Carbon emission monitoring method and system for battery recycling, device, and medium

Abstract

The present invention provides a carbon emission monitoring method and system for battery recycling, a device, and a medium. The method comprises: inputting acquired scrap information of a lithium battery to be recycled into a pre-constructed scrap classification model for classification prediction to obtain a scrap type; on the basis of the scrap type, determining an optimal recovery method and performing recovered carbon emission prediction on said lithium battery to obtain a carbon emission monitoring target value; performing recycling treatment on said lithium battery according to the optimal recovery method, and on the basis of the acquired actual carbon emission and actual economic revenue from recycling which correspond to each process step, obtaining the actual total carbon emission in recycling and the actual total economic revenue from recycling; and on the basis of the actual total carbon emission in recycling and the actual total economic revenue from recycling in combination with the carbon emission monitoring target value, evaluating the carbon emission in the recovery process of said lithium battery to obtain a carbon emission evaluation result. According to the present invention, a universal carbon emission monitoring method can be provided for lithium battery recovery, and comprehensive and accurate integrated monitoring and scientific and reasonable evaluation of carbon emission in the lithium battery recovery process can be realized.

Description

A carbon emission monitoring method, system, device and medium for battery recycling Technical Field

[0001] The present invention relates to the technical field of battery recycling carbon emission monitoring technology, and in particular to a battery recycling carbon emission monitoring method, system, computer equipment and storage medium. Background Art

[0002] Lithium battery recycling not only protects the environment but also creates significant economic value from resource recovery. Existing methods for recycling used lithium batteries primarily rely on thermal decomposition and dry crushing and sorting to recover heavy metals and other elements. However, carbon emissions inevitably result from process or material loss during recycling, undoubtedly causing a degree of environmental pollution. Therefore, how to effectively monitor carbon emissions during the recycling process and control them within a reasonable range during lithium battery recycling has become a key concern for relevant companies.

[0003] Existing carbon emission monitoring of lithium battery recycling and utilization mostly focuses on carbon emission accounting of recycling methods such as pyrolysis and dry crushing and sorting, and does not pay attention to carbon emission monitoring research based on battery scrapping types and the rational formulation of carbon emission monitoring targets. In addition, the carbon emission rationality monitoring and evaluation process does not consider the balance between economic income and carbon emissions, and cannot truly achieve scientific and reasonable monitoring of carbon emissions in the process of lithium battery recycling and utilization, as well as truly effective evaluation closed-loop management integration.

[0004] Summary of the Invention

[0005] The purpose of the present invention is to provide a carbon emission monitoring method for battery recycling, which performs scrap classification and optimal recycling method determination based on lithium battery scrap information, and obtains scientific and reasonable carbon emission monitoring targets based on scrap classification and optimal recycling method, and then combines effective evaluation indicators to reliably evaluate the carbon emissions of the lithium battery recycling process, thereby solving the application defects of the existing carbon emission monitoring method for lithium battery recycling, providing a universal carbon emission monitoring method for lithium battery recycling, and realizing comprehensive and accurate integrated monitoring and scientific and reasonable evaluation of carbon emissions in the lithium battery recycling process, thereby providing reliable guarantee for the optimization of the lithium battery recycling process.

[0006] In order to achieve the above objectives, it is necessary to provide a carbon emission monitoring method and system for battery recycling in response to the above technical problems.

[0007] In a first aspect, an embodiment of the present invention provides a method for monitoring carbon emissions from battery recycling, the method comprising the following steps:

[0008] Obtain scrap information of the lithium batteries to be recycled, and input the scrap information into a pre-built scrap classification model for classification prediction to obtain the corresponding scrap type;

[0009] Determine the corresponding optimal recycling method according to the scrap type, and predict the recycling carbon emissions of the lithium battery to be recycled based on the scrap type and the optimal recycling method to obtain a carbon emission monitoring target value;

[0010] Recycling the lithium battery to be recycled according to the optimal recycling method, and obtaining the actual total carbon emissions and the actual total economic income of recycling based on the actual carbon emissions and actual economic income of recycling corresponding to each process step;

[0011] According to the actual total carbon emissions from recycling and the actual total economic income from recycling, a corresponding recycling value index is obtained. Based on the comparative analysis results of the actual total carbon emissions from recycling and the carbon emission monitoring target value, combined with the recycling value index, a comprehensive assessment of the carbon emissions of the recycling process of the lithium battery to be recycled is performed to obtain a carbon emission assessment result.

[0012] Furthermore, the scrapping information includes an appearance image, a three-dimensional point cloud feature map and a battery capacity retention rate.

[0013] Furthermore, the step of obtaining scrap information of lithium batteries to be recycled includes:

[0014] Collecting image data and point cloud data of the lithium battery to be recycled by a laser scanning device, and preprocessing the image data and point cloud data respectively to obtain corresponding appearance images and three-dimensional point cloud feature maps;

[0015] Obtain the number of charge and discharge cycles of the lithium battery to be recycled, and obtain the corresponding battery capacity retention rate based on the number of charge and discharge cycles and a preset battery capacity retention attenuation model; the battery capacity retention rate is expressed as: Rc = Rc0e -dn

[0016] Among them, Rc0, -d and n represent the initial capacity retention rate, capacity attenuation coefficient and number of charging cycles respectively; Rc represents the battery capacity retention rate after n cycles.

[0017] Furthermore, the scrap classification model includes a first classification module and a second classification module connected in sequence; the first classification module includes an ACmix network model;

[0018] The step of inputting the scrap information into a pre-built scrap classification model for classification prediction to obtain the corresponding scrap type includes:

[0019] Inputting the appearance image and the three-dimensional point cloud feature map into the first classification module for classification and recognition to obtain a corresponding initial classification result;

[0020] Determine whether the initial classification result is normal. If not, use the initial classification result as the corresponding scrap type; if so, input the corresponding battery capacity retention rate into the second classification module for abnormality inspection, and obtain the corresponding scrap type based on the corresponding abnormality inspection result.

[0021] Furthermore, the types of scrapping include appearance damage and deformation, liquid leakage, abnormal internal bulging and abnormal capacity retention rate;

[0022] The step of determining the corresponding optimal recycling method according to the scrap type includes:

[0023] According to the scrap type, a pre-built type recycling mapping relationship library is searched to obtain the corresponding optimal recycling method; the type recycling mapping relationship library is a database that stores the mapping relationship between scrap types and optimal recycling methods.

[0024] Furthermore, the carbon emission monitoring target value includes the total carbon emission target value of the recycling process and the corresponding carbon emission target value of each process step;

[0025] The step of predicting the carbon emissions of the recycled lithium battery according to the scrap type and the optimal recycling method to obtain a carbon emission monitoring target value includes:

[0026] Obtaining the battery model, capacity, and weight of the lithium battery to be recycled;

[0027] Inputting the battery model, capacity, weight, scrap type and optimal recycling method into a pre-built carbon emission regression prediction model for combined prediction to obtain the total target value of carbon emissions from the recycling process;

[0028] The carbon emission target value of each process step is obtained according to the total carbon emission target value of the recycling process and the historical carbon emission proportion of each process step in the optimal recycling method.

[0029] Furthermore, the step of comprehensively evaluating the carbon emissions of the recycling process of the lithium battery to be recycled based on the comparative analysis results of the actual total recycled carbon emissions and the carbon emission monitoring target value in combination with the recycling value index to obtain the carbon emission evaluation result includes:

[0030] Determine whether the actual total carbon emissions from recycling are less than the total target carbon emissions from the recycling process in the carbon emissions monitoring target value; if not, set the carbon emissions assessment result as exceeding the carbon emissions standard; otherwise, determine whether the recycling value index is greater than a preset value threshold;

[0031] If the recycling value index is greater than the preset value threshold, the carbon emission assessment result is set as carbon emission qualified; otherwise, the carbon emission assessment result is set as carbon emission exceeding the standard;

[0032] When the carbon emission assessment result is that the carbon emission exceeds the standard, the process steps to be optimized whose actual carbon emissions exceed the carbon emission target value of the corresponding process steps in the carbon emission monitoring target value are obtained, and the process steps to be optimized are graded to indicate that the carbon emission exceeds the standard.

[0033] Furthermore, the recycling value index is expressed as:

[0034] Where I represents the recycling value index; E inc and C rel They represent the actual total economic income from recycling and the actual total carbon emissions from recycling, respectively.

[0035] In a second aspect, an embodiment of the present invention provides a carbon emission monitoring system for battery recycling, the system comprising:

[0036] A type identification module is used to obtain scrap information of lithium batteries to be recycled and input the scrap information into a pre-built scrap classification model for classification prediction to obtain the corresponding scrap type;

[0037] A target acquisition module is used to determine the corresponding optimal recycling method according to the scrap type, and to predict the recycled carbon emissions of the lithium battery to be recycled based on the scrap type and the optimal recycling method to obtain a carbon emission monitoring target value;

[0038] A carbon emission monitoring module is used to recycle the lithium batteries to be recycled according to the optimal recycling method, and obtain the actual total carbon emissions and actual total economic income of recycling based on the actual carbon emissions and actual recycling economic income corresponding to each process step;

[0039] The carbon emission assessment module is used to obtain a corresponding recycling value index based on the actual total carbon emissions of recycling and the actual total economic income of recycling, and based on the comparative analysis results of the actual total carbon emissions of recycling and the carbon emission monitoring target value, combined with the recycling value index, comprehensively evaluate the carbon emissions of the recycling process of the lithium battery to be recycled to obtain a carbon emission assessment result.

[0040] In a third aspect, an embodiment of the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0041] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0042] The above-mentioned present application provides a carbon emission monitoring method, system, computer equipment and storage medium for battery recycling. Through the method, the scrap information of the obtained lithium battery to be recycled is input into a pre-built scrap classification model for classification prediction to obtain the scrap type, and the optimal recycling method is determined according to the scrap type. After the recycling carbon emissions of the lithium battery to be recycled are predicted to obtain the carbon emission monitoring target value, the actual carbon emissions and actual recycling economic income corresponding to each process step of the recycling treatment of the lithium battery to be recycled according to the optimal recycling method are obtained, and the actual total recycling carbon emissions and actual total recycling economic income are obtained. Combined with the carbon emission monitoring target value, the carbon emissions of the recycling process of the lithium battery to be recycled are evaluated to obtain the carbon emission evaluation result. A technical solution. Compared with the existing technology, the carbon emission monitoring method for battery recycling can not only provide a universal carbon emission monitoring method for lithium battery recycling, but also realize comprehensive and accurate integrated monitoring and scientific and reasonable evaluation of carbon emissions in the lithium battery recycling process, thereby providing reliable guarantee for the optimization of the lithium battery recycling process, and has high practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] FIG1 is a schematic diagram of an application scenario of a carbon emission monitoring method for battery recycling according to an embodiment of the present invention;

[0044] FIG2 is a schematic flow chart of a method for monitoring carbon emissions from battery recycling according to an embodiment of the present invention;

[0045] FIG3 is a schematic diagram of the structure of a scrap classification model according to an embodiment of the present invention;

[0046] FIG4 is a schematic diagram of the structure of an ACmix network model in a scrap classification model according to an embodiment of the present invention;

[0047] FIG5 is a schematic structural diagram of a carbon emission monitoring system for battery recycling according to an embodiment of the present invention;

[0048] FIG6 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and beneficial effects of this application more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0050] The carbon emission monitoring method for battery recycling provided by the present invention can be applied to a terminal or server as shown in Figure 1. The terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server can be implemented as an independent server or a server cluster composed of multiple servers. The server can use the carbon emission monitoring method for battery recycling of the present invention to monitor and evaluate the carbon emissions of the retired lithium battery recycling process according to actual application requirements, and use the obtained carbon emission evaluation results for subsequent research on the server, or send them to the terminal for further analysis and research by the terminal user. It can not only provide a universal carbon emission monitoring method for lithium battery recycling, but also realize comprehensive and accurate integrated monitoring and scientific and reasonable evaluation of carbon emissions in the lithium battery recycling process, thereby providing reliable guarantees for the optimization of the lithium battery recycling process. The following embodiments will explain the carbon emission monitoring method for battery recycling of the present invention in detail.

[0051] In one embodiment, as shown in FIG2 , a method for monitoring carbon emissions from battery recycling is provided, comprising the following steps:

[0052] S11. Obtain scrap information of lithium batteries to be recycled, and input the scrap information into a pre-built scrap classification model for classification prediction to obtain a corresponding scrap type; wherein the scrap information can be understood as information that can directly or indirectly reflect the scrap status of the lithium batteries to be recycled. Based on the three possibilities that existing scrapped lithium batteries usually have, namely, abnormal appearance (external damage or liquid leakage), abnormal internal structure (bulging inside the battery), or abnormal capacity retention rate, this embodiment preferably collects information related to these three aspects of the lithium batteries to be recycled to perform simple and effective scrap type identification; specifically, the scrap information includes an appearance image for detecting external damage or liquid leakage, a three-dimensional point cloud feature map for detecting internal bulging of the battery, and a battery capacity retention rate for detecting abnormal capacity retention rate; correspondingly, the step of obtaining the scrap information of the lithium batteries to be recycled includes:

[0053] The image data and point cloud data of the lithium battery to be recycled are collected by a laser scanning device, and the image data and point cloud data are preprocessed respectively to obtain a corresponding appearance image and a three-dimensional point cloud feature map; wherein the laser scanning device can be understood as a laser scanning device capable of obtaining the image data and point cloud data of the lithium battery to be recycled, and the specific type is not limited here; the corresponding image data preprocessing can be understood as an image preprocessing method selected according to the actual application requirements of the appearance image, including grayscale, geometric transformation and image enhancement (denoising) and other processing operations to eliminate irrelevant information and restore useful real information; similarly, the point cloud data preprocessing can be understood as a point cloud data preprocessing method selected according to the actual application requirements of the three-dimensional point cloud feature map, including denoising, simplification, registration, hole filling, normalization and depth map mapping and other processing operations to effectively remove noise and external points in the point cloud;

[0054] The number of charge and discharge cycles of the lithium battery to be recycled is obtained, and the corresponding battery capacity retention rate is obtained according to the number of charge and discharge cycles and the preset battery capacity retention attenuation model; wherein, the number of charge and discharge cycles can be understood as the number of charge and discharge cycles that the lithium battery to be recycled has experienced during normal operation; the battery capacity retention rate can be understood as the ratio of the actual capacity of the battery to the rated capacity. During use, the remaining capacity of the battery decreases with the increase of the number of charge and discharge cycles, and the decay law of the capacity retention rate with the number of charge and discharge cycles conforms to the power function relationship. After collecting a large number of data on the number of cycle charge and capacity retention rates of retired lithium batteries, the corresponding battery capacity retention attenuation model is fitted, which is expressed as: Rc=Rc0e -dn

[0055] Wherein, Rc0, -d and n represent the initial capacity retention rate, capacity attenuation coefficient and number of charging cycles respectively; Rc represents the battery capacity retention rate after n cycles;

[0056] The above-mentioned scrap classification model for identifying the scrap type of recycled lithium batteries can be understood as being obtained by obtaining a large number of appearance images, three-dimensional point cloud feature maps and battery capacity retention rates of retired lithium batteries, and performing corresponding type annotations to obtain a scrap information data set for training; specifically, as shown in FIG3 , the scrap classification model includes a first classification module and a second classification module connected in sequence; wherein, in principle, the first classification module can adopt a network model with image and point cloud data recognition functions, but considering the accuracy and efficiency of the initial classification, this embodiment preferably adopts the ACmix network model shown in FIG4 that integrates the self-attention mechanism and convolution operation in the deep neural network, which first uses the convolution operation to input The input features are mapped to obtain rich intermediate features, and then the intermediate features are reused and aggregated using convolution operations and self-attention mechanisms respectively. This processing method combines the advantages of convolution operations and self-attention mechanisms, avoiding complex secondary projection operations while effectively improving the efficiency and accuracy of image classification and recognition applications. The ACmix network model can be understood as an image classification and recognition model with a parameter-stable ACmix network structure obtained by annotating and training a large number of appearance images and 3D point cloud feature maps of retired lithium batteries. The second classification module can be understood as a processing module that performs abnormal inspection on the capacity retention rate of recycled lithium batteries based on the capacity retention rate threshold obtained by statistical analysis of the battery capacity retention rate of a large number of retired lithium batteries.

[0057] Specifically, the step of inputting the scrap information into a pre-built scrap classification model for classification prediction to obtain the corresponding scrap type includes:

[0058] The appearance image and the three-dimensional point cloud feature map are input into the first classification module for classification and recognition to obtain a corresponding initial classification result. The initial classification result can be understood as the classification result obtained by using the ACmix network model to identify the scrap type of the recycled lithium battery using the fused feature map based on self-attention and convolution extraction. The classification results include four categories: external damage, liquid leakage, internal bulge, and normal. The specific acquisition process is as follows:

[0059] In the first stage: the input appearance image and 3D point cloud feature map are projected through three 1×1 convolutions, and then the deformation function (reshape) operation is performed into N segments. Thus, a rich set of intermediate features including 3×N feature maps is obtained;

[0060] In the second stage: after receiving the intermediate features, a fully connected layer MLP (Multilayer Perceptron) is used to generate nonlinear feature maps. Then, by transferring and aggregating the generated features, convolution processing is performed on the input features. This allows the extraction of useful feature information from the local receptive field, just like a traditional CNN network.

[0061] For the self-attention path, the intermediate features obtained above are grouped into N groups, each containing 3 features, each from a 1×1 convolution; the corresponding three feature maps are used as query, key, and value respectively, and the traditional multi-head self-attention module is used for operation and calculation;

[0062] Finally, the feature tensors generated by the two paths are weightedly added to generate a fused feature map with a dimension of H×W×C. The fused feature map is then input into the preset classifier for classification processing to obtain the corresponding initial classification result.

[0063] Determine whether the initial classification result is normal. If not, use the initial classification result as the corresponding scrap type. Otherwise, input the corresponding battery capacity retention rate into the second classification module for abnormality inspection, and obtain the corresponding scrap type according to the corresponding abnormality inspection result. The scrap type acquisition process can be understood as follows: when the initial classification result is abnormal, that is, the aforementioned external damage and deformation, liquid leakage or internal bulging of the battery, the initial classification result is directly used as the scrap type; when the initial classification result is normal, that is, it is not identified as any type of external damage, liquid leakage or internal bulging of the battery, it is necessary to perform capacity retention rate abnormality detection on it, and obtain the final scrap type according to the abnormality detection result. It should be noted that the capacity retention rate abnormality detection can be understood as judging the battery capacity retention rate corresponding to the recycled lithium battery based on the capacity retention rate threshold obtained by the aforementioned statistical analysis. If the battery capacity retention rate has decayed to the preset capacity retention rate threshold, it is considered that the capacity retention rate of the lithium battery is abnormal.

[0064] S12. Determine the corresponding optimal recycling method based on the scrap type, and predict the recycled carbon emissions of the lithium battery to be recycled based on the scrap type and the optimal recycling method to obtain a carbon emission monitoring target value; wherein the scrap type is the scrap category of the lithium battery determined by the above method steps, including external damage and deformation, liquid leakage, internal abnormal bulging, and abnormal capacity retention rate; it should be noted that in actual application, the specific categories of scrap classification can be increased or decreased according to actual conditions and needs;

[0065] Existing lithium battery recycling methods are mainly divided into dry recycling, wet recycling and biological recycling. Among them, dry recycling mainly refers to the direct recovery of various battery materials or valuable metals without the use of media such as solutions, mainly including mechanical sorting and high-temperature decomposition. The crude product obtained by dry recycling can be further subjected to high-temperature thermal repair to obtain positive and negative electrode materials containing certain impurities, or the organic binder in the electrode material can be removed by pyrometallurgy, and the metals and their compounds therein undergo redox reactions and condense for recovery. The metals in the slag can also be recovered by screening, pyrolysis, magnetic separation or chemical methods. Wet recycling uses various acidic and alkaline solutions as transfer media to transfer metal ions from the electrode material to the leachate, and then extracts the metal ions from the solution in the form of salts, oxides, etc. through ion exchange, precipitation, adsorption and other means. It mainly includes methods such as hydrometallurgy, chemical extraction and ion exchange. Biological recovery mainly uses microbial leaching to convert the useful components of the system into soluble compounds and selectively dissolve them, thereby separating the target components from the impurity components and recovering valuable metals such as lithium, cobalt and nickel.

[0066] The above-mentioned optimal recycling method refers to the most suitable recycling method selected from the above-mentioned three recycling methods according to the scrap type; specifically, the step of determining the corresponding optimal recycling method according to the scrap type includes:

[0067] According to the scrap type, a pre-constructed type recycling mapping relationship library is searched to obtain the corresponding optimal recycling method; wherein, the type recycling mapping relationship library can be understood as a database established based on the research results analysis of a large number of scrapped lithium batteries of various scrap types collected and selected different recycling methods, for example, dry recycling is used for external damage or abnormal capacity retention rate, wet recycling is used for liquid leakage or internal bulging of the battery, etc.; it should be noted that the recycling methods corresponding to the above scrap types are only illustrative descriptions and can be selected based on existing research results or actual application needs, and are not specifically limited here.

[0068] The above-mentioned carbon emission monitoring target value can be understood as the target upper limit value of the carbon emissions during the recycling process of the recycled battery, including the total target value of carbon emissions during the recycling process and the corresponding carbon emission target values ​​of each process step; specifically, the step of predicting the carbon emissions of the recycling of the recycled lithium battery according to the scrap type and the optimal recycling method to obtain the carbon emission monitoring target value includes:

[0069] Obtaining the battery model, capacity, and weight of the lithium battery to be recycled;

[0070] The battery model, capacity, weight, scrap type, and optimal recycling method are input into a pre-established carbon emission regression prediction model for combined prediction to obtain a total target carbon emission value for the recycling process; wherein the carbon emission regression prediction model can be understood as a multiple linear regression model established in advance based on the battery model, capacity, weight, scrap type, recycling method, and corresponding carbon emission values ​​of a large number of scrapped batteries, with the carbon emission value as the dependent variable and the other variables as independent variables. The specific mathematical model representation can be determined by fitting data collected in actual applications and is not limited here;

[0071] According to the total carbon emission target value of the recycling process and the historical carbon emission proportion of each process step in the optimal recycling method, the carbon emission target value of each process step is obtained; wherein, the carbon emission target value of each process step can be understood as the total carbon emission target value of the recycling process multiplied by the historical carbon emission proportion of the corresponding process step. For example, the total carbon emission target value of the recycling process is A, and the historical carbon emission proportion of the first process step in the corresponding optimal recycling method is w%, then the corresponding carbon emission target value of each process step is A*w%; correspondingly, the historical carbon emission proportion of the process step can also be obtained by collecting a large amount of relevant historical data for analysis, which will not be described in detail here.

[0072] S13. Recycle the lithium battery to be recycled according to the optimal recycling method, and obtain the actual total carbon emissions and actual total economic income of recycling based on the actual carbon emissions and actual recycling economic income corresponding to each process step; wherein, the actual carbon emissions include direct carbon emissions and indirect carbon emissions; for example, in the disassembly recycling of the optimal recycling method dry recycling, the corresponding direct carbon emissions can be understood as the amount of carbon dioxide gas directly generated during the recycling process, which can be obtained by installing a corresponding carbon dioxide gas monitoring device and monitoring the gas flow and carbon dioxide concentration generated during the recycling process through infrared technology; indirect carbon emissions can be understood as the carbon emissions generated by the conversion of energy use (such as electricity, natural gas, gasoline and steam, etc.), material use (liquid nitrogen, water, acid and alkali reagents, extractants, precipitants, etc.) and depreciation of production equipment during the recycling process, which can be expressed as:

[0073] Among them, C ind Indicates indirect carbon emissions; E i and λ en,i Respectively represent the i-th energy usage and the corresponding carbon emission coefficient; M j and λ ma,j They represent the usage of the jth material and the corresponding carbon emission coefficient; D k and λ de,k They represent the depreciation value of the k-th equipment and the corresponding carbon emission coefficient respectively;

[0074] At the same time, the actual recycling economic income can be understood as the economic income that can be obtained by directly selling the materials obtained in each process step. It can be reasonably calculated based on the actual recycling process steps and is not specifically limited here. For example, the recycling economic value generated by obtaining valuable metals such as copper, aluminum, lithium, cobalt, manganese, nickel and their compounds, graphite, diaphragms, and positive and negative electrode materials;

[0075] After obtaining the actual carbon emissions and actual recycling economic income of each process step through the above method, the actual total recycling carbon emissions and actual total recycling economic income of the entire recycling process can be obtained by adding them up, which will not be described in detail here.

[0076] S14. Obtain a corresponding recycling value index based on the actual total carbon emissions recovered and the actual total economic income from recycling, and conduct a comprehensive assessment of the carbon emissions of the recycling process of the lithium battery to be recycled based on the comparative analysis results of the actual total carbon emissions recovered and the carbon emission monitoring target value, combined with the recycling value index, to obtain a carbon emission assessment result; wherein, in principle, the carbon emission assessment result can be directly obtained by judging whether the carbon emissions in the actual recycling process exceed the corresponding target value. However, in actual applications, the fact that carbon emissions do not exceed the standard only proves that the carbon emissions of the recycling process meet the standard, and does not necessarily guarantee that the recycling can achieve the expected economic value. In order to ensure that the recycling assessment is more in line with practical applications, this embodiment preferably combines the carbon emission monitoring target value, the actual total carbon emissions recovered, and the actual economic income from recycling to establish a scientific and reasonable evaluation index. The carbon emission monitoring results obtained by judging whether the actual total carbon emissions recovered are less than the carbon emission monitoring target value and whether the recycling economic income is meaningful relative to the carbon emissions, as well as the final assessment results of the carbon emission exceeding process steps that can be optimized when the carbon emissions of the entire process exceed the standard,

[0077] The above recycling value index can be understood as an indicator for determining whether the recycling income obtained has actual economic significance relative to the recovered carbon emissions. Specifically, the recycling value index is expressed as:

[0078] Where I represents the recycling value index; E inc and C rel They represent the actual total economic income from recycling and the actual total carbon emissions from recycling respectively;

[0079] Specifically, the step of comprehensively evaluating the carbon emissions of the recycling process of the lithium battery to be recycled based on the comparative analysis results of the actual total recycled carbon emissions and the carbon emission monitoring target value in combination with the recycling value index to obtain the carbon emission evaluation result includes:

[0080] Determine whether the actual total carbon emissions from recycling are less than the total target carbon emissions from the recycling process in the carbon emissions monitoring target value; if not, set the carbon emissions assessment result as exceeding the carbon emissions standard; otherwise, determine whether the recycling value index is greater than a preset value threshold; wherein the preset value threshold can be selected based on actual conditions and is not specifically limited here;

[0081] If the recycling value index is greater than the preset value threshold, the carbon emission assessment result is set as carbon emission qualified; otherwise, the carbon emission assessment result is set as carbon emission exceeding the standard;

[0082] The process of obtaining the above-mentioned carbon emission assessment results can be understood as follows: when the actual total carbon emissions recovered are less than the total target carbon emissions of the recycling process, it is necessary to further determine whether the obtained recovery value index is greater than the preset value threshold in order to obtain the carbon emission assessment result that the carbon emissions do not exceed the standard (carbon emissions are qualified); on the contrary, if the actual total carbon emissions recovered are greater than the total target carbon emissions of the recycling process, or the actual total carbon emissions recovered are less than the total target carbon emissions of the recycling process, and the recovery value index is less than the preset value threshold, the obtained carbon emission assessment result is that the carbon emissions exceed the standard, which proves that there is room for carbon emission optimization in the recycling method. The process steps to be optimized can be further determined based on the actual carbon emissions collected in each process step and the actual recovery economic income to provide relevant optimization guidance;

[0083] When the carbon emission assessment result is that the carbon emission exceeds the standard, the process steps to be optimized whose actual carbon emission exceeds the carbon emission target value of the corresponding process step in the carbon emission monitoring target value are obtained, and the process steps to be optimized are graded and prompted for carbon emission exceeding the standard; wherein, the process steps to be optimized can be understood as the process steps with carbon emission exceeding the standard obtained by screening according to the actual carbon emission of each process step, and the actual carbon emission corresponding to each process step can be directly compared and analyzed with the corresponding process step carbon emission target value to find out the process steps whose actual carbon emission exceeds the process step carbon emission target value as the process steps to be optimized, and ranked in descending order according to the specific values ​​of the calculated actual carbon emission exceeding the process step carbon emission target value, and graded and prompted for carbon emission exceeding the standard according to the ranking, for example, the process steps with a higher ranking are given a higher prompt level, that is, there is a larger optimization space and optimization solution is urgently needed; the process steps with a lower ranking are given a relatively low prompt level, that is, the optimization space is not too large, and you can choose whether to optimize according to the actual situation;

[0084] At the same time, in order to ensure the rationality of the process steps to be optimized and the corresponding graded prompts, after finding the process steps whose actual carbon emissions exceed the carbon emission target values ​​of the process steps, the corresponding process recovery value index can be further calculated based on the actual carbon emissions and the actual recovery economic income, and the obtained process recovery value index and the specific values ​​of the actual carbon emissions exceeding the carbon emission target values ​​of the process steps are weighted and summed according to the preset weight values, and then the summed results are ranked in descending order, and graded prompts for carbon emissions exceeding the standard are given according to the ranking. For example, process steps with higher rankings are given higher prompt levels, and process steps with lower rankings are given relatively lower prompt levels. It should be noted that the aforementioned preset weight values ​​can be selected and determined according to actual application needs, and are not limited here.

[0085] The embodiment of the present application inputs the scrap information of the obtained lithium batteries to be recycled into a pre-built scrap classification model for classification prediction to obtain the scrap type, and determines the optimal recycling method according to the scrap type, and predicts the recycling carbon emissions of the lithium batteries to be recycled to obtain the carbon emission monitoring target value, and then evaluates the carbon emissions of the recycling process of the lithium batteries to be recycled in accordance with the obtained actual carbon emissions and actual recycling economic income corresponding to each process step in the recycling process of the lithium batteries to be recycled according to the optimal recycling method, and obtains the actual recycling total carbon emissions and actual recycling total economic income, and combines them with the carbon emission monitoring target value to obtain the technical method of carbon emission evaluation result. The technical solution is to classify lithium batteries and determine the optimal recycling method based on their scrap information, and obtain scientific and reasonable carbon emission monitoring targets based on the scrap classification and optimal recycling method. Combined with effective evaluation indicators, the carbon emissions of the lithium battery recycling process are reliably evaluated, which effectively solves the application defects of the existing carbon emission monitoring methods for lithium battery recycling. It can not only provide a universal carbon emission monitoring method for lithium battery recycling, but also realize comprehensive and accurate integrated monitoring and scientific and reasonable evaluation of carbon emissions in the lithium battery recycling process, thereby providing reliable guarantees for the optimization of the lithium battery recycling process, and has high practical application value.

[0086] It should be noted that although the steps in the above flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0087] In one embodiment, as shown in FIG5 , a carbon emission monitoring system for battery recycling is provided, the system comprising:

[0088] Type identification module 1, used to obtain scrap information of lithium batteries to be recycled, and input the scrap information into a pre-built scrap classification model for classification prediction to obtain the corresponding scrap type;

[0089] Target acquisition module 2, used to determine the corresponding optimal recycling method according to the scrap type, and predict the recycled carbon emissions of the lithium battery to be recycled based on the scrap type and the optimal recycling method, to obtain a carbon emission monitoring target value;

[0090] The carbon emission monitoring module 3 is used to recycle the lithium battery to be recycled according to the optimal recycling method, and obtain the actual total carbon emissions and actual total economic income of recycling based on the actual carbon emissions and actual economic income of recycling corresponding to each process step;

[0091] The carbon emission assessment module 4 is used to obtain a corresponding recycling value index based on the actual total carbon emissions recovered and the actual total economic income from recycling, and based on the comparative analysis results of the actual total carbon emissions recovered and the carbon emission monitoring target value, combined with the recycling value index, comprehensively evaluate the carbon emissions of the recycling process of the lithium battery to be recycled to obtain a carbon emission assessment result.

[0092] For the specific definition of the carbon emission monitoring system for battery recycling, please refer to the definition of the carbon emission monitoring method for battery recycling above. The corresponding technical effects can also be obtained equivalently, so they will not be repeated here. The various modules in the above-mentioned carbon emission monitoring system for battery recycling can be implemented in whole or in part through software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0093] Figure 6 shows the internal structure of a computer device in one embodiment, which can specifically be a terminal or server. As shown in Figure 6, the computer device includes a processor, memory, a network interface, a display, a camera, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a carbon emission monitoring method for battery recycling is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch layer covering the display screen, or it can be buttons, a trackball, or a touchpad provided on the computer device housing, or it can be an external keyboard, touchpad, or mouse.

[0094] Those skilled in the art will understand that the structure shown in FIG6 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 computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0095] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0096] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0097] In summary, the embodiments of the present invention provide a carbon emission monitoring method and system for battery recycling and utilization. The carbon emission monitoring method for battery recycling and utilization realizes the input of the scrap information of the obtained lithium batteries to be recycled into a pre-built scrap classification model for classification prediction to obtain the scrap type, and determines the optimal recycling method according to the scrap type. After the recycling carbon emissions of the lithium batteries to be recycled are predicted to obtain the carbon emission monitoring target value, the actual carbon emissions and actual recycling economic income corresponding to each process step in the recycling treatment of the lithium batteries to be recycled according to the optimal recycling method are obtained, and the actual total recycling carbon emissions and actual total recycling economic income are obtained. Combined with the carbon emission monitoring target value, the carbon emissions of the recycling process of the lithium batteries to be recycled are evaluated to obtain the carbon emission evaluation results. The technical solution can not only provide a universal carbon emission monitoring method for lithium battery recycling, but also realize comprehensive and accurate integrated monitoring and scientific and reasonable evaluation of carbon emissions in the lithium battery recycling process, thereby providing reliable guarantee for the optimization of the lithium battery recycling process flow, and has high practical application value.

[0098] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] 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 method for monitoring carbon emissions from battery recycling, characterized in that: The method comprises the following steps: Obtaining scrap information of the lithium battery to be recycled, and inputting the scrap information into a pre-built scrap classification model for classification prediction to obtain the corresponding scrap type; Determine the corresponding optimal recycling method according to the scrap type, and predict the recycling carbon emissions of the lithium battery to be recycled according to the scrap type and the optimal recycling method to obtain a carbon emission monitoring target value; Recycling the lithium battery to be recycled according to the optimal recycling method, and obtaining the actual total carbon emissions and the actual total economic income of recycling according to the actual carbon emissions and the actual economic income of recycling corresponding to each process step; According to the actual total carbon emissions from recycling and the actual total economic income from recycling, a corresponding recycling value index is obtained, and based on the comparative analysis results of the actual total carbon emissions from recycling and the carbon emission monitoring target value, the carbon emissions of the recycling process of the lithium battery to be recycled are comprehensively evaluated in combination with the recycling value index to obtain a carbon emission evaluation result.

2. The method for monitoring carbon emissions from battery recycling as claimed in claim 1, characterized in that: The scrapping information includes an appearance image, a three-dimensional point cloud feature map and a battery capacity retention rate.

3. The method for monitoring carbon emissions from battery recycling as claimed in claim 2, characterized in that: The step of obtaining scrap information of lithium batteries to be recycled comprises: The image data and point cloud data of the lithium battery to be recycled are collected by a laser scanning device, and the image data and point cloud data are preprocessed respectively to obtain a corresponding appearance image and a three-dimensional point cloud feature map; The number of charge and discharge cycles of the lithium battery to be recycled is obtained, and the corresponding battery capacity retention rate is obtained according to the number of charge and discharge cycles and a preset battery capacity retention attenuation model; the battery capacity retention rate is expressed as: Rc=Rc0e -dn Among them, Rc0, -d and n represent the initial capacity retention rate, capacity attenuation coefficient and number of charging cycles respectively; Rc represents the battery capacity retention rate after n cycles.

4. The method for monitoring carbon emissions from battery recycling as claimed in claim 2, characterized in that: The scrap classification model includes a first classification module and a second classification module connected in sequence; the first classification module includes an ACmix network model; The step of inputting the scrap information into a pre-built scrap classification model for classification prediction to obtain the corresponding scrap type includes: Inputting the appearance image and the three-dimensional point cloud feature map into the first classification module for classification and recognition to obtain a corresponding initial classification result; Determine whether the initial classification result is normal. If not, use the initial classification result as the corresponding scrap type; if yes, input the corresponding battery capacity retention rate into the second classification module for abnormality inspection, and obtain the corresponding scrap type according to the corresponding abnormality inspection result.

5. The carbon emission monitoring method for battery recycling as claimed in claim 1, characterized in that: The types of scrapping include appearance damage and deformation, liquid leakage, abnormal internal bulging and abnormal capacity retention rate; The step of determining the corresponding optimal recycling method according to the scrap type includes: According to the scrap type, searching a pre-built type recycling mapping relationship library to obtain the corresponding optimal recycling method; The type recycling mapping relationship library is a database that stores the mapping relationship between scrap types and optimal recycling methods.

6. The method for monitoring carbon emissions from battery recycling as claimed in claim 1, characterized in that: The carbon emission monitoring target value includes the total carbon emission target value of the recycling process and the corresponding carbon emission target value of each process step; The step of predicting the carbon emission of the recycled lithium battery according to the scrap type and the optimal recycling method to obtain the carbon emission monitoring target value comprises: Obtaining the battery model, capacity and weight of the lithium battery to be recycled; Inputting the battery model, capacity, weight, scrap type and optimal recycling method into a pre-built carbon emission regression prediction model for combined prediction to obtain a total target value of carbon emissions from the recycling process; According to the total carbon emission target value of the recycling process and the historical carbon emission proportion of each process step in the optimal recycling method, the carbon emission target value of each process step is obtained.

7. The method for monitoring carbon emissions from battery recycling as claimed in claim 6, characterized in that: The step of comprehensively evaluating the carbon emissions of the recycling process of the lithium battery to be recycled based on the comparative analysis result of the actual total carbon emissions recycled and the carbon emission monitoring target value in combination with the recycling value index to obtain the carbon emission evaluation result comprises: Determine whether the actual total carbon emission of recycling is less than the total target carbon emission value of the recycling process in the carbon emission monitoring target value; if not, set the carbon emission assessment result as carbon emission exceeding the standard; otherwise, determine whether the recycling value index is greater than a preset value threshold; If the recycling value index is greater than the preset value threshold, the carbon emission assessment result is set as qualified carbon emission; otherwise, the carbon emission assessment result is set as excessive carbon emission; When the carbon emission assessment result is that the carbon emission exceeds the standard, the process steps to be optimized whose actual carbon emission exceeds the carbon emission target value of the corresponding process step in the carbon emission monitoring target value are obtained, and the carbon emission exceeding standard classification prompt is given to the process steps to be optimized.

8. The method for monitoring carbon emissions from battery recycling as claimed in claim 1, characterized in that: The recovery value index is expressed as: Where I represents the recycling value index; E inc and C rel They represent the actual total economic income from recycling and the actual total carbon emissions from recycling respectively.

9. A carbon emission monitoring system for battery recycling, characterized in that: The system comprises: A type identification module is used to obtain the scrap information of the lithium battery to be recycled, and input the scrap information into a pre-built scrap classification model for classification prediction to obtain the corresponding scrap type; A target acquisition module is used to determine the corresponding optimal recycling method according to the scrap type, and to predict the recycling carbon emission of the lithium battery to be recycled according to the scrap type and the optimal recycling method to obtain a carbon emission monitoring target value; A carbon emission monitoring module, used to recycle the lithium battery to be recycled according to the optimal recycling method, and obtain the actual total carbon emission and the actual total economic income of recycling according to the actual carbon emission and the actual recycling economic income corresponding to each process step; The carbon emission assessment module is used to obtain a corresponding recycling value index based on the actual total carbon emissions from recycling and the actual total economic income from recycling, and based on the comparative analysis results of the actual total carbon emissions from recycling and the carbon emission monitoring target value, combined with the recycling value index, comprehensively evaluate the carbon emissions of the recycling process of the lithium battery to be recycled to obtain a carbon emission assessment result.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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