Waste lithium battery positive electrode material repairing system optimized by adopting machine learning algorithm

Abstract

The invention relates to the technical field of positive electrode material repair, and discloses a waste lithium battery positive electrode material repair system optimized by adopting a machine learning algorithm, which comprises a first repair module, a detection module, a repair strategy module and a second repair module, the first repairing module is used for pre-repairing a to-be-repaired positive electrode material to obtain a first repairing material; the detection module is used for performing state detection on the first repair materials and dividing the first repair materials into different repair groups; the repair strategy module is used for controlling the repair process of the first repair material; the second repair module is used for executing the repair strategy to obtain a second repair material of each repair group; the repair strategy module is also used for carrying out grouping adjustment on the second repair materials of each repair group; through the grouping repairing method, efficient, low-cost and environment-friendly repairing of the waste lithium battery positive electrode material is achieved, and the quality and the reutilization rate of the repaired material are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of positive electrode material repair, and in particular to a waste lithium battery positive electrode material repair system optimized by a machine learning algorithm. Background Art

[0002] At present, most of the methods for repairing the positive electrode materials of waste lithium batteries adopt a unified processing flow, and fail to carry out personalized repairs according to the specific conditions of different materials. Due to the large differences in the initial states of different materials, a unified repair scheme is often difficult to achieve the best results, resulting in uneven performance of the repaired materials. Traditional methods for repairing the positive electrode materials of waste lithium batteries usually require multiple complex processing steps, such as deep cleaning, high-temperature drying, chemical precipitation, etc. These steps are not only time-consuming and labor-intensive, but also require a lot of energy and chemicals, resulting in high overall repair costs. In addition, due to the lack of effective group management, some repair steps may be unnecessary for certain materials, further increasing costs. Existing repair methods often find it difficult to fully restore the original performance of the positive electrode materials of waste lithium batteries, especially in terms of electrochemical performance. This is because during the repair process, the microstructure and surface characteristics of the material may be damaged to varying degrees, and a single repair method is difficult to fully repair these damages, resulting in the performance of the repaired material being inferior to expectations.

[0003] For example, the Chinese patent application with the authorization announcement number CN117594900B discloses a solid phase repair method for waste lithium battery positive electrode materials. In order to improve the powder removal rate of the pole pieces during the solid phase repair of lithium batteries, reduce impurities, and improve the solid phase recovery effect, the invention includes: discharging and disassembling the waste lithium batteries to obtain the positive pole pieces, heat treating them, crushing, peeling, screening, calcining them, adding lithium sources and calcining them again to obtain the recycled positive electrode materials of lithium batteries. The recycling method of the invention is simple to operate, has low equipment requirements, and has good recycling effect. The pole piece powder removal rate is ≥99.5%, and the impurity content is <0.1%. It can recycle the positive electrode materials of waste lithium batteries at low cost, green and effectively, and significantly reduce production costs.

[0004] For example, the patent application with publication number CN117239272A discloses a method for repairing positive electrode materials of lithium batteries. The method includes the following steps: step 1, disassembling the waste lithium battery to obtain the positive electrode powder before repair; step 2, mixing the positive electrode powder before repair with the lithium source, the binder and the dispersant to obtain a slurry; step 3, centrifugally drying the slurry to obtain a powder; step 4, plasma treating the powder to obtain the repaired positive electrode material of the lithium battery. This technical solution is aimed at the recovered failed positive electrode material, and through the supplementation of the lithium source, the crystal structure is regenerated under the action of the plasma jet, and the electrochemical activity of the positive electrode powder material is restored, and the direct repair of the failed positive electrode material can be achieved in a short process.

[0005] The above technical solutions all have the problem raised by the background technology: they fail to perform personalized repair according to the specific conditions of different materials.

[0006] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0007] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a waste lithium battery positive electrode material repair system optimized by a machine learning algorithm. Through a group repair method, efficient, low-cost and environmentally friendly repair of waste lithium battery positive electrode materials can be achieved, thereby improving the quality and reuse rate of the repaired materials.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: The waste lithium battery positive electrode material repair system optimized by machine learning algorithm includes a first repair module, a detection module, a repair strategy module, and a second repair module; wherein: The first repair module is used to pre-repair the positive electrode material to be repaired to obtain a first repair material; The detection module is used to perform state detection on the first repair material, extract state parameters, and classify the first repair material into different repair groups based on the state parameters; The repair strategy module includes a first strategy unit, wherein the first strategy unit is configured with a repair strategy, wherein the repair strategy is used to control the repair process of the first repair material in each repair group; The second repair module is used to execute the repair strategy, and repair the first repair materials in different repair groups respectively to obtain the second repair material of each repair group; The repair strategy module further includes a second strategy unit, wherein the second strategy unit is configured with an adjustment strategy for grouping and adjusting the second repair material of each repair group.

[0009] As a preferred solution of the waste lithium battery positive electrode material repair system optimized by machine learning algorithm described in the present invention, wherein: the pre-repair includes cleaning, drying and grinding; The first repair module includes a first cleaning unit, a first heat treatment unit, and a mechanical treatment unit; wherein the first cleaning unit is used to clean the positive electrode material to be repaired; The first heat treatment unit is used to dry the positive electrode material to be repaired; The mechanical processing unit is used for grinding the positive electrode material to be repaired.

[0010] As a preferred solution of the waste lithium battery positive electrode material repair system optimized by machine learning algorithm described in the present invention, wherein: the repair group includes a first repair group, a second repair group, and a third repair group; The state parameters include defect density, specific surface area, pore volume, key metal content, and total impurity content; The detection module includes a first detection unit, a second detection unit, a third detection unit, and a grouping unit; wherein: The first detection unit is configured with a scanning electron microscope for extracting the defect density; The second detection unit is equipped with a specific surface area measuring device for extracting the specific surface area and pore volume; The third detection unit is equipped with ICP-ES component analysis equipment for extracting key metal content and total impurity content.

[0011] As a preferred solution of the waste lithium battery positive electrode material repair system optimized by machine learning algorithm according to the present invention, wherein: the grouping unit divides the first repair material into different repair groups based on the state parameter, the method is as follows: Each state index of the first restoration material is subjected to a standardization process, wherein the standardization process includes normalization and dimension removal, and the state index of the first restoration material is calculated, and the formula is as follows: ; Wherein, S represents the state index of the first repair material; Indicates the content of the i-th key metal; the value range of i is 1, 2, ..., n, where n is the number of key metal types; represents the weight coefficient of the i-th key metal content, represents the defect density; represents specific surface area; represents the pore volume; Indicates the total impurity content; , , , All are weight coefficients; The grouping unit is configured with a first threshold and a second threshold of the status indicator; the first threshold is less than the second threshold; for any first repair material, if the status indicator is lower than the first threshold, the first repair material is divided into a third repair group; if the status indicator is not lower than the first threshold and lower than the second threshold, the first repair material is divided into the second repair group; if the status indicator is not lower than the second threshold, the first repair material is divided into the first repair group.

[0012] As a preferred solution of the waste lithium battery positive electrode material repair system optimized by machine learning algorithm described in the present invention, wherein: the repair strategy includes a first repair strategy; the first repair strategy is used to control the repair process of the first repair material in the first repair group, which is as follows: Deep cleaning and material purification of the first repair material; Heating, drying and optimizing the crystal structure of the first repair material; performing surface modification treatment on the first repair material; The first repair material is subjected to electrochemical activation treatment.

[0013] As a preferred solution of the waste lithium battery positive electrode material repair system optimized by machine learning algorithm described in the present invention, wherein: the repair strategy also includes a second repair strategy; the second repair strategy is used to control the repair process of the first repair material in the second repair group, which is as follows: The first repair material is routinely cleaned; the first repair material is heated and dried; and the passivation layer of the first repair material is removed.

[0014] The repair strategy also includes a third repair strategy; the third repair strategy is used to control the repair process of the first repair material in the third repair group, specifically as follows: the first repair material is routinely cleaned and heated and dried.

[0015] As a preferred solution of the waste lithium battery positive electrode material repair system optimized by machine learning algorithm described in the present invention, wherein: the second repair module includes a second cleaning unit, a second heat treatment unit, a surface repair unit, and an activation unit; wherein the second cleaning unit is used to perform conventional cleaning, deep cleaning and material purification on the first repair material; the second heat treatment unit is used to heat and dry the first repair material and optimize the crystal structure; the surface repair unit is used to perform surface modification treatment on the first repair material; the activation unit is used to perform electrochemical activation treatment and passivation layer removal treatment on the first repair material.

[0016] As a preferred solution of the waste lithium battery positive electrode material repair system optimized by machine learning algorithm described in the present invention, wherein: the detection module is also used to perform state detection on the second repair material of each repair group, and extract the state parameters of the second repair material in each repair group; wherein the state parameters extracted from the second repair materials of the first repair group and the second repair group include defect density, specific surface area, pore volume, key metal content, and total impurity content; the state parameters extracted from the third repair group include key metal content; The second strategy unit is also respectively configured with a threshold range corresponding to each state parameter of the second repair material in each repair group; the adjustment strategy includes: for the first repair group, if each state parameter of any second repair material is within the corresponding threshold range, no group adjustment is performed; otherwise, the second repair material is divided into the second repair group; For the second repair group, if each state parameter of any second repair material is within the corresponding threshold range, no grouping adjustment is made; otherwise, the second repair material is divided into the third repair group; For the third repair group, if each state parameter of any second repair material is within the corresponding threshold range, no grouping adjustment is made; otherwise, the second repair material is removed from the third repair group.

[0017] As a preferred solution of the waste lithium battery positive electrode material repair system optimized by machine learning algorithm described in the present invention, wherein: the second strategy unit is also configured with a machine learning algorithm for clustering; the adjustment strategy also includes modularizing and packaging the second repair material of the second repair group by using the machine learning algorithm, as follows: S1: standardizing each state parameter of the second restoration material in the second restoration group and forming a state parameter vector; S2: Determine the number K of clusters into which the second restoration material is divided; S3: randomly selecting K state parameter vectors of the second restorative material as cluster centers of the K clusters; S4: Divide each second restorative material into a cluster with the closest state distance; S5: updating the cluster center of each cluster to the mean vector of the state parameter vectors of all the second restoration materials in the cluster; S6: repeating steps S4 to S5 until the change rate of the cluster center of any cluster is less than a preset change rate threshold; The change rate of the cluster center is the Euclidean distance between the current cluster center and the cluster center of the previous iteration; those skilled in the art may set the change rate threshold according to actual needs.

[0018] S7: The second repair materials in each cluster are combined together for modular packaging.

[0019] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention uses a detection module to perform detailed state detection on the positive electrode material of waste lithium batteries, extracts multiple state parameters, and divides the materials into different repair groups based on these parameters. Each repair group adopts a targeted repair strategy to ensure that the repair process is more accurate and effective, thereby significantly improving the performance consistency of the repaired materials. Different repair strategies are adopted for materials in different repair groups. This personalized repair strategy can more effectively restore the electrochemical properties of the materials and improve the overall quality of the repaired materials.

[0020] Through the group repair method, the most appropriate repair process is selected according to the actual state of different materials, avoiding unnecessary processing steps and reducing the repair cost. This flexible repair strategy greatly improves resource utilization and reduces the overall repair cost. The repaired positive electrode material can be applied to different scenarios according to its quality and performance.

[0021] The second repair materials of the second repair group are modularly packaged through machine learning algorithms, so that the performance of each group of modularly packaged second repair materials is similar and can be used in the production of the same energy storage device. Automatically matching the optimal positive electrode material for modular packaging combination can minimize the difference in the performance of the positive electrode materials within the energy storage device package and maintain the consistency and stability of the overall performance of the energy storage device. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0023] Figure 1 A schematic diagram of the structure of a waste lithium battery positive electrode material repair system optimized by a machine learning algorithm provided by the present invention;

[0024] Figure 2 A schematic diagram of the working principle of the waste lithium battery positive electrode material repair system optimized by a machine learning algorithm provided by the present invention;

[0025] Figure 3 A flow chart of a machine learning algorithm for modularly packaging a second restorative material provided in the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0027] This embodiment introduces a waste lithium battery positive electrode material repair system optimized by machine learning algorithm. Figure 1 The system includes a first repair module, a detection module, a repair strategy module, and a second repair module; the working principle of each module is as follows Figure 2 shown.

[0028] The first repair module is used to pre-repair the positive electrode material to be repaired to obtain a first repair material; The pre-repair includes cleaning, drying and grinding; the pre-repair is mainly to remove pollutants and impurities on the surface of the positive electrode material to be repaired, in preparation for subsequent status detection and further repair.

[0029] The first repair module includes a first cleaning unit, a first heat treatment unit, and a mechanical treatment unit; wherein the first cleaning unit is used to clean the positive electrode material to be repaired; the first cleaning unit uses deionized water, an organic solvent (such as ethanol) or a weak acid solution (such as diluted hydrochloric acid) to perform ultrasonic cleaning to remove electrolyte residues and other contaminants attached to the surface of the positive electrode material to be repaired.

[0030] The first heat treatment unit is used to dry the positive electrode material to be repaired; the first heat treatment unit dries or roasts the positive electrode material to be repaired at a certain temperature to remove moisture and volatile substances.

[0031] The mechanical processing unit is used to grind the positive electrode material to be repaired. The mechanical processing unit removes large particle impurities through mechanical means such as screening and grinding, and ensures that the particle size distribution of the positive electrode material to be repaired is uniform.

[0032] The detection module is used to perform state detection on the first repair material, extract state parameters, and classify the first repair material into different repair groups based on the state parameters; The repair group includes a first repair group, a second repair group, and a third repair group; The state parameters include defect density, specific surface area, pore volume, key metal content, and total impurity content; After the pre-repair is completed, the first repair material needs to be tested for its status. The above status parameters can be used to evaluate the repair difficulty and potential recycling value of the first repair material.

[0033] The detection module includes a first detection unit, a second detection unit, a third detection unit, and a grouping unit; wherein: The first detection unit is equipped with a scanning electron microscope for extracting the defect density; the scanning electron microscope can observe the material morphology, analyze the surface element composition and distribution, observe and record surface morphological features, such as cracks, holes, etc., and calculate the density of defects as the defect density.

[0034] The second detection unit is equipped with a specific surface area measuring device for extracting the specific surface area and pore volume; the second detection unit can accurately measure the specific surface area and pore volume of the first repair material through the BET method. These two state parameters reflect the pore structure characteristics of the material and are important indicators for measuring the activity of the material.

[0035] The third detection unit is equipped with ICP-ES component analysis equipment for extracting key metal content and total impurity content; wherein the key metal content includes the percentage content of lithium, cobalt, nickel, manganese, etc.; the total impurity content includes the total percentage content of possible impurity elements such as iron, copper, aluminum, etc.

[0036] The grouping unit divides the first repair material into different repair groups based on the state parameter in the following manner: Each state index of the first restoration material is subjected to a standardization process, wherein the standardization process includes normalization and dimension removal, and the state index of the first restoration material is calculated, and the formula is as follows: ; Wherein, S represents the state index of the first repair material; represents the i-th key metal content; The weight coefficient representing the content of the i-th key metal is set by technicians in this field based on actual needs; the value range of i is 1, 2, ..., n, where n is the number of key metals; represents the defect density; represents specific surface area; represents the pore volume; Indicates the total impurity content; , , , They are all weight coefficients, which are set by technicians in this field based on actual needs.

[0037] The grouping unit is configured with a first threshold and a second threshold of the status indicator; the first threshold is less than the second threshold; for any first repair material, if the status indicator is lower than the first threshold, the first repair material is divided into a third repair group; if the status indicator is not lower than the first threshold and lower than the second threshold, the first repair material is divided into the second repair group; if the status indicator is not lower than the second threshold, the first repair material is divided into the first repair group.

[0038] The repair strategy module includes a first strategy unit, wherein the first strategy unit is configured with a repair strategy, wherein the repair strategy is used to control the repair process of the first repair material in each repair group; The repair strategy includes a first repair strategy; the first repair strategy is used to control the repair process of the first repair material in the first repair group, specifically as follows: The first repair material is deeply cleaned and purified; a high-concentration cleaning solvent is used for multiple cleanings, and a chemical precipitation method is used to remove trace impurities on the surface of the first repair material.

[0039] The first repair material is heated, dried and its crystal structure is optimized; the first repair material is annealed by precisely controlling the temperature curve to restore or optimize its crystal structure.

[0040] The first repair material is subjected to surface modification treatment; coating technology or doping treatment is used to improve the interface properties and electrochemical stability of the first repair material.

[0041] The first repair material is subjected to an electrochemical activation treatment; the first repair material is activated by charge / discharge cycles under specific conditions to increase its initial capacity.

[0042] The repair strategy also includes a second repair strategy; the second repair strategy is used to control the repair process of the first repair material in the second repair group, as follows: Performing routine cleaning on the first repair material; compared with deep cleaning, routine cleaning reduces the cleaning solvent concentration and the number of cleaning times; The first repair material is heated and dried; the purpose is to dry the surface of the cleaned first repair material.

[0043] The first repair material is subjected to a passivation layer removal treatment; the passivation layer on the surface of the first repair material is removed by performing a limited number of charge and discharge cycles to improve its electrochemical performance.

[0044] The repair strategy also includes a third repair strategy; the third repair strategy is used to control the repair process of the first repair material in the third repair group, specifically as follows: the first repair material is routinely cleaned and heated and dried.

[0045] Through such detailed grouping and targeted repair process design, the recycling rate of waste lithium battery positive electrode materials can be maximized while saving repair costs and ensuring that the repaired materials can meet the requirements of different application scenarios.

[0046] The second repair module is used to execute the repair strategy, and repair the first repair materials in different repair groups respectively to obtain the second repair material of each repair group; The second repair module includes a second cleaning unit, a second heat treatment unit, a surface repair unit, and an activation unit; wherein the second cleaning unit is used to perform conventional cleaning, deep cleaning, and material purification on the first repair material; the second heat treatment unit is used to heat and dry the first repair material and optimize the crystal structure; the surface repair unit is used to perform surface modification treatment on the first repair material; and the activation unit is used to perform electrochemical activation treatment and passivation layer removal treatment on the first repair material.

[0047] The repaired positive electrode materials can be applied to different scenarios according to their quality and performance. Specifically: The positive electrode materials in the first repair group are reused in the manufacture of lithium batteries. The positive electrode materials in the first repair group are of good quality and can be directly used in the manufacture of new lithium batteries, especially in electronic products and electric vehicle batteries with high performance requirements. The performance of the positive electrode materials in the second repair group has slightly decreased but can still be used in energy storage batteries. They can be used in stationary energy storage systems, such as home energy storage units or grid energy storage facilities. The positive electrode materials in the third repair group are difficult to repair and have low potential for reuse. They are not suitable for direct use in lithium battery production, but the metal components therein still have recycling value and can be supplied as raw materials to other industries, such as alloy manufacturing, chemical products, etc.

[0048] The repair strategy module further includes a second strategy unit, wherein the second strategy unit is configured with an adjustment strategy for grouping and adjusting the second repair material of each repair group.

[0049] The detection module is also used to perform state detection on the second repair material of each repair group and extract the state parameters of the second repair material in each repair group; wherein the state parameters extracted from the second repair materials of the first repair group and the second repair group include defect density, specific surface area, pore volume, key metal content, and total impurity content; the state parameters extracted from the third repair group include key metal content; The second strategy unit is also configured with the threshold range corresponding to each state parameter of the second repair material in each repair group; for example: for the first repair group, the threshold range corresponding to each state parameter includes: Defect density: <100 / μm²; the materials of the first repair group need to have as few defects as possible to ensure good electrochemical performance and cycle stability. Specific surface area: 5-20m² / g; the appropriate specific surface area helps to increase the diffusion rate of lithium ions, but too high a specific surface area may lead to increased side reactions. Pore volume: 0.2-0.5cm³ / g; the appropriate pore volume helps the insertion and deinsertion of lithium ions while maintaining good structural stability. Key metal content: lithium: ≥5wt%; cobalt: ≥15wt%; nickel: ≥10wt%; manganese: ≥10wt%; these contents meet the standard specifications for new battery production, ensuring that the material has good electrochemical performance. Total impurity content: <0.1wt%; high purity is the key to high-quality materials, and low impurity content helps to improve the stability and electrochemical performance of materials.

[0050] For the second repair group, the threshold ranges corresponding to each state parameter include: defect density: <300 / μm²; specific surface area: 10-30m² / g; pore volume: 0.3-0.8cm³ / g; key metal content: lithium: ≥4wt%; cobalt ≥10wt%; nickel: ≥8wt%; manganese: ≥8wt%; total impurity content: <0.2wt%; For the third repair group, the threshold range corresponding to each state parameter includes: lithium content ≥5wt%, cobalt content ≥2wt%, nickel content ≥2wt%. Since the third repair group is subsequently used for metal recovery, it is only necessary to confirm whether the metal content meets the recovery standard.

[0051] The adjustment strategy includes: for the first repair group, if each state parameter of any second repair material is within the corresponding threshold range, no grouping adjustment is performed; otherwise, the second repair material is divided into the second repair group; For the second repair group, if each state parameter of any second repair material is within the corresponding threshold range, no grouping adjustment is made; otherwise, the second repair material is divided into the third repair group; For the third repair group, if each state parameter of any second repair material is within the corresponding threshold range, no grouping adjustment is made; otherwise, the second repair material is removed from the third repair group; the second repair material removed from the third repair group has too low a metal content and has no recycling value.

[0052] The second strategy unit is also configured with a machine learning algorithm for clustering; the adjustment strategy also includes modularizing and packaging the second repair material of the second repair group using the machine learning algorithm; referring to Figure 3 , as follows: S1: standardizing each state parameter of the second restoration material in the second restoration group and forming a state parameter vector; S2: Determine the number K of clusters into which the second restoration material is divided; determine the appropriate number of clusters based on modular packaging requirements and expert experience.

[0053] S3: randomly selecting K state parameter vectors of the second restorative material as cluster centers of the K clusters; S4: Divide each second restorative material into a cluster with the closest state distance; The state distance is the Euclidean distance between the state parameter vector of any second restoration material and the cluster center of any cluster; S5: updating the cluster center of each cluster to the mean vector of the state parameter vectors of all the second restoration materials in the cluster; S6: repeating steps S4 to S5 until the change rate of the cluster center of any cluster is less than a preset change rate threshold; The change rate of the cluster center is the Euclidean distance between the current cluster center and the cluster center of the previous iteration; those skilled in the art may set the change rate threshold according to actual needs.

[0054] S7: The second repair materials in each cluster are combined together for modular packaging.

[0055] Through the above steps, the second repair materials of the second repair group are modularly packaged, so that the performance of each group of modularly packaged second repair materials is similar and can be used for the production of the same energy storage device; automatically matching the optimal positive electrode material for modular packaging combination can minimize the difference in the performance of the positive electrode materials inside the energy storage device package and maintain the consistency and stability of the overall performance of the energy storage device.

[0056] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the purpose and scope of protection of the present invention, which are all within the protection of the present invention.

Claims

1. A waste lithium battery positive electrode material repair system optimized by machine learning algorithm, characterized by: It includes a first repair module, a detection module, a repair strategy module, and a second repair module; wherein: The first repair module is used to pre-repair the positive electrode material to be repaired to obtain a first repair material; The detection module is used to perform state detection on the first repair material, extract state parameters, and classify the first repair material into different repair groups based on the state parameters; The repair strategy module includes a first strategy unit, wherein the first strategy unit is configured with a repair strategy, wherein the repair strategy is used to control the repair process of the first repair material in each repair group; The second repair module is used to execute the repair strategy, and repair the first repair materials in different repair groups respectively to obtain the second repair material of each repair group; The repair strategy module further includes a second strategy unit, wherein the second strategy unit is configured with an adjustment strategy for grouping and adjusting the second repair material of each repair group.

2. The waste lithium battery positive electrode material repair system optimized by machine learning algorithm as claimed in claim 1, characterized in that: The pre-repairing includes cleaning, drying and grinding; The first repair module includes a first cleaning unit, a first heat treatment unit, and a mechanical treatment unit; wherein the first cleaning unit is used to clean the positive electrode material to be repaired; The first heat treatment unit is used to dry the positive electrode material to be repaired; The mechanical processing unit is used for grinding the positive electrode material to be repaired.

3. The waste lithium battery positive electrode material repair system optimized by machine learning algorithm as claimed in claim 1, characterized in that: The repair group includes a first repair group, a second repair group, and a third repair group; The state parameters include defect density, specific surface area, pore volume, key metal content, and total impurity content; The detection module includes a first detection unit, a second detection unit, a third detection unit, and a grouping unit; wherein: The first detection unit is configured with a scanning electron microscope for extracting the defect density; The second detection unit is equipped with a specific surface area measuring device for extracting the specific surface area and pore volume; The third detection unit is equipped with ICP-ES component analysis equipment for extracting key metal content and total impurity content.

4. The waste lithium battery positive electrode material repair system optimized by machine learning algorithm as claimed in claim 3, characterized in that: The grouping unit divides the first repair material into different repair groups based on the state parameter in the following manner: Performing standardization processing on each state index of the first repair material, wherein the standardization processing includes normalization and dimension removal, and calculating the state index of the first repair material; the state index of the first repair material is obtained by weighted summation of each key metal content, defect density, specific surface area, pore volume, and total impurity content; The grouping unit is configured with a first threshold and a second threshold of a state indicator; the first threshold is less than the second threshold; for any first repair material, if the state indicator is lower than the first threshold, the first repair material is classified into a third repair group; If the state indicator is not lower than the first threshold and lower than the second threshold, classifying the first repair material into the second repair group; If the state indicator is not lower than the second threshold, the first repair material is classified into the first repair group.

5. The waste lithium battery positive electrode material repair system optimized by machine learning algorithm as claimed in claim 4, characterized in that: The repair strategy includes a first repair strategy; the first repair strategy is used to control the repair process of the first repair material in the first repair group, specifically as follows: Deep cleaning and material purification of the first repair material; Heating, drying and optimizing the crystal structure of the first repair material; performing surface modification treatment on the first repair material; The first repair material is subjected to electrochemical activation treatment.

6. The waste lithium battery positive electrode material repair system optimized by machine learning algorithm as claimed in claim 5, characterized in that: The repair strategy also includes a second repair strategy; the second repair strategy is used to control the repair process of the first repair material in the second repair group, as follows: Performing routine cleaning on the first repair material; heating and drying the first repair material; and removing the passivation layer of the first repair material; The repair strategy also includes a third repair strategy; the third repair strategy is used to control the repair process of the first repair material in the third repair group, specifically as follows: the first repair material is routinely cleaned and heated and dried.

7. The waste lithium battery positive electrode material repair system optimized by machine learning algorithm as claimed in claim 1, characterized in that: The second repair module includes a second cleaning unit, a second heat treatment unit, a surface repair unit, and an activation unit; wherein the second cleaning unit is used to perform conventional cleaning, deep cleaning, and material purification on the first repair material; the second heat treatment unit is used to heat and dry the first repair material and optimize the crystal structure; the surface repair unit is used to perform surface modification treatment on the first repair material; and the activation unit is used to perform electrochemical activation treatment and passivation layer removal treatment on the first repair material.

8. The waste lithium battery positive electrode material repair system optimized by machine learning algorithm as claimed in claim 6, characterized in that: The detection module is also used to perform state detection on the second repair material of each repair group, and extract the state parameters of the second repair material in each repair group; wherein the state parameters extracted from the second repair materials of the first repair group and the second repair group include defect density, specific surface area, pore volume, key metal content, and total impurity content; the state parameters extracted from the third repair group include key metal content; The second strategy unit is also respectively configured with a threshold range corresponding to each state parameter of the second repair material in each repair group; the adjustment strategy includes: for the first repair group, if each state parameter of any second repair material is within the corresponding threshold range, no group adjustment is performed; otherwise, the second repair material is divided into the second repair group; For the second repair group, if each state parameter of any second repair material is within the corresponding threshold range, no grouping adjustment is made; otherwise, the second repair material is divided into the third repair group; For the third repair group, if each state parameter of any second repair material is within the corresponding threshold range, no grouping adjustment is made; otherwise, the second repair material is removed from the third repair group.

9. The waste lithium battery positive electrode material repair system optimized by machine learning algorithm as claimed in claim 8, characterized in that: The second strategy unit is also configured with a machine learning algorithm for clustering; the adjustment strategy also includes modularizing and packaging the second repair materials of the second repair group using the machine learning algorithm, as follows: S1: standardizing each state parameter of the second restoration material in the second restoration group and forming a state parameter vector; S2: Determine the number K of clusters into which the second restoration material is divided; S3: randomly selecting K state parameter vectors of the second restorative material as cluster centers of the K clusters; S4: Divide each second restorative material into a cluster with the closest state distance; S5: updating the cluster center of each cluster to the mean vector of the state parameter vectors of all the second restoration materials in the cluster; S6: Repeat steps S4 to S5 until the change rate of the cluster center of any cluster cluster is less than a preset change rate threshold; the change rate of the cluster center is the Euclidean distance between the current cluster center and the cluster center of the previous iteration; S7: The second repair materials in each cluster are combined together for modular packaging.

Images