Electrolyte recovery evaluation method of power lithium battery

Abstract

The invention discloses an electrolyte recovery evaluation method for a power lithium battery, and relates to the field of lithium battery electrolyte recovery, and the method comprises the steps: constructing a distributed edge node network based on the regional distribution characteristics of the electrolyte recovery of the power lithium battery; establishing an electrolyte stability and recovery rate evaluation model, and preprocessing and evaluating the to-be-recovered waste lithium battery electrolyte; obtaining the transportation distance of electrolyte recovery, and judging the transportation risk of electrolyte recovery according to the transportation distance of electrolyte recovery; according to the stability, the recovery rate, the transportation distance and the risk of electrolyte recovery, a comprehensive evaluation model for power lithium battery electrolyte recovery is constructed based on a neural network, and comprehensive evaluation is carried out on power lithium battery electrolyte recovery. The method has the advantages that the stability, the recovery rate, the transportation distance and the risk of electrolyte recovery are comprehensively evaluated, the transportation process is accurately optimized, the transportation and risk cost is reduced, and double improvement of economic benefits and safety is achieved.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery electrolyte recycling, and specifically relates to a method for evaluating the recycling of power lithium battery electrolytes. Background Art

[0002] The recycling of power lithium battery electrolytes refers to the process of extracting, purifying, and reusing electrolytes and their effective components (such as organic solvents, lithium salts, additives, etc.) from waste power lithium batteries through physical, chemical, or biological methods. With the rapid development of the new energy vehicle and electric vehicle markets, the demand for power lithium batteries has increased sharply, and the number of waste batteries has also risen accordingly. As an important component of power lithium batteries, the recycling and reuse of electrolytes are of great significance for environmental protection and resource recycling. By recycling the electrolytes of lithium batteries, the overexploitation of rare metals such as lithium and cobalt can be effectively reduced, the dependence on non-renewable resources can be lowered, and at the same time, the leakage of harmful substances in the electrolytes to pollute the soil and water sources can be avoided, such as fluorides generated by the decomposition of lithium hexafluorophosphate. Through the evaluation of electrolyte recycling, the recycling process can be effectively optimized, costs can be reduced, resource utilization rates can be increased, and economic value can be created.

[0003] Currently, the recycling of power lithium battery electrolytes faces multiple challenges. Economically, the recycling cost is high, while the added value of recycled products is limited, resulting in poor economic benefits. Technologically, due to the unbalanced distribution of the focus directions of power lithium battery electrolyte recycling technologies, adopting a unified recycling strategy is difficult to achieve the effects of efficient separation and precise purification, affecting the quality of electrolyte recycling. Logistically, as electrolytes are hazardous chemicals, the transportation cost increases with the increase in distance, and the transportation risk also increases, adding to the economic cost of electrolyte recycling. In addition, the industry lacks a comprehensive evaluation system for electrolyte recycling in terms of stability, recoverability, transportation distance, transportation risk, and recycling process, making it difficult to scientifically guide recycling practices and restricting the large-scale and sustainable development of this field. Summary of the Invention

[0004] To solve the above technical problems, a method for evaluating the recycling of power lithium battery electrolytes is provided. This technical solution solves the problems raised in the above background art, including high recycling costs, limited added value of recycled products resulting in poor economic benefits, the unbalanced distribution of the focus directions of power lithium battery electrolyte recycling technologies, making it difficult to achieve the effects of efficient separation and precise purification with a unified recycling strategy, affecting the quality of electrolyte recycling, the increase in transportation cost with the increase in distance and the increase in transportation risk. In addition, the industry lacks a comprehensive evaluation system for electrolyte recycling in terms of stability, recoverability, transportation distance, transportation risk, and recycling process, making it difficult to scientifically guide recycling practices and restricting the large-scale and sustainable development of this field.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An evaluation method for the electrolyte recovery of power lithium batteries, comprising: Based on the regional distribution characteristics of the electrolyte recovery of power lithium batteries, a distributed edge node network is constructed to complete the pretreatment before electrolyte recovery; Obtain the status data of the waste lithium battery electrolyte, establish an evaluation model for electrolyte stability and recoverability, and perform pretreatment and evaluation on the waste lithium battery electrolyte to be recycled; According to the recovery process of the power lithium battery electrolyte, obtain the transportation distance of the electrolyte recovery, and judge the transportation risk of the electrolyte recovery based on the transportation distance of the electrolyte recovery; Based on the stability, recoverability, transportation distance, and transportation risk of the electrolyte recovery, a comprehensive evaluation model for the electrolyte recovery of power lithium batteries is constructed based on a neural network to comprehensively evaluate the electrolyte recovery of power lithium batteries.

[0006] Preferably, the constructing a distributed edge node network based on the regional distribution characteristics of the electrolyte recovery of power lithium batteries to complete the pretreatment before electrolyte recovery specifically includes: According to the population density and the density of the lithium battery-containing industry, the area of the electrolyte recovery of power lithium batteries is divided into several sub-areas; According to the population density and the density of the lithium battery-containing industry, obtain the number of distributed edge nodes in each sub-area; Obtain the number of distributed edge nodes in each sub-area, and construct a distributed edge node network based on the SDN network architecture; Through the pretreatment of the electrolyte recovery by the distributed edge nodes in each sub-area, based on the distributed edge node network, obtain the pretreatment data information of the electrolyte recovery of power lithium batteries in each sub-area; The expression for the number of distributed edge nodes in each sub-area is: ; In the formula, is the number of distributed edge nodes in the th sub-area, is the balance coefficient, which is used to prevent the obtained value from being too large or too small, and at the same time, eliminate the influence of the unit dimension of the density, and are the weighted values of the population density and the density of the lithium battery-containing industry respectively, and accurate values can be obtained by using the least square method, is the population density value of the th sub-area, is the density value of the lithium battery-containing industry in the th sub-area,

[0007] Preferably, the obtaining of the status data of the waste lithium battery electrolyte, establishing an evaluation model for electrolyte stability and recoverability, and pre-treating and evaluating the waste lithium battery electrolyte to be recycled specifically include: Obtain the status data of the waste lithium battery electrolyte through each sub-region distributed edge node, where the status data of the electrolyte includes: electrolyte concentration, composition, component mass, and temperature; Normalize the status data of the electrolyte to eliminate the influence of data peaks and data dimensions; Evaluate the electrolyte stability according to the normalized status data of the electrolyte; Evaluate the recoverability of the electrolyte according to the normalized status data of the electrolyte; The expression for evaluating the electrolyte stability is: ; In the formula, is the electrolyte stability evaluation value, , , are the weights of the toxic parameter components, corrosive parameter components, and flammable and explosive parameter components in the electrolyte respectively, , , are the mass values of the th toxic parameter component, corrosive parameter component, and flammable and explosive parameter component in the electrolyte respectively, , , are the number of types of toxic parameter components, corrosive parameter components, and flammable and explosive parameter components in the electrolyte respectively, is the amplification effect of temperature on toxicity, is the toxicity risk coefficient caused by vibration leading to electrolyte leakage, is the influence coefficient of temperature on corrosion, is the influence coefficient of temperature on flammability and explosiveness, is the influence coefficient of vibration on flammability and explosiveness; The expression for evaluating the recoverability of the electrolyte is: ; In the formula, is the recoverability of the electrolyte, is the number of types of recoverable components in the electrolyte, is the detection value of the mass of the th recoverable component, is the mass value of obtaining the th recoverable component through technical recovery, is the weight of the th recoverable component.

[0008] Preferably, for the recycling process based on the recycling of power lithium battery electrolyte, the transportation distance of electrolyte recycling is obtained, and based on the transportation distance of electrolyte recycling, the transportation risk of electrolyte recycling is judged, which specifically includes: According to the status data and battery type of the waste lithium battery electrolyte, the waste lithium battery electrolyte recycling is packaged and classified; By analyzing the characteristics of the recycling process of power lithium battery electrolyte, combining the battery type and electrolyte composition, the compatibility of the recycling process with the target battery is evaluated, and a hierarchical management strategy is established accordingly; The hierarchical management strategy refers to dividing the electrolyte recycling of different battery types and electrolyte compositions into the recycling processes corresponding to the mapping relationships according to the mapping relationship between the compatibility of the recycling process with the target battery; Through GIS and positioning technology, the location information of the recycling center and each sub-region distributed edge node is obtained; Based on the location information of the recycling center and each sub-region distributed edge node, and based on the GIS road network data, the actual transportation distance of electrolyte recycling is obtained; Based on the actual transportation distance of electrolyte recycling, combined with the electrolyte stability evaluation value, the transportation risk of electrolyte recycling is judged; The expression of the transportation risk of the electrolyte recycling is: ; In the formula, is the transportation risk of electrolyte recycling, is the number of road segments in the transportation process of electrolyte recycling, is the initial risk setting value based on the electrolyte stability evaluation value, is the distance risk coefficient, is the actual transportation distance of electrolyte recycling, is the th risk coefficient of the road segment in the transportation process of electrolyte recycling.

[0009] Preferably, for the comprehensive evaluation of the recycling of power lithium battery electrolyte based on the stability, recoverability, transportation distance and transportation risk of electrolyte recycling, and based on a neural network, the specific steps include: Taking the stability, recoverability, transportation distance, transportation risk and recycling process label of electrolyte recycling as input features, where the transportation distance reflects the transportation cost; By expert evaluation or historical data annotation, the comprehensive score of power lithium battery electrolyte recycling is set as the output label; According to the normalization formula, the input feature data is normalized to eliminate the influence of data dimensions; Set the training set, validation set, and test set respectively according to historical data or test simulation experiments; According to the input features, the input layer uses 5 neurons, the hidden layer uses 2 layers with 64 neurons in each layer, and the output layer uses 1 neuron; The loss function uses the mean squared error, , where, is the number of samples, is the predicted value of the comprehensive score; is the target value of the comprehensive score; The activation function uses a piecewise expression, ; Construct a comprehensive evaluation model for the recycling of power lithium battery electrolytes to comprehensively evaluate the recycling of power lithium battery electrolytes.

[0010] Furthermore, this solution proposes an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an evaluation method for the electrolyte recycling of a power lithium battery as described above.

[0011] Still further, this solution proposes a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements an evaluation method for the electrolyte recycling of a power lithium battery as described above.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides an evaluation method for the electrolyte recycling of power lithium batteries. By constructing a distributed edge node network, it fully considers the regional distribution characteristics of the electrolyte recycling of power lithium batteries. According to the characteristics of different regions, it can more efficiently complete the pretreatment work before electrolyte recycling, improve the adaptability and flexibility of the recycling process. Secondly, by obtaining the state data of used lithium battery electrolytes, it establishes an evaluation model for electrolyte stability and recoverability, realizes accurate evaluation of electrolyte stability and recoverability, helps the risk assessment of electrolyte recycling distribution and transportation, and improves the recycling efficiency and quality. Moreover, by obtaining the transportation distance and transportation risk of electrolyte recycling, it comprehensively evaluates the transportation link, can identify potential safety problems and economic losses in transportation in advance. Finally, based on a neural network, the present invention constructs a comprehensive evaluation model for the electrolyte recycling of power lithium batteries. Through this model, it can comprehensively consider multiple factors such as electrolyte stability, recoverability, transportation distance, transportation risk, and recycling process, and comprehensively and objectively evaluate the electrolyte recycling, so as to achieve the effect of accurately optimizing the transportation process, reducing transportation and risk costs, and realizing the dual improvement of economic benefits and safety. Description of the Drawings

[0013] Figure 1 It is a flowchart of a method for evaluating the recycling of the electrolyte of a power lithium battery according to the present invention; Figure 2 Based on the regional distribution characteristics of the recycling of the electrolyte of a power lithium battery, a distributed edge node network is constructed to complete the pretreatment before the electrolyte recycling; Figure 3 It is a flowchart of obtaining the state data of the electrolyte of waste lithium batteries, establishing an evaluation model for the stability and recoverability of the electrolyte, and performing pretreatment and evaluation on the electrolyte of waste lithium batteries to be recycled according to the present invention; Figure 4 According to the recycling process of the electrolyte of a power lithium battery, the transportation distance of the electrolyte recycling is obtained, and the transportation risk of the electrolyte recycling is judged according to the transportation distance of the electrolyte recycling; Figure 5 Based on the stability, recoverability, transportation distance and transportation risk of the electrolyte recycling, a comprehensive evaluation model for the recycling of the electrolyte of a power lithium battery is constructed based on a neural network to comprehensively evaluate the recycling of the electrolyte of a power lithium battery; Figure 6 It is a structural diagram of the architecture of an electronic device proposed by the present invention; Figure 7 It is a schematic structural diagram of a computer-readable storage medium proposed by the present invention. Detailed Embodiment

[0014] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0015] Referring to Figure 1 shown, a method for evaluating the recycling of the electrolyte of a power lithium battery includes: Based on the regional distribution characteristics of the recycling of the electrolyte of a power lithium battery, a distributed edge node network is constructed to complete the pretreatment before the electrolyte recycling; Obtain the state data of the electrolyte of waste lithium batteries, establish an evaluation model for the stability and recoverability of the electrolyte, and perform pretreatment and evaluation on the electrolyte of waste lithium batteries to be recycled; According to the recycling process of the electrolyte of a power lithium battery, obtain the transportation distance of the electrolyte recycling, and judge the transportation risk of the electrolyte recycling according to the transportation distance of the electrolyte recycling; According to the stability, recoverability, transportation distance and transportation risk of the electrolyte recycling, a comprehensive evaluation model for the recycling of the electrolyte of a power lithium battery is constructed based on a neural network to comprehensively evaluate the recycling of the electrolyte of a power lithium battery.

[0016] It can be explained that the evaluation of the electrolyte recovery of power lithium batteries involves many factors such as the regional distribution characteristics of the electrolyte recovery of power lithium batteries, electrolyte stability and recoverability, transportation distance and transportation risk, and recovery process. The evaluation of a single factor will lead to the one-sidedness and unreliability of the evaluation of the electrolyte recovery of power lithium batteries. Therefore, this solution integrates and evaluates data from multiple aspects by constructing a distributed edge node network, establishing an evaluation model for electrolyte stability and recoverability, obtaining the transportation distance of electrolyte recovery, and judging the transportation risk of electrolyte recovery. Based on a neural network, a comprehensive evaluation model for the electrolyte recovery of power lithium batteries is constructed to comprehensively and objectively evaluate the electrolyte recovery, so as to comprehensively evaluate the stability, recoverability, transportation distance, and risk of electrolyte recovery, accurately optimize the transportation process, reduce transportation and risk costs, and achieve the dual goals of improving economic efficiency and safety.

[0017] Refer to Figure 2 As shown, based on the regional distribution characteristics of the electrolyte recovery of power lithium batteries, a distributed edge node network is constructed to complete the pretreatment before electrolyte recovery, which specifically includes: According to the population density and the density of the lithium battery-containing industry, the area of the electrolyte recovery of power lithium batteries is divided into several sub-areas; According to the population density and the density of the lithium battery-containing industry, the number of distributed edge nodes in each sub-area is obtained; The number of distributed edge nodes in each sub-area is obtained, and based on the SDN network architecture, a distributed edge node network is constructed; Through the pretreatment of each sub-area distributed edge node before electrolyte recovery, based on the distributed edge node network, the pretreatment data information of the electrolyte recovery of power lithium batteries in each sub-area is obtained; The expression for the number of distributed edge nodes in each sub-area is: ; In the formula, is the number of distributed edge nodes in the th sub-area, is the balance coefficient, which is used to prevent the obtained value from being too large or too small. At the same time, it eliminates the influence of the unit dimension of density. and are the weighted values of the population density and the density of the lithium battery-containing industry respectively, and the accurate values can be obtained by using the least squares method. is the population density value of the th sub-area, is the density value of the lithium battery-containing industry in the th sub-area,

[0018] It can be explained that population density and the industrial density of lithium battery-containing are two important factors affecting the recycling of power lithium battery electrolytes. When setting up distributed edge nodes, the impacts of population density and the industrial density of lithium battery-containing need to be fully considered to avoid resource waste and increased difficulty in electrolyte recycling scheduling caused by unreasonable settings of distributed edge nodes. This solution provides distributed edge node support for the pre-treatment before the recycling of power lithium battery electrolytes by constructing a distributed edge node network, and establishes an expression for the number of distributed edge nodes in each sub-region. The number of distributed edge nodes in each sub-region is scientifically and reasonably obtained through the formula, and then through the uniform distribution method, the distributed edge nodes in each sub-region are evenly distributed into each sub-region. Among them, the balance coefficient is used to adjust the numerical size and eliminate the influence of unit dimensions, and can be obtained according to historical data or test experiments. The weighted values of population density and industrial density 、 , are determined by the least squares method to ensure reasonable weight distribution. This process fully considers regional characteristics, and through precise configuration of edge nodes, efficient and targeted pre-treatment is achieved, providing reliable data support for subsequent electrolyte recycling, and helping to improve the overall recycling efficiency and quality.

[0019] Refer to Figure 3 As shown, the steps of obtaining the state data of used lithium battery electrolytes, establishing an evaluation model for electrolyte stability and recoverability, and pre-treating and evaluating the used lithium battery electrolytes to be recycled specifically include: Through the distributed edge nodes in each sub-region, obtain the state data of used lithium battery electrolytes. Among them, the state data of the electrolyte includes: electrolyte concentration, composition, component mass, and temperature; Perform normalization processing on the state data of the electrolyte to eliminate the influence of data peaks and data dimensions; Evaluate the electrolyte stability according to the normalized state data of the electrolyte; Evaluate the recoverability of the electrolyte according to the normalized state data of the electrolyte; The expression for evaluating the electrolyte stability is: ; In the formula, is the electrolyte stability evaluation value, 、 、 are the weights of the toxic parameter components, corrosive parameter components, and flammable and explosive parameter components in the electrolyte respectively, 、 、 are the first The mass values of the toxicity parameter components, corrosiveness parameter components, and flammable and explosive parameter components , , are the number of component types of the toxicity parameter components, corrosiveness parameter components, and flammable and explosive parameter components in the electrolyte respectively, is the amplification effect of temperature on toxicity, is the toxicity risk coefficient caused by vibration leading to electrolyte leakage, is the influence coefficient of temperature on corrosiveness, is the influence coefficient of temperature on flammability and explosiveness, is the influence coefficient of vibration on flammability and explosiveness; The evaluation expression for the recoverability of the electrolyte is: ; In the formula, is the recoverability of the electrolyte, is the number of recoverable component types in the electrolyte, is the detection value of the mass of the th recoverable component, is the mass value of obtaining the th recoverable component through technical recovery, is the weight of the th recoverable component.

[0020] It can be explained that due to the danger in the transportation and storage of lithium battery electrolyte recycling, it is easy to cause potential safety hazards. Among them, the components of toxicity, corrosiveness, and flammability and explosiveness need to be particularly concerned. Secondly, to improve the recycling quality of lithium battery electrolyte and the distribution of scientific recycling processes, it is necessary to analyze the recoverability of lithium battery electrolyte. Therefore, this solution obtains the status data of used lithium battery electrolyte through distributed edge nodes in each sub-region, and evaluates the electrolyte stability and recoverability according to the electrolyte stability and recoverability evaluation model, so as to improve the safety and recovery efficiency of the lithium battery electrolyte recycling process. Among them, in the formula, , , can be dynamically calibrated by the Analytic Hierarchy Process (AHP), while , in the formula, is the actual detected temperature, is the temperature reference value for toxicity volatilization, is the temperature sensitivity attenuation constant, is the toxicity amplification coefficient, , in the formula, is the fitted value of vibration generated during vehicle transportation, is the vibration threshold for toxicity leakage, , in the formula, is the corrosion amplification coefficient, is the corrosion amplification temperature reference value, , where is the flash point temperature, , where is the critical threshold for flammability and explosion. In particular, to ensure the consistency of units on both sides of the formula, normalization processing is performed on the state data, which can eliminate the influence of data peaks and dimensions, making data of different magnitudes and ranges comparable and improving the accuracy and reliability of data analysis.

[0021] Referring to Figure 4 shown, the recovery process based on the recovery of power lithium battery electrolyte, obtaining the transportation distance of electrolyte recovery, and judging the transportation risk of electrolyte recovery according to the transportation distance of electrolyte recovery specifically includes: According to the state data and battery type of used lithium battery electrolyte, the used lithium battery electrolyte recovery is sub-packed and classified; By analyzing the characteristics of the recovery process of power lithium battery electrolyte, combining the battery type and electrolyte composition, evaluating the compatibility of the recovery process with the target battery, and accordingly establishing a hierarchical management strategy; The hierarchical management strategy refers to dividing the electrolyte recovery of different battery types and electrolyte compositions into the recovery processes corresponding to the mapping relationships according to the mapping relationship between the compatibility of the recovery process with the target battery; Through GIS and positioning technology, obtain the location information of the recovery center and distributed edge nodes in each sub-region; According to the location information of the recovery center and distributed edge nodes in each sub-region, based on the GIS road network data, obtain the actual transportation distance of electrolyte recovery; According to the actual transportation distance of electrolyte recovery, combined with the electrolyte stability evaluation value, judge the transportation risk of electrolyte recovery; The expression for the transportation risk of electrolyte recovery is: ; where is the transportation risk of electrolyte recovery, is the number of road segments in the transportation process of electrolyte recovery, is the initial risk setting value based on the electrolyte stability evaluation value, is the distance risk coefficient, is the actual transportation distance of electrolyte recovery, is the th risk coefficient of the road segment in the transportation process of electrolyte recovery.

[0022] It can be explained that since the power lithium battery electrolyte recycling technology has an unbalanced distribution of process focus directions, it is difficult to achieve efficient separation and precise purification effects using a unified recycling strategy, which affects the quality of electrolyte recycling. Therefore, in this solution, a hierarchical management strategy is established to determine the mapping relationship between the recycling process and the compatibility of the target battery, thereby obtaining the actual transportation distance of electrolyte recycling through the mapping relationship. Based on the actual transportation distance of electrolyte recycling and combined with the electrolyte stability evaluation value, a transportation risk expression for electrolyte recycling is established to judge the transportation risk of electrolyte recycling, effectively solving the problems of poor separation and purification effects and low recycling quality caused by process imbalance and unified recycling strategy, and accurately judging the transportation risk of electrolyte recycling. Among them, is positively correlated with the road surface slope of the road section and negatively correlated with the smoothness; Referring to Figure 5 as shown, based on the stability, recoverability, transportation distance, and transportation risk of electrolyte recycling, a comprehensive evaluation model for power lithium battery electrolyte recycling is constructed using a neural network. The specific comprehensive evaluation of power lithium battery electrolyte recycling includes: Taking the stability, recoverability, transportation distance, transportation risk, and recycling process label of electrolyte recycling as input features, where the transportation distance reflects the transportation cost; Setting the comprehensive score of power lithium battery electrolyte recycling as the output label through expert evaluation or historical data annotation; According to the normalization formula, the input feature data is normalized to eliminate the influence of data dimensions; Setting the training set, validation set, and test set respectively according to historical data or test simulation experiments; According to the input features, the input layer uses 5 neurons, the hidden layer uses 2 layers with 64 neurons each, and the output layer uses 1 neuron; The loss function uses the mean square error, , where is the number of samples, is the predicted value of the comprehensive score; is the target value of the comprehensive score; The activation function uses a piecewise expression, ; Construct a comprehensive evaluation model for power lithium battery electrolyte recycling to comprehensively evaluate the power lithium battery electrolyte recycling.

[0023] It can be explained that the evaluation of the electrolyte recovery of power lithium batteries involves many factors such as the regional distribution characteristics of the electrolyte recovery of power lithium batteries, electrolyte stability and recoverability, transportation distance and transportation risks, and recovery processes. The evaluation of a single factor will lead to the one-sidedness and unreliability of the evaluation of the electrolyte recovery of power lithium batteries. To ensure the comprehensiveness and reliability of the evaluation of the electrolyte recovery of power lithium batteries, this solution constructs a comprehensive evaluation model for the electrolyte recovery of power lithium batteries based on neural networks. Through the neural network model, the input features and output labels are mapped and fitted, so as to comprehensively evaluate the electrolyte recovery of power lithium batteries. In particular, when there is a lack of a corresponding recovery process for electrolyte recovery, no matter how other features change, the output label is a fixed value. Therefore, this solution sets a piecewise function as the activation function in the output layer. Through this function, it is judged whether the recovery process label is lacking in the input features. If so, the fixed label is output. If not, standard activation functions such as ReLU and Sigmoid are used to process the input, thus ensuring the rationality and stability of the evaluation model and improving the evaluation efficiency and accuracy.

[0024] Furthermore, the method according to the embodiment of the present application can also be implemented with the aid of Figure 6 the architecture of the electronic device shown. As Figure 6 shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to the network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a method for evaluating the electrolyte recovery of a power lithium battery provided by the present application. The electronic device 500 may also include a user interface 508. Of course, Figure 6 the architecture shown is only exemplary. When implementing different devices, one or more components in the Figure 6 shown electronic device may be omitted according to actual needs.

[0025] Figure 7 is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of the present application. As Figure 7As shown, it is a computer-readable storage medium 600 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, it can execute an electrolyte recovery evaluation method for a power lithium battery according to an embodiment of the present application described with reference to the above drawings. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0026] In summary, the advantages of the present invention are as follows: comprehensively evaluate the stability, recoverability, transportation distance and risks of electrolyte recovery, accurately optimize the transportation process, reduce transportation and risk costs, and achieve a double improvement in economic benefits and safety.

[0027] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. An evaluation method for electrolyte recovery of a power lithium battery, characterized in that, Including: Based on the regional distribution characteristics of power lithium battery electrolyte recycling, a distributed edge node network is constructed to complete the pretreatment before electrolyte recycling; Obtain the status data of used lithium battery electrolyte, establish an evaluation model for electrolyte stability and recoverability, and perform pretreatment and evaluation on the used lithium battery electrolyte to be recycled; According to the recycling process of power lithium battery electrolyte, obtain the transportation distance of electrolyte recycling, and judge the transportation risk of electrolyte recycling based on the transportation distance of electrolyte recycling; Based on the stability, recoverability, transportation distance and transportation risk of electrolyte recycling, a comprehensive evaluation model for power lithium battery electrolyte recycling is constructed based on a neural network to comprehensively evaluate the power lithium battery electrolyte recycling.

2. The electrolyte recovery evaluation method of a power lithium battery according to claim 1, wherein The construction of the distributed edge node network based on the regional distribution characteristics of power lithium battery electrolyte recycling to complete the pretreatment before electrolyte recycling specifically includes: According to the population density and the density of industries containing lithium batteries, the area of power lithium battery electrolyte recycling is divided into several sub-areas; According to the population density and the density of industries containing lithium batteries, obtain the number of distributed edge nodes in each sub-area; Obtain the number of distributed edge nodes in each sub-area, and based on the SDN network architecture, construct a distributed edge node network; Through the distributed edge nodes in each sub-area for the pretreatment before electrolyte recycling, based on the distributed edge node network, obtain the pretreatment data information of power lithium battery electrolyte recycling in each sub-area; The expression for the number of distributed edge nodes in each sub-area is: ； Wherein, is the number of distributed edge nodes in the i-th sub-region, is the balance coefficient, which is used to prevent the obtained value from being too large or too small. At the same time, it eliminates the influence of the unit dimension of density, , are the weighted values of population density and the density of the lithium battery-containing industry respectively, and accurate values can be obtained by the least squares method, is the population density value in the i-th sub-region, is the density value of the lithium battery-containing industry in the i-th sub-region, is the rounding symbol.

3. The electrolyte recovery evaluation method for a power lithium battery according to claim 2, characterized in that The obtaining of the status data of used lithium battery electrolyte, establishing an evaluation model for electrolyte stability and recoverability, and performing pretreatment and evaluation on the used lithium battery electrolyte to be recycled specifically includes: Through the distributed edge nodes in each sub-area, obtain the status data of used lithium battery electrolyte, where the status data of the electrolyte includes: electrolyte concentration, composition, component mass and temperature; Perform normalization processing on the status data of the electrolyte to eliminate the influence of data peaks and data dimensions; Evaluate the electrolyte stability according to the normalized status data of the electrolyte; Evaluate the recoverability of the electrolyte according to the normalized status data of the electrolyte; The expression for evaluating the electrolyte stability is: ； Wherein, is the evaluation value of electrolyte stability, , , are the weights of the toxic parameter component, corrosive parameter component and flammable and explosive parameter component in the electrolyte respectively, , , are the mass values of the th toxic parameter component, corrosive parameter component and flammable and explosive parameter component in the electrolyte respectively, , , are the number of component types of the toxic parameter component, corrosive parameter component and flammable and explosive parameter component in the electrolyte respectively, is the amplification effect of temperature on toxicity, is the toxicity risk coefficient of electrolyte leakage caused by vibration, is the influence coefficient of temperature on corrosivity, is the influence coefficient of temperature on flammability and explosiveness, is the influence coefficient of vibration on flammability and explosiveness; The expression for evaluating the recoverability of the electrolyte is: ； Wherein, is the recoverable rate of the electrolyte, is the number of types of recoverable components in the electrolyte, is the th measured value of the mass of the recoverable component, is the mass value of the th recoverable component obtained by technical recovery, is the th weight of the recoverable component.

4. The electrolyte recovery evaluation method for a power lithium battery according to claim 3, characterized in that, The obtaining of the transportation distance of electrolyte recycling according to the recycling process of power lithium battery electrolyte and judging the transportation risk of electrolyte recycling based on the transportation distance of electrolyte recycling specifically includes: According to the status data of used lithium battery electrolyte and battery types, perform packaging and classification on the recycling of used lithium battery electrolyte; By analyzing the characteristics of the recycling process of power lithium battery electrolyte, combined with battery types and electrolyte components, evaluate the compatibility of the recycling process with the target battery, and accordingly establish a hierarchical management strategy; The hierarchical management strategy refers to dividing the electrolyte recycling of different battery types and electrolyte components into the corresponding recycling processes according to the mapping relationship between the compatibility of the recycling process with the target battery; Obtain the location information of the recycling center and distributed edge nodes in each sub-region through GIS and positioning technology; Based on the location information of the recycling center and distributed edge nodes in each sub-region, obtain the actual transportation distance of electrolyte recycling based on GIS road network data; Based on the actual transportation distance of electrolyte recycling and combined with the electrolyte stability evaluation value, judge the transportation risk of electrolyte recycling; The expression of the transportation risk of the electrolyte recycling is: ； In the formula, is the transportation risk of electrolyte recovery, is the number of road sections in the process of electrolyte recovery transportation, is the initial risk setting value based on the electrolyte stability evaluation value, is the distance risk coefficient, is the actual transportation distance of electrolyte recovery, is the risk coefficient of the th road section in the process of electrolyte recovery transportation.

5. The electrolyte recovery evaluation method for a power lithium battery according to claim 4, characterized in that Based on the stability, recoverability, transportation distance, and transportation risk of electrolyte recycling, construct a comprehensive evaluation model for power lithium battery electrolyte recycling based on a neural network. The specific comprehensive evaluation of power lithium battery electrolyte recycling includes: Take the stability, recoverability, transportation distance, transportation risk, and recycling process label of electrolyte recycling as input features. Among them, the transportation distance reflects the transportation cost; Set the comprehensive score of power lithium battery electrolyte recycling as the output label through expert evaluation or historical data annotation; According to the normalization formula, perform normalization processing on the input feature data to eliminate the influence of data dimensions; Set the training set, validation set, and test set respectively according to historical data or test simulation experiments; According to the input features, the input layer uses 5 neurons, the hidden layer uses 2 layers with 64 neurons in each layer, and the output layer uses 1 neuron; The loss function uses the mean squared error, , where is the number of samples, is the predicted value of the comprehensive score; is the target value of the comprehensive score; The activation function is expressed in a piecewise manner, ; Construct a comprehensive evaluation model for power lithium battery electrolyte recycling to comprehensively evaluate the power lithium battery electrolyte recycling.

6. An electronic device, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor. When the instructions are executed by the at least one processor, the at least one processor is enabled to execute an electrolyte recycling evaluation method for a power lithium battery as described in any one of claims 1-5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements an electrolyte recycling evaluation method for a power lithium battery as described in any one of claims 1-5.

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