power battery carbon emissions is optimized and adjusted on the basis of reinforcement learning, thereby increasing the accuracy of quantification of carbon emissions.

Description

A carbon emission quantification analysis method and system based on power battery recycling Technical Field

[0001] The present invention relates to the field of carbon emission technology, and in particular to a carbon emission quantitative analysis method and system based on power battery recycling. Background Art

[0002] With the advent of the era of full electrification, power batteries are increasingly becoming important energy storage devices. At present, the carbon emissions generated by the production of electric vehicles are higher than those of traditional fuel vehicles. Among them, the carbon emissions of power batteries in the production process can account for 36% of the entire life cycle of power batteries. Since most of the materials of the batteries can be reused when they are retired, some researchers have stated that the total retirement volume of power batteries in my country is expected to reach 380.3 GWh in 2030. From 2021 to 2030, the annual compound growth rate will be as high as 48.9%, and it is expected to show exponential growth in the future. Therefore, the large-scale use of recycled batteries is also one of the important means to achieve low-carbon batteries. The raw materials obtained after the retired power batteries are disassembled, crushed and refined again, such as nickel, cobalt, lithium, etc., can become the raw materials for the next batch of batteries and be provided to battery suppliers for the production of new power batteries, realizing the closed-loop management of power battery raw materials. Therefore, the recycling of power batteries will not only generate extremely high economic value, but also greatly reduce carbon dioxide emissions in the production process.

[0003] The manufacturing and recycling of power batteries will generate a large amount of carbon emissions. However, there is currently little research on the recycling of power batteries in China, and there is no complete carbon emission quantification calculation system for the power battery recycling industry, which restricts the scale development of the entire industry chain. Therefore, how to quantify and analyze these carbon emissions has become an important issue in research in this field.

[0004] Summary of the Invention

[0005] The purpose of the present invention is to provide a carbon emission quantification analysis method and system based on power battery recycling, so as to achieve a comprehensive and accurate analysis of the carbon emission status of the recycling process and improve the accuracy of carbon emission quantification.

[0006] In order to solve the above technical problems, the present invention provides a carbon emission quantitative analysis method and system based on power battery recycling.

[0007] In a first aspect, the present invention provides a method for quantitatively analyzing carbon emissions based on power battery recycling, the method comprising the following steps:

[0008] Based on the physical and chemical reactions of the entire power battery recycling process, battery recycling data of the power battery at each stage of recycling is collected at specific sampling time intervals within a sampling period. The battery recycling data includes equipment operation data, energy consumption data, and waste energy and waste heat recovery analysis data.

[0009] Extract battery recycling data at each stage of power battery recycling and obtain corresponding time quantification features;

[0010] Performing an influencing factor analysis on the time quantification characteristics to obtain key influencing factors of carbon emissions;

[0011] According to the key influencing factors of carbon emissions and the time quantification characteristics, quantitative information on carbon emissions of power batteries is obtained.

[0012] In a further embodiment, the step of extracting battery recycling processing data of the power battery at each stage of recycling to obtain corresponding time quantification features includes:

[0013] Wavelet transform is used to extract battery recycling data at various stages of power battery recycling and utilization, and the corresponding battery recycling data features are obtained;

[0014] The battery recycling processing data features are processed based on a dynamic time warping algorithm to obtain corresponding time quantification features; wherein the time quantification features include equipment time quantification features, energy consumption time quantification features, and surplus energy and waste heat recovery time quantification features.

[0015] In a further embodiment, the step of performing an influencing factor analysis on the time quantification feature to obtain key influencing factors of carbon emissions includes:

[0016] Extracting features from the time quantification features to obtain impact factor features;

[0017] Using a pre-established integrated model to evaluate the importance of the influencing factor features to obtain a feature importance score; the integrated model is a model that integrates a convolutional neural network model and a decision tree model;

[0018] The influencing factor features are sorted according to the feature importance scores, and the key influencing factors of carbon emissions are obtained based on a preset number of key influencing factors and the sorted influencing factor features.

[0019] In a further embodiment, the step of evaluating the importance of the influencing factor features using a pre-established integrated model to obtain a feature importance score comprises:

[0020] The decision tree model is used to analyze the importance of the influencing factor features and obtain the importance weights of the influencing factor features;

[0021] The weights in the convolutional neural network model are modified based on the feature importance weights of the influencing factors to obtain a feature importance score.

[0022] In a further embodiment, the step of obtaining the quantitative information of carbon emissions of the power battery according to the key influencing factors of carbon emissions and the time quantitative characteristics includes:

[0023] Calculate the carbon emission contribution of the power battery at each stage of recycling based on the time quantification characteristics and the key influencing factors of carbon emissions;

[0024] Based on the stage weights of the power battery in each stage of recycling, the carbon emission contributions of the power battery in each stage of recycling are added together to obtain the quantitative information of the power battery's carbon emissions.

[0025] In a further embodiment, the step of calculating the carbon emission contribution of the power battery at each stage of recycling based on the time quantification characteristics and the key influencing factors of carbon emissions includes:

[0026] Analyze the equipment time quantification characteristics to obtain the equipment operation time distribution of the power battery in each stage of regeneration;

[0027] The estimated value of energy consumption is calculated based on the distribution of equipment operating time and equipment operating power;

[0028] According to the feature importance score, the energy consumption estimate and the previously obtained energy consumption measured value are processed using the weighted average method to obtain the energy consumption;

[0029] Based on the key influencing factors of energy consumption and carbon emissions, the carbon emission contribution of power batteries at each stage of recycling is obtained.

[0030] In a further embodiment, the method further comprises optimizing carbon emission quantification information through reinforcement learning, specifically:

[0031] Establishing a carbon emissions reinforcement learning model to determine a state space and actions; wherein the state space is determined based on equipment operation data and energy consumption data;

[0032] determining a reward function based on the action;

[0033] The carbon emission reinforcement learning model is used to adjust and optimize the carbon emission quantification information of the power battery.

[0034] In a second aspect, the present invention provides a carbon emission quantification analysis system based on power battery recycling, the system comprising:

[0035] A data acquisition module is used to collect battery recycling data of power batteries at various stages of recycling at specific sampling time intervals within a sampling period based on the physical and chemical reactions of the entire recycling process of power batteries. The battery recycling data includes equipment data, energy consumption data, and waste energy and waste heat recovery analysis data.

[0036] The data quantification module is used to extract the battery recycling processing data of the power battery at each stage of recycling and obtain the corresponding time quantification features;

[0037] A factor analysis module is used to perform an influencing factor analysis on the time quantification characteristics to obtain key influencing factors of carbon emissions;

[0038] The carbon emission calculation module is used to obtain the carbon emission quantitative information of the power battery according to the key influencing factors of carbon emission and the time quantitative characteristics.

[0039] In a third aspect, the present invention also provides a computer device comprising a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the computer device performs the steps of implementing the above method.

[0040] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0041] The present invention provides a method and system for quantifying and analyzing carbon emissions based on power battery recycling. The method samples battery recycling and processing data from power batteries at various recycling stages at specific sampling intervals within a sampling period; extracts the battery recycling and processing data from each recycling stage to obtain corresponding time quantification features; performs influencing factor analysis on the time quantification features to obtain key influencing factors for carbon emissions; and obtains quantified carbon emission information from the power batteries based on the key influencing factors for carbon emissions and the time quantification features. Compared with existing technologies, this method obtains quantified carbon emission information from power batteries based on battery recycling and processing data from each recycling stage. The resulting quantified carbon emission information from power batteries can be tailored to each stage of power battery recycling, thereby obtaining more accurate quantified carbon emission information from power batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG1 is a flow chart of a carbon emission quantification analysis method based on power battery recycling according to an embodiment of the present invention;

[0043] FIG2 is a block diagram of a carbon emission quantification analysis system based on power battery recycling provided by an embodiment of the present invention;

[0044] FIG3 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and are not to be construed as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of protection of the present invention. Many changes may be made to the present invention without departing from the spirit and scope of the present invention.

[0046] Referring to FIG1 , an embodiment of the present invention provides a method for quantitatively analyzing carbon emissions based on power battery recycling. As shown in FIG1 , the method includes the following steps:

[0047] S1. Based on the physical and chemical reactions of the entire power battery recycling process, battery recycling and processing data of the power battery at each stage of recycling and utilization is sampled at specific sampling time intervals within a sampling period, wherein the battery recycling and processing data includes equipment operation data, energy consumption data, and waste energy and waste heat recovery and utilization analysis data.

[0048] This embodiment collects battery recycling data in the power battery recycling process through sensors and detection instruments. During the power battery recycling process, the use of recycling equipment and the waste energy and waste heat generated in the recycling process will also affect the energy utilization efficiency. For example, the use of equipment in different time periods may have different effects on carbon emissions. That is, if the recycling equipment uses more electricity during peak hours, the carbon emissions may increase. Therefore, when calculating carbon emissions, it is necessary to pay attention to the distribution pattern of equipment usage time to obtain more accurate carbon emissions results. Therefore, this embodiment not only analyzes the use of the equipment, but also analyzes the waste energy and waste heat recovery. The recycling and utilization situation is analyzed to improve the energy utilization efficiency, thereby conducting a comprehensive quantitative analysis of the carbon emissions of power batteries in the recycling process, and providing a reference basis for achieving carbon emission reduction targets. In this embodiment, the equipment data may include data such as equipment type, equipment model, equipment specifications, and equipment operation data, wherein the equipment operation data includes the operating frequency of the equipment operation time period and the switching time interval between different equipment; the energy consumption data includes data such as the type of energy used in battery recycling and the actual measured value of energy consumption; the waste energy and waste heat recovery analysis data includes information such as the waste energy and waste heat recovery situation, the waste energy and waste heat recovery effect, the waste energy and waste heat recovery consumption, and the recovered energy type.

[0049] S2. Extract battery recycling and processing data at each stage of power battery recycling to obtain corresponding time quantification features.

[0050] In one embodiment, the step of extracting battery recycling processing data at each stage of power battery recycling to obtain corresponding time quantification features includes:

[0051] Wavelet transform is used to extract battery recycling data at various stages of power battery recycling and utilization, and the corresponding battery recycling data features are obtained;

[0052] The battery recycling processing data features are processed based on a dynamic time warping algorithm to obtain corresponding time quantification features. In this embodiment, the time quantification features include equipment time quantification features, energy consumption time quantification features, and surplus energy and waste heat recovery time quantification features. The equipment time quantification features include equipment operating time periods, operating frequencies, and switching time intervals between different devices. The energy consumption time quantification features include energy consumption time and energy consumption intensity. The surplus energy and waste heat time quantification features include surplus energy and waste heat recovery efficiency.

[0053] In this embodiment, the steps of processing the battery recycling data features based on the dynamic time warping algorithm to obtain corresponding time quantization features specifically include:

[0054] The battery recycling data features are divided into two eigenvectors, and a distance matrix between the two eigenvectors is calculated, where the distance matrix can be a Euclidean distance matrix, a Manhattan distance matrix, etc.

[0055] The dynamic programming algorithm is used to obtain all path lengths of the distance matrix, and the DTW distance is calculated based on the minimum distance of all path lengths.

[0056] Align the DTW distances to obtain aligned battery recycling data features;

[0057] The aligned battery recycling processing data features are interpolated or downsampled to extract time quantization features.

[0058] Since the wavelet transform is better at extracting useful time quantization features and effectively processing various distribution features that may exist in battery recycling and processing data, and the dynamic time warping (DTW) dimensionality reduction method is a method of mapping time series to a low-dimensional space, this method can retain important information in the time series data during the dimensionality reduction process. Therefore, this embodiment combines the wavelet transform and the dynamic time warping algorithm to extract time quantization features, so as to extract the internal changes of the battery recycling and processing data through the wavelet transform. Then, considering that the amount of battery recycling and processing data in the battery recycling link is large, this embodiment combines the dynamic time warping algorithm to extract the important features contained therein, so that interpretable, reasonable, and accurate time quantization features can be extracted from the battery recycling and processing data, so that the time quantization features can reasonably and accurately reflect the use and consumption of energy in the recycling process, thereby achieving a comprehensive analysis of the carbon emissions status caused by energy use and consumption in the recycling process.

[0059] S3. Perform an influencing factor analysis on the time quantification characteristics to obtain key influencing factors of carbon emissions.

[0060] In one embodiment, the step of performing an impact factor analysis on the time quantification feature to obtain key impact factors of carbon emissions includes:

[0061] Extracting features from the time quantification features to obtain impact factor features;

[0062] Using a pre-established integrated model to evaluate the importance of the influencing factor features to obtain a feature importance score; the integrated model is a model that integrates a convolutional neural network model and a decision tree model;

[0063] The influencing factor features are sorted according to the feature importance scores, and the key influencing factors of carbon emissions are obtained based on a preset number of key influencing factors and the sorted influencing factor features.

[0064] In this embodiment, the step of using a pre-established integrated model to evaluate the importance of the influencing factor features to obtain a feature importance score includes:

[0065] The decision tree model is used to analyze the importance of the influencing factor features and obtain the importance weights of the influencing factor features;

[0066] The weights in the convolutional neural network model are modified based on the feature importance weights of the influencing factors to obtain a feature importance score.

[0067] Since only analyzing the importance of the influencing factor characteristics based on the decision tree model will result in poor accuracy of the feature importance, this embodiment uses the decision tree model to analyze the degree of influence of the influencing factor characteristics on the time quantification characteristics, and can analyze the influencing factor characteristics that have a more significant impact on the time quantification characteristics. The influencing factor feature importance weights determined by the decision tree model are then used to adjust the influence weights of each variable in the convolutional neural network model, thereby obtaining a more accurate feature importance score, so that when the carbon emissions are subsequently calculated through key influencing factors, comprehensive and reasonable quantification of power battery carbon emissions can be achieved.

[0068] S4. Obtaining quantitative information on carbon emissions of power batteries based on the key influencing factors of carbon emissions and the time quantification characteristics.

[0069] In one embodiment, the step of obtaining the power battery carbon emission quantitative information based on the carbon emission key influencing factors and the time quantification characteristics includes:

[0070] Calculate the carbon emission contribution of the power battery at each stage of recycling based on the time quantification characteristics and the key influencing factors of carbon emissions;

[0071] Based on the stage weights of the power battery in each stage of recycling, the carbon emission contributions of the power battery in each stage of recycling are added together to obtain the quantitative information of the power battery's carbon emissions.

[0072] In this embodiment, the step of calculating the carbon emission contribution of the power battery at each stage of recycling based on the time quantification feature and the feature importance score of the key carbon emission influencing factor includes:

[0073] Analyze the equipment time quantification characteristics to obtain the equipment operation time distribution of the power battery in each stage of regeneration;

[0074] The energy consumption estimate is calculated based on the equipment operating time distribution and equipment operating power. The energy consumption estimate is calculated as follows: α=P*T

[0075] Where, α represents the estimated value of energy consumption; P represents the equipment operating power; T represents the equipment operating time;

[0076] According to the feature importance score, the energy consumption estimate and the energy consumption measured value are processed using the weighted average method to obtain the energy consumption;

[0077] Based on energy consumption and key influencing factors of carbon emissions, the carbon emission contribution of power batteries at each stage of recycling is obtained. The calculation formula for carbon emission contribution is: β=C*δ*(1-d)

[0078] In the formula, β represents the carbon emission contribution of the power battery at each stage of recycling; C represents energy consumption; δ represents the key influencing factor of carbon emissions; and d represents the efficiency of waste energy and waste heat recovery.

[0079] Among them, for the step of processing the estimated energy consumption value and the actual energy consumption value using the weighted average method according to the feature importance score to obtain the energy consumption, since the estimated energy consumption value is calculated through the operation data of the equipment, in order to be able to calculate the carbon emissions more reasonably and accurately, this embodiment adopts weighted summation for the actual energy consumption value detected by the equipment and the energy consumption detection instrument, specifically: the feature importance score corresponding to the equipment time quantification feature is used as the weight of the estimated energy consumption value, and the feature importance score corresponding to the energy consumption time quantification feature is used as the weight of the actual energy consumption value, and the weighted average method is used to process the estimated energy consumption value and the actual energy consumption value to obtain the energy consumption.

[0080] In this embodiment, the step of adding the carbon emission contributions of the power battery at each stage of recycling based on the stage weights of the power battery at each stage of recycling to obtain the quantitative information of the power battery's carbon emissions includes:

[0081] Determine the stage weights for each stage of the power battery recycling process;

[0082] Multiply the carbon emission contribution of the power battery at each stage of recycling by the corresponding stage weight to obtain the carbon emission contribution value of the corresponding stage to the entire power battery recycling process;

[0083] The carbon emission contribution values ​​of power batteries at each stage of recycling are added together to obtain quantitative information on power battery carbon emissions.

[0084] Among them, the various stages in the recycling process of power batteries may include disassembly stage, detection stage, classification stage, recycling stage and other stages. Since the degree of influence of each stage is different, this embodiment sets stage weights to reflect the degree of influence of each stage on the contribution of carbon emissions of the entire power battery in the recycling process. This embodiment preferentially determines the stage weights based on the ratio of carbon emissions in each stage such as disassembly, detection, classification, recycling and other stages to the total emissions; it should be noted that those skilled in the art can adjust the stage division and stage weight setting of the power battery in the recycling process according to specific implementation conditions, and are not limited to the embodiments of the present invention.

[0085] In one embodiment, the method further includes optimizing carbon emissions through reinforcement learning, specifically:

[0086] Establishing a carbon emissions reinforcement learning model to determine the state space and actions, where the state space is determined based on equipment operation data and energy consumption data;

[0087] determining a reward function based on the action;

[0088] The carbon emission reinforcement learning model is used to adjust and optimize the carbon emission quantification information of the power battery.

[0089] Specifically, this embodiment requires designing a simulation environment for simulating the operation status and energy consumption of the equipment, and then defining the state space and action. In this embodiment, the state space is the operation data and energy consumption data of the equipment in the simulation environment. The action can be to reduce the energy consumption of the equipment, adjust the equipment operation time, etc. At the same time, the reward function is determined based on the action. The reward function can be to reduce carbon emissions or reduce energy consumption, etc. Finally, the carbon emissions reinforcement learning model is used to adjust and optimize the total carbon emissions.

[0090] An embodiment of the present invention provides a method for quantifying and analyzing carbon emissions based on power battery recycling. The method, based on the physical and chemical reactions of the entire power battery recycling process, samples battery recycling and processing data from each stage of the recycling process at specific sampling time intervals within a sampling period. The method extracts the battery recycling and processing data from each stage of the recycling process to obtain corresponding time quantification features. The method then performs an influencing factor analysis on the time quantification features to obtain key influencing factors for carbon emissions. Based on the key influencing factors for carbon emissions and the time quantification features, quantitative information on the carbon emissions of the power battery is obtained. Compared to the prior art, the present application performs influencing factor analysis and quantitative processing on the battery recycling and processing data from each stage of the recycling process, obtains quantitative information on the carbon emissions of the power battery from a comprehensive calculation from multiple aspects, and achieves a comprehensive and accurate analysis of the carbon emissions status of the recycling process. Furthermore, based on reinforcement learning, the quantitative information on the carbon emissions of the power battery is optimized and adjusted, thereby improving the accuracy of carbon emission estimates.

[0091] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of this application.

[0092] In one embodiment, as shown in FIG2 , an embodiment of the present invention provides a carbon emission quantification analysis system based on power battery recycling, the system comprising:

[0093] The data acquisition module 101 is configured to collect battery recycling data of the power battery at various stages of recycling at specific sampling intervals within a sampling period based on the physical and chemical reactions of the entire recycling process of the power battery. The battery recycling data includes equipment data, energy consumption data, and waste energy and waste heat recovery analysis data.

[0094] The data quantification module 102 is used to extract the battery recycling processing data of the power battery at each stage of recycling and obtain the corresponding time quantification features;

[0095] A factor analysis module 103 is used to perform an influencing factor analysis on the time quantification feature to obtain key influencing factors of carbon emissions;

[0096] The carbon emission calculation module 104 is configured to obtain the carbon emission quantitative information of the power battery according to the key influencing factors of carbon emission and the time quantitative characteristics.

[0097] For the specific definition of a carbon emission quantitative analysis system based on power battery recycling, please refer to the above-mentioned definition of a carbon emission quantitative analysis method based on power battery recycling, which will not be repeated here. A person of ordinary skill in the art will appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0098] An embodiment of the present invention provides a carbon emission quantification and analysis system based on power battery recycling. The system uses a data acquisition module to acquire battery recycling and processing data for power batteries at various recycling stages. A data quantification module extracts the battery recycling and processing data from each recycling stage to obtain corresponding time quantification features. A factor analysis module performs influencing factor analysis on the time quantification features to obtain key carbon emission influencing factors. A carbon emission calculation module calculates power battery carbon emission quantification information. Compared with the prior art, this application calculates power battery carbon emission quantification information based on battery recycling and processing data from each recycling stage, improving the accuracy of carbon emission quantification analysis and making it more suitable for practical applications.

[0099] Figure 3 is a computer device provided by an embodiment of the present invention, including a memory, a processor and a transceiver, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and can transmit the stored data to the processor, and the processor can execute the program instructions stored in the memory to perform the steps of the above method.

[0100] The memory may include volatile memory or non-volatile memory, or may include both volatile and non-volatile memory; the processor may be a central processing unit, a microprocessor, an application-specific integrated circuit, a programmable logic device, or a combination thereof. By way of example and not limitation, the programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0101] Additionally, the memory may be a physically separate unit or integrated with the processor.

[0102] Those skilled in the art will understand that the structure shown in FIG3 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0103] In one embodiment, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above method are implemented.

[0104] An embodiment of the present invention provides a carbon emission quantification analysis method and system based on power battery recycling. The carbon emission quantification analysis method based on power battery recycling not only realizes a comprehensive and accurate analysis of the carbon emission status of the recycling process, but also optimizes and adjusts the power battery carbon emission quantification information based on reinforcement learning, thereby improving the accuracy and rationality of the carbon emission quantification of power battery recycling.

[0105] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., an SSD).

[0106] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0107] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A carbon emission quantitative analysis method based on power battery recycling, characterized in that: The following steps are involved: Based on the physical and chemical reactions of the entire process of power battery recycling, the battery recycling data of the power battery at each stage of recycling is sampled at specific sampling time intervals within the sampling period, wherein the battery recycling data includes equipment operation data, energy consumption data and waste energy and waste heat recovery analysis data; Extract the battery recycling data at each stage of power battery recycling and obtain the corresponding time quantification features; Performing an influencing factor analysis on the time quantification characteristics to obtain key influencing factors of carbon emissions; According to the key influencing factors of carbon emissions and the time quantification characteristics, the quantified information of carbon emissions of the power battery is obtained.

2. A carbon emission quantitative analysis method based on power battery recycling as claimed in claim 1, characterized in that: The step of extracting battery recycling processing data at each stage of power battery recycling to obtain corresponding time quantification features includes: Wavelet transform is used to extract the battery recycling data of power batteries at various stages of recycling and utilization, and the corresponding battery recycling data features are obtained; The battery recycling processing data features are processed based on a dynamic time warping algorithm to obtain corresponding time quantification features; wherein the time quantification features include equipment time quantification features, energy consumption time quantification features, and surplus energy and waste heat recovery time quantification features.

3. A carbon emission quantitative analysis method based on power battery recycling as claimed in claim 1, characterized in that: The step of performing an influencing factor analysis on the time quantification feature to obtain the key influencing factors of carbon emissions comprises: Extracting the time quantification feature to obtain an influencing factor feature; Using a pre-established integrated model to evaluate the importance of the influencing factor features to obtain a feature importance score; the integrated model is a model that integrates a convolutional neural network model and a decision tree model; The influencing factor characteristics are sorted according to the characteristic importance scores, and the key influencing factors of carbon emissions are obtained based on the preset number of key influencing factors and the sorted influencing factor characteristics.

4. A carbon emission quantitative analysis method based on power battery recycling as claimed in claim 3, characterized in that: The step of using the pre-established integrated model to evaluate the importance of the influencing factor feature to obtain the feature importance score comprises: The decision tree model is used to analyze the importance of the influencing factor features and obtain the importance weights of the influencing factor features; The weights in the convolutional neural network model are modified based on the feature importance weights of the influencing factors to obtain a feature importance score.

5. A carbon emission quantitative analysis method based on power battery recycling as claimed in claim 4, characterized in that: The step of obtaining the carbon emission quantitative information of the power battery according to the key influencing factors of carbon emission and the time quantitative characteristics comprises: Calculate the carbon emission contribution of the power battery at each stage of recycling according to the time quantification characteristics and the key influencing factors of carbon emission; Based on the stage weights of the power battery in each stage of recycling, the carbon emission contributions of the power battery in each stage of recycling are added together to obtain the quantitative information of the power battery's carbon emissions.

6. A carbon emission quantitative analysis method based on power battery recycling as claimed in claim 5, characterized in that: The step of calculating the carbon emission contribution of the power battery at each stage of recycling according to the time quantification feature and the key influencing factor of carbon emission includes: Analyze the equipment time quantification characteristics to obtain the equipment operation time distribution of the power battery in each stage of recycling; The estimated value of energy consumption is calculated based on the distribution of equipment operation time and equipment operation power; According to the feature importance score, the estimated value of energy loss and the measured value of energy consumption obtained in advance are processed by weighted average method to obtain energy consumption; According to the key influencing factors of energy consumption and carbon emissions, the carbon emission contribution of power batteries at each stage of recycling is obtained.

7. A carbon emission quantitative analysis method based on power battery recycling as claimed in claim 6, characterized in that: The method also includes optimizing carbon emission quantification information through reinforcement learning, specifically: Establishing a carbon emission reinforcement learning model to determine the state space and actions; wherein the state space is determined based on the equipment operation data and energy consumption data; determining a reward function based on the actions; The carbon emission reinforcement learning model is used to adjust and optimize the carbon emission quantification information of the power battery.

8. A carbon emission quantitative analysis system based on power battery recycling, characterized in that: The system comprises: A data acquisition module, which is used to sample the battery recycling data of the power battery at each stage of recycling at a specific sampling time interval within a sampling period based on the physical and chemical reactions of the entire process of power battery recycling, wherein the battery recycling data includes equipment data, energy consumption data and waste energy and waste heat recycling analysis data; The data quantification module is used to extract the battery recycling data at each stage of power battery recycling and obtain the corresponding time quantification features; A factor analysis module is used to perform an influencing factor analysis on the time quantification characteristics to obtain key influencing factors of carbon emissions; The carbon emission calculation module is used to obtain the carbon emission quantitative information of the power battery according to the key influencing factors of carbon emission and the time quantitative characteristics.

9. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the computer device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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