Efficient crushing and sorting automobile metal recovery method and system

Abstract

The invention provides an efficient crushing and sorting automobile metal recovery method and system, and relates to the technical field of automobile sorting, and the efficient crushing and sorting automobile metal recovery method comprises the steps that aiming at automobile metal recovery, an optical recognition system is used for determining optical sorting characteristics and crushing characteristics corresponding to alloy series categories; in combination with the characteristics, an integrated management module is constructed through a decision tree, and the integrated management module comprises an optical sorting unit and a crushing management unit; determining an initialization recovery strategy; and through process phase change and production line configuration characteristics, sorting verification compensation and parameter control conversion of the strategy are carried out so as to optimize the pre-recovery strategy. And finally, controlling metal recovery. By means of the method and device, the problems that in the prior art, due to the lack of an accurate recognition technology, the sorting efficiency is low in the metal recycling process, environment pollution is serious, and the resource recycling efficiency and sustainability are further affected can be solved. And different production environments and requirements can be met during recycling, the automobile metal recycling efficiency and purity are improved, and meanwhile environmental pollution is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile sorting, and particularly relates to a high-efficiency crushing and sorting automobile metal recycling method and system. BACKGROUND

[0002] With the increasing environmental protection index requirements of the automobile industry on the whole life cycle of raw materials, production and recycling process, improper crushing and sorting may cause secondary pollution.

[0003] At present, the existing automobile metal recycling technology mainly relies on traditional physical separation methods such as manual sorting, magnetic separation, air flow sorting and the like. These methods are often low in efficiency and low in sorting precision. At the same time, due to the lack of effective pretreatment and sorting feature recognition, the secondary pollution problem caused in the metal recycling process is serious, and the resource recycling rate is low.

[0004] In summary, due to the lack of accurate identification technology, the existing technology causes low sorting efficiency and serious environmental pollution in the metal recycling process, which further affects the efficiency and sustainability of resource recycling. SUMMARY

[0005] The purpose of the present application is to provide a high-efficiency crushing and sorting automobile metal recycling method and system, so as to solve the problem that the existing technology causes low sorting efficiency and serious environmental pollution in the metal recycling process due to the lack of accurate identification technology, which further affects the efficiency and sustainability of resource recycling.

[0006] In view of the above problems, the present application provides a high-efficiency crushing and sorting automobile metal recycling method and system.

[0007] In a first aspect, the application provides a high-efficiency crushing and sorting automobile metal recycling method, which is realized by a high-efficiency crushing and sorting automobile metal recycling system. The method comprises the following steps: determining optical sorting features and crushing features based on alloy series categories for automobile metals, wherein the features exist in categories and are distinctive; training an integrated management module by constructing a decision tree based on the optical sorting features and the crushing features, wherein the integrated management module comprises an optical sorting unit and a crushing management unit that interact with each other, the optical sorting unit is based on an optical recognition system, and the crushing management unit is based on a crusher system; identifying pre-recycled metals, performing compound sorting and coupled recycling analysis based on the integrated management module and a pre-sorting-crushing-sorting benchmark, and determining an initial recycling strategy, wherein the sorting comprises physical sorting and chemical sorting, and the initial recycling strategy is a state strategy; performing sorting verification and compensation on the initial recycling strategy based on process phase changes, performing parameter control conversion based on production line configuration characteristics, and determining a pre-recycling strategy; and performing metal recycling control based on the pre-recycling strategy.

[0008] In a second aspect, the application also provides a high-efficiency crushing and sorting automobile metal recycling system for performing the high-efficiency crushing and sorting automobile metal recycling method of the first aspect. The system comprises: a feature determination unit configured to determine optical sorting features and crushing features based on alloy series categories for automobile metals, wherein the features exist in categories and are distinctive; a management module construction unit configured to train an integrated management module by constructing a decision tree based on the optical sorting features and the crushing features, wherein the integrated management module comprises an optical sorting unit and a crushing management unit that interact with each other, the optical sorting unit is based on an optical recognition system, and the crushing management unit is based on a crusher system; a recycling analysis unit configured to identify pre-recycled metals, perform compound sorting and coupled recycling analysis based on the integrated management module and a pre-sorting-crushing-sorting benchmark, and determine an initial recycling strategy, wherein the sorting comprises physical sorting and chemical sorting, and the initial recycling strategy is a state strategy; a recycling strategy determination unit configured to perform sorting verification and compensation on the initial recycling strategy based on process phase changes, perform parameter control conversion based on production line configuration characteristics, and determine a pre-recycling strategy; and a recycling control unit configured to perform metal recycling control based on the pre-recycling strategy.

[0009] The one or more technical solutions provided in the application have at least the following technical effects or advantages: Determine the optical sorting features and crushing features by targeting the automobile metal based on the alloy series class, wherein the feature exists class corresponds to a distinguishing feature; by constructing a decision tree, training an integrated management module combining the optical sorting features and crushing features, the integrated management module includes a bidirectional interactive optical sorting unit and a crushing management unit, the optical sorting unit takes the optical identification system as the construction baseline, and the crushing management unit takes the crusher system as the construction baseline; identify the pre-recycled metal, combine the integrated management module, and perform composite sorting and coupled recycling analysis based on the pre-sorting-crushing-sorting as the baseline to determine the initialization recycling strategy, wherein the sorting includes physical sorting and chemical sorting, and the initialization recycling strategy is a state strategy; based on the process phase change, the initialization recycling strategy is sorted, compensated, combined with the production line configuration characteristics, and the parameter control conversion is performed to determine the pre-recycling strategy; based on the pre-recycling strategy, the metal recycling control of the pre-recycled metal is performed, which effectively solves the problem that the existing technology lacks accurate identification technology, resulting in low sorting efficiency and serious environmental pollution in the metal recycling process, which further affects the efficiency and sustainability of resource recycling. Make the recycling process adapt to different production environments and needs, improve the efficiency and purity of automobile metal recycling, and reduce environmental pollution.

[0010] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and those skilled in the art can obtain other drawings without creative labor on the basis of the provided drawings.

[0012] Figure 1 Flowchart of the automobile metal recycling method of the present application; Figure 2 Structure diagram of the automobile metal recycling system of the present application.

[0013] Explanation of reference signs: The feature determination unit 11, the module construction unit 12, the recycling analysis unit 13, the recycling strategy determination unit 14, and the recycling management unit 15. DETAILED DESCRIPTION

[0014] The present application provides a high-efficiency crushing and sorting automobile metal recycling method and system, which solves the problem of low sorting efficiency and serious environmental pollution in the metal recycling process due to the lack of accurate identification technology in the prior art, further affecting the efficiency and sustainability of resource recycling. The present application can adapt to different production environments and demands during recycling, improve the efficiency and purity of automobile metal recycling, and reduce environmental pollution.

[0015] The technical solutions in the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for convenience of description, only parts related to the present application are shown in the drawings, rather than all parts.

[0016] Embodiment one, please refer to the accompanying Figure 1 The present application provides a high-efficiency crushing and sorting automobile metal recycling method, wherein the high-efficiency crushing and sorting automobile metal recycling method is applied to a high-efficiency crushing and sorting automobile metal recycling system, and the high-efficiency crushing and sorting automobile metal recycling method specifically includes the following steps: S1: For automobile metal, based on the alloy series category, determine the optical sorting features and the crushing features, wherein the features exist category corresponding, and are distinguishing features.

[0017] Specifically, different types of metal samples are collected from scrap cars, including various alloy series such as steel, copper, aluminum, stainless steel, etc. Spectral analysis techniques such as X-ray fluorescence XRF analysis are used to determine the chemical composition of each metal. Based on the spectral analysis results, the features of each metal are extracted, such as specific spectral peaks, wavelengths, etc., which will serve as the basis for optical sorting. Analyze the physical properties of different metals, such as hardness, toughness, density, etc., which will affect the crushing process. Perform crushing tests to observe the behavior of different metals during the crushing process, such as fragment size, shape, etc. Based on the test results, the crushing features of each metal are extracted, such as the optimal crushing speed, crushing force, etc.

[0018] S2: In combination with the optical sorting features and the crushing features, a decision tree is constructed to train an integrated management module, which includes a bidirectional interactive optical sorting unit and a crushing management unit. The optical sorting unit is based on an optical recognition system, and the crushing management unit is based on a crusher system.

[0019] Specifically, a large amount of metal sample data is collected, including various alloy types, and their optical sorting features and crushing features are recorded. From the collected data, the most effective features for classification are selected, which will be used for the construction of the decision tree. A suitable algorithm is selected to construct the decision tree, such as C4.5, ID3, CART, etc. The selected algorithm and feature data are used to construct the decision tree. Each internal node represents a feature test, each branch represents a test result, and each leaf node represents a metal category. From the collected data, a training set and a test set are divided to ensure that the training set can fully represent different metal categories and features. The training set is used to train the decision tree model, and the model parameters are adjusted until the model can accurately classify metals. The test set is used to verify the accuracy of the model to ensure that the model has good generalization ability. The optical sorting unit is based on an optical recognition system, which uses the decision tree model to preliminarily sort metals. The optical recognition system can be an optical sensor based on spectral, color, shape, etc. The crushing management unit is based on a crusher system, which adjusts the working parameters of the crusher, such as speed, feed rate, etc., according to the crushing feature parameters provided by the decision tree model to optimize the crushing process. The output of the optical sorting unit can be fed back to the crushing management unit to adjust the crushing parameters; similarly, the output of the crushing management unit can also be fed back to the optical sorting unit to improve the sorting process.

[0020] S3: Identify pre-recycled metals, combine the integrated management module, and perform composite sorting and coupled recycling analysis based on the pre-sorting-crushing-sorting benchmark to determine the initial recycling strategy, where sorting includes physical sorting and chemical sorting, and the initial recycling strategy is a state strategy.

[0021] Specifically, the automotive metal scrap is initially sorted using an optical sorting unit in the integrated management module. Easily identifiable and separable metal parts in the bulk metal scrap are first sorted out. The pre-sorted metal scrap is sent to the crushing management unit for processing. According to the crushing characteristics of the metal, the working parameters of the crusher, such as crushing force, speed, etc., are adjusted to achieve more effective crushing. The crushed metal fragments are again finely sorted by the optical sorting unit. At this time, the size and shape of the metal fragments are more suitable for accurate optical identification and separation. By taking advantage of physical property differences such as density, magnetism, electrical conductivity, etc., further separation of metal fragments is achieved through physical sorting equipment such as air flow sorting, magnetic separation, vibrating screen, etc. For metals with similar physical properties, chemical methods such as leaching, electrolysis, solvent extraction, etc. are used to achieve high-purity metal recovery. Throughout the sorting process, the results of different sorting steps are combined for comprehensive analysis to optimize the recovery strategy. This includes adjusting sorting parameters, changing process flow or introducing new sorting technologies. Based on the results of composite sorting and coupled recovery analysis, the initial recovery strategy is determined. These strategies will guide the actual recovery operation, including sorting order, crushing parameters, optical identification system settings, etc. During the recovery process, based on real-time data feedback, the sorting strategy is dynamically adjusted to adapt to the changing material flow and recovery efficiency.

[0022] S4: Sorting verification compensation is performed on the initial recovery strategy based on process phase change, parameter control conversion is performed combined with production line configuration characteristics, and the pre-recovery strategy is determined.

[0023] Specifically, during the metal recovery process, the metal may undergo phase changes, such as the transition from solid to liquid. This phase change may affect the physical and chemical properties of the metal, thereby affecting the sorting effect. Based on the results of process phase change analysis, the initial recovery strategy is verified and compensated. For example, if it is found that a certain metal is prone to phase change during crushing, the crushing parameters are adjusted or additional cooling measures are introduced. A comprehensive evaluation of the existing metal recovery production line is conducted, including equipment performance, process flow, production capacity, etc. According to the production line configuration characteristics, parameter control conversion is performed. This includes adjusting equipment layout, introducing new sorting equipment, improving control systems, etc. to optimize the entire recovery process. The sorting verification compensation measures are combined with the production line configuration characteristics for integration and optimization. Based on the results of sorting verification compensation and production line configuration characteristic analysis, comprehensive analysis is performed to determine the final pre-recovery strategy.

[0024] S5: Metal recovery control is performed on the pre-recovered metal based on the pre-recovery strategy.

[0025] Specifically, based on the pre-recycling strategy, detailed operating procedures are developed, including specific steps for each stage such as pretreatment, crushing, and sorting. The operating procedures must comply with safety regulations, including safe equipment operation, the use of personal protective equipment, and emergency response. Sensors and monitoring equipment are installed during the recycling process to collect data in real time, such as output, purity, energy consumption, and equipment status. Regular product quality inspections are conducted to ensure that the recycled metals meet the prescribed purity and quality standards. The environmental impact of the recycling process, such as waste gas, wastewater, and noise, is monitored to ensure compliance with environmental protection requirements.

[0026] Furthermore, step S2 of this application also includes: Based on the optical sorting features, for the first genus, a first series of features are determined as sorting features, a first decision node is constructed, the construction of the Nth decision node and node association are completed, and a decision tree is generated; based on the decision tree, a first decision layer is constructed, the first decision layer corresponding to the first genus; the optical sorting features are traversed, the construction of the Nth decision layer for the Nth genus is completed, and a first decision tree is generated; based on the first decision tree, the optical sorting unit is trained under supervision.

[0027] Specifically, for the first genus, such as stainless steel, a series of features are identified as sorting features based on optical sorting characteristics. These features include specific spectral absorption peaks, reflectivity, color, etc. The first decision node is constructed using these sorting features. A decision node is the basic unit in a decision tree; it makes a classification decision based on one or more features. The construction of the Nth decision node is completed, and these nodes are interconnected to form a layer of the decision tree. This layer of the decision tree can classify the first genus. Based on this layer of the decision tree, the first decision layer is constructed. The decision layer consists of multiple decision nodes, each representing a test of a feature. For the Nth genus, such as copper or aluminum, the above process is repeated to complete the construction of the Nth decision layer, ultimately generating the first decision tree. A large amount of metal sample data, including various genera, is collected, and their optical sorting characteristics are recorded. The collected data is used to train the decision tree. This process includes selecting the optimal features, constructing decision nodes, and determining node associations. The accuracy of the decision tree is validated using an independent test dataset. If the performance is unsatisfactory, the feature selection or decision tree construction method may need to be adjusted. The trained decision tree is then applied to the optical sorting unit. The optical sorting unit will use a decision tree to identify and classify metals. In practice, the performance of the optical sorting unit is monitored. If classification errors are detected, the parameters of the decision tree or the optical sorting unit need to be adjusted.

[0028] Furthermore, step S3 of this application also includes: By performing optical scanning, a pre-sorting strategy is determined based on the optical sorting unit. The pre-sorting strategy is a genus-based cutting strategy. Combining the pre-sorting strategy, a crushing strategy is determined based on the particle size and efficiency crushing decision made by the crushing management unit. Based on the crushing strategy, a sorting strategy is determined based on the optical sorting unit. The initialization recycling strategy is determined by integrating the pre-sorting strategy, the crushing strategy, and the sorting strategy.

[0029] Specifically, an optical sorting unit scans the metal scrap to identify different metal species. Based on the optical scanning results, a pre-sorting strategy is determined. This strategy is based on the metal species for cutting. Combined with the pre-sorting strategy, a crushing management unit makes crushing decisions regarding particle size and efficiency. This includes determining optimal crushing force, speed, and other parameters to achieve efficient crushing. Based on the crushing decisions, a crushing strategy is determined. Combined with the crushing strategy, an optical sorting unit makes sorting decisions. This includes determining sorting parameters, such as the settings of the optical recognition system and sorting thresholds. Based on the sorting decisions, a sorting strategy is determined. This strategy aims to improve the purity and recovery rate of the sorted metals. The pre-sorting strategy, crushing strategy, and sorting strategy are integrated to form a complete initialization recovery strategy. The initialization recovery strategy guides the entire metal recycling process, including pretreatment, crushing, and sorting stages.

[0030] Furthermore, this application also includes: Identify the pre-sorting strategy and determine the parallel processing volume, wherein the parallel processing volume corresponds to the strategy category; based on the parallel processing volume, temporarily divide the crushing management unit and the optical sorting unit into multiple parallel processing blocks, wherein the division criteria include processing domain division and functional segmentation; complete the analysis of the initialization recycling strategy and restore the multiple temporarily defined parallel processing blocks.

[0031] Specifically, based on the pre-sorting strategy, the categories of metal scrap are identified, which determines the demand and volume of parallel processing. The parallel processing volume corresponding to each category is determined to ensure that different types of metal scrap can be processed simultaneously during the recycling process. Based on the parallel processing volume, the crushing management unit and optical sorting unit are temporarily divided into zones to facilitate the simultaneous processing of different types of metal scrap. These zones are based on the division of processing domains and functional segmentation, ensuring that each block can effectively complete its assigned tasks. Within the designated blocks, the crushing and sorting processes of the metal scrap are completed, ensuring that each block operates according to the established strategy. Through this process, the initial recycling strategy can be analyzed and optimized to improve the efficiency and effectiveness of the entire recycling process.

[0032] Furthermore, step S4 of this application also includes: The alloy series categories are traversed, and phase transformation characteristics are extracted based on process phase transformation. Based on the phase transformation characteristics, the initialization recycling strategy is sorted and verified to identify phase transformation error points. Based on the phase transformation error points, the initialization recycling strategy is compensated and corrected.

[0033] Specifically, the process involves traversing different alloy series and analyzing the potential phase transitions they may undergo during recycling. Based on these phase transitions, characteristics of each series during the phase transition process are identified, such as temperature changes, morphological changes, and density changes. These identified phase transition characteristics are then used to validate the initial recycling strategy. During validation, potential sorting error points caused by phase transitions are identified. These error points may be due to changes in metal properties caused by the phase transition, preventing the sorting equipment from accurately identifying and separating the metals. Based on these identified phase transition error points, the initial recycling strategy is compensated and corrected. This includes adjusting sorting parameters, changing the crushing strategy, and introducing additional pretreatment steps. Through this compensation and correction, the recycling process is optimized, ensuring high-efficiency and high-purity metal recovery under different phase transition conditions.

[0034] Furthermore, this application also includes: The equipment and system configurations of the interactive metal recycling production line are integrated with the production line configuration characteristics. Based on the production line configuration characteristics, the parameter control conversion relationship is determined, wherein the parameter control conversion relationship is the relative conversion relationship between the state of each strategy and the production line parameters under a unit step size. In combination with the parameter control conversion relationship, the initialization recycling strategy is converted into production line parameter control to determine the pre-recycling strategy.

[0035] Specifically, this involves analyzing the equipment configuration of the metal recycling production line, such as crushers, sorting equipment, conveyors, and system configurations, including control systems, monitoring systems, and data processing systems. The equipment and system configuration information is integrated to form a comprehensive description of the production line's configuration characteristics, including the line layout, equipment capacity, and system functions. Parameter-control conversion relationships refer to the relative conversion relationships between various strategy states and production line parameters at a unit step size. For example, how should the crusher speed be adjusted when the sorting strategy switches from mode A to mode B? These parameter-control conversion relationships are determined based on the production line configuration characteristics. Using these determined relationships, the initial recycling strategy is converted to adapt to actual production line operation. Through these production line parameter-control conversions, the final pre-recycling strategy is determined. This strategy will guide the actual operation of the production line, including equipment setup and system adjustments.

[0036] Furthermore, this application also includes: The production line monitoring data is transmitted back to the digital feedback unit to identify deviations, generate single-frequency adjustment commands, and store error data. Based on the single-frequency adjustment commands, production line parameter control is adjusted. Based on a preset cycle, error data that meets a preset frequency is determined, and decision optimization commands are generated. Based on the decision optimization commands, error tracing and the update learning of the integrated management module are performed.

[0037] Specifically, various sensors and monitoring devices are installed on the metal recycling production line to collect data such as equipment status, production efficiency, and material flow rate in real time. This data is transmitted back to a digital feedback unit, where deviations are identified using preset algorithms and models—that is, the difference between actual and ideal data is compared. For identified deviations, single-frequency adjustment commands are generated, which can adjust production line parameters to correct the deviations. Based on the generated single-frequency adjustment commands, the equipment on the production line is adjusted in real time, such as changing the speed of the crusher or adjusting the threshold of the sorting equipment. A preset cycle and frequency are set according to the operating characteristics and needs of the production line for decision optimization. Within each preset cycle, error data that meets the preset frequency requirements is collected. Based on the collected error data, decision optimization commands are generated to improve the operation and strategies of the production line. Source analysis is performed on the generated errors to find the root causes of the errors. Based on the results of error source analysis, the integrated management module is updated and learned to improve its predictive and decision-making capabilities.

[0038] In summary, the efficient crushing and sorting method for recycling automotive metals provided in this application has the following technical advantages: By targeting automotive metals and determining optical sorting and crushing characteristics based on alloy series classifications, features with corresponding classifications are identified as distinguishable characteristics. Combining these optical sorting and crushing characteristics, a decision tree is constructed to train and generate an integrated management module. This module includes a bidirectional interactive optical sorting unit and a crushing management unit. The optical sorting unit uses an optical recognition system as its baseline, while the crushing management unit uses a crusher system as its baseline. Pre-recovery metals are identified. Using the integrated management module, composite sorting and coupled recovery analysis are performed based on pre-sorting-crushing-sorting to determine an initial recovery strategy. Sorting includes physical and chemical sorting, and the initial recovery strategy is a state-based strategy. Based on process phase changes, the initial recovery strategy is validated and compensated. Parameter control conversion is performed based on production line configuration characteristics to determine the pre-recovery strategy. Based on this pre-recovery strategy, metal recovery management is implemented for the pre-recovery metals. This effectively solves the problem of low sorting efficiency and severe environmental pollution during metal recovery due to the lack of accurate identification technology in existing technologies, further affecting the efficiency and sustainability of resource recovery. This allows for adaptation to different production environments and needs during recycling, improving the efficiency and purity of automotive metal recycling while reducing environmental pollution.

[0039] Example 2: Based on the same inventive concept as the efficient crushing and sorting method for recycling automotive metals described in the previous examples, this application also provides an efficient crushing and sorting system for recycling automotive metals. Please refer to the appendix. Figure 2 The aforementioned high-efficiency crushing and sorting automotive metal recycling system includes: The feature determination unit 11 is used to determine optical sorting features and breakage features for automotive metals based on alloy series categories. The features have corresponding categories and are distinguishable features.

[0040] The management module construction unit 12 is used to combine the optical sorting features and the crushing features, and to train and generate an integrated management module by constructing a decision tree. The integrated management module includes a two-way interactive optical sorting unit and a crushing management unit. The optical sorting unit is based on the optical recognition system as the construction baseline, and the crushing management unit is based on the crusher system as the construction baseline.

[0041] The recycling analysis unit 13 is used to identify pre-recoverable metals. In conjunction with the integrated management module, it performs composite sorting and coupled recycling analysis based on pre-sorting-crushing-sorting to determine the initial recycling strategy. The sorting includes physical sorting and chemical sorting, and the initial recycling strategy is a state strategy.

[0042] The recycling strategy determination unit 14 is used to perform sorting, verification and compensation of the initial recycling strategy based on the process phase change, and to perform parameter control conversion in combination with the production line configuration characteristics to determine the pre-recycling strategy.

[0043] The recycling control unit 15 is used to manage the recycling of the pre-recyclable metal based on the pre-recycling strategy.

[0044] Furthermore, the management module building unit 12 in the aforementioned high-efficiency crushing and sorting automotive metal recycling system is also used for: Based on the optical sorting features, for the first genus, a first series of features are determined as sorting features, a first decision node is constructed, the construction of the Nth decision node and node association are completed, and a decision tree is generated; based on the decision tree, a first decision layer is constructed, the first decision layer corresponding to the first genus; the optical sorting features are traversed, the construction of the Nth decision layer for the Nth genus is completed, and a first decision tree is generated; based on the first decision tree, the optical sorting unit is trained under supervision.

[0045] Furthermore, the recycling analysis unit 13 in the aforementioned high-efficiency crushing and sorting automotive metal recycling system is also used for: By performing optical scanning, a pre-sorting strategy is determined based on the optical sorting unit. The pre-sorting strategy is a genus-based cutting strategy. Combining the pre-sorting strategy, a crushing strategy is determined based on the particle size and efficiency crushing decision made by the crushing management unit. Based on the crushing strategy, a sorting strategy is determined based on the optical sorting unit. The initialization recycling strategy is determined by integrating the pre-sorting strategy, the crushing strategy, and the sorting strategy.

[0046] Furthermore, the aforementioned high-efficiency crushing and sorting automotive metal recycling system also includes a block reduction unit for: Identify the pre-sorting strategy and determine the parallel processing volume, wherein the parallel processing volume corresponds to the strategy category; based on the parallel processing volume, temporarily divide the crushing management unit and the optical sorting unit into multiple parallel processing blocks, wherein the division criteria include processing domain division and functional segmentation; complete the analysis of the initialization recycling strategy and restore the multiple temporarily defined parallel processing blocks.

[0047] Furthermore, the recycling strategy determination unit 14 in the aforementioned efficient crushing and sorting automotive metal recycling system is also used for: The alloy series categories are traversed, and phase transformation characteristics are extracted based on process phase transformation. Based on the phase transformation characteristics, the initialization recycling strategy is sorted and verified to identify phase transformation error points. Based on the phase transformation error points, the initialization recycling strategy is compensated and corrected.

[0048] Furthermore, the aforementioned high-efficiency crushing and sorting automotive metal recycling system also includes a pre-recycling strategy determination unit, used for: The equipment and system configurations of the interactive metal recycling production line are integrated with the production line configuration characteristics. Based on the production line configuration characteristics, the parameter control conversion relationship is determined, wherein the parameter control conversion relationship is the relative conversion relationship between the state of each strategy and the production line parameters under a unit step size. In combination with the parameter control conversion relationship, the initialization recycling strategy is converted into production line parameter control to determine the pre-recycling strategy.

[0049] Furthermore, the aforementioned high-efficiency crushing and sorting automotive metal recycling system also includes an update learning unit for: The production line monitoring data is transmitted back to the digital feedback unit to identify deviations, generate single-frequency adjustment commands, and store error data. Based on the single-frequency adjustment commands, production line parameter control is adjusted. Based on a preset cycle, error data that meets a preset frequency is determined, and decision optimization commands are generated. Based on the decision optimization commands, error tracing and the update learning of the integrated management module are performed.

[0050] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The efficient crushing and sorting method and specific example for recycling automotive metals in Example 1 are also applicable to the efficient crushing and sorting system for recycling automotive metals in this embodiment. Through the foregoing detailed description of the efficient crushing and sorting method for recycling automotive metals, those skilled in the art can clearly understand the efficient crushing and sorting system for recycling automotive metals in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As the system disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively simple; relevant details can be found in the method section.

[0051] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0052] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method of efficient shredding and sorting of car metal recycling, characterized by, The application relates to a metal recycling management method and device. For automobile metal, based on alloy series categories, optical sorting features and crushing features are determined, wherein the features exist in category correspondence and are distinguishing features; By combining the optical sorting features and the crushing features, a decision tree is constructed to train an integrated management module, the integrated management module comprises a bidirectional interactive optical sorting unit and a crushing management unit, the optical sorting unit takes an optical identification system as a construction baseline, and the crushing management unit takes a crusher system as a construction baseline; For pre-recycled metal, combined with the integrated management module, composite sorting and coupled recycling analysis are carried out based on pre-sorting-crushing-sorting to determine an initial recycling strategy, wherein the sorting comprises physical sorting and chemical sorting, and the initial recycling strategy is a state strategy; Based on process phase change, the initial recycling strategy is sorted, verified and compensated, combined with production line configuration characteristics for parameter control conversion to determine a pre-recycling strategy; Based on the pre-recycling strategy, metal recycling control is carried out on the pre-recycled metal.

2. A high efficiency shredding and sorting method of car metal recycling as claimed in claim 1, wherein, The integrated management module comprises an optical sorting unit, which comprises: Based on the optical sorting features, for the first category, the first series of features are determined as sorting features, a first decision node is constructed, the construction of the Nth decision node is completed, and a layer of decision tree is generated; Based on the layer of decision tree, a first decision layer is constructed, the first decision layer corresponds to the first category; The optical sorting features are traversed, the construction of the Nth decision layer of the Nth category is completed, and a first decision tree is generated; Based on the first decision tree, the optical sorting unit is supervised and trained.

3. A highly efficient shredding and sorting method of car metal recycling as claimed in claim 1, wherein, The determination of the initial recycling strategy comprises: By optical scanning, a pre-sorting strategy is determined based on the optical sorting unit, the pre-sorting strategy is a cutting strategy based on categories; Combined with the pre-sorting strategy, a crushing decision of granularity and efficiency is made based on the crushing management unit to determine a crushing strategy; Based on the crushing strategy, a sorting decision is made combined with the optical sorting unit to determine a sorting strategy; The pre-sorting strategy, the crushing strategy and the sorting strategy are integrated to determine the initial recycling strategy.

4. A highly efficient shredding and sorting method of car metal recycling as claimed in claim 3, wherein, After determining the pre-sorting strategy based on the optical sorting unit, the following steps are included: The pre-sorting strategy is identified to determine the parallel processing amount, wherein the parallel processing amount corresponds to the strategy category; Based on the parallel processing amount, the crushing management unit and the optical sorting unit are temporarily divided into multiple parallel processing blocks, wherein the division standard comprises processing domain division and function segmentation; The analysis of the initial recycling strategy is completed, and the multiple temporarily divided parallel processing blocks are restored.

5. A highly efficient shredding and sorting method of car metal recycling as claimed in claim 1, wherein, The sorting verification and compensation of the initial recycling strategy based on process phase change comprises: Traverse the alloy series categories, and mine phase change features based on process phase change; Based on the phase change features, the initial recycling strategy is sorted, verified and compensated to identify phase change error points; Based on the phase change error points, the initial recycling strategy is compensated and corrected.

6. A highly efficient shredding and sorting method of car metal recycling as claimed in claim 1, wherein, The parameter control conversion combined with the production line configuration characteristics comprises: Interactive metal recycling production line equipment configuration and system configuration are integrated to integrate production line configuration characteristics; Determine a parameter-control conversion relationship based on the production line configuration characteristics, wherein the parameter-control conversion relationship is a relative conversion relationship between each policy state and production line parameter under a unit step length; Perform production line parameter-control conversion on the initialization recovery policy based on the parameter-control conversion relationship to determine the pre-recovery policy.

7. A highly efficient shredding and sorting method of car metal recycling as claimed in claim 1, wherein, After the metal recovery control is performed, the following steps are included: Return the production line monitoring data to the digital feedback device, identify deviations, generate single-frequency adjustment instructions, and store error data; Perform production line parameter-control adjustment based on the single-frequency adjustment instructions; Based on a preset period, determine error data that meets a preset frequency, and generate decision optimization instructions; Based on the decision optimization instructions, perform error tracing and update learning of the integrated management module.

8. A high efficiency shredding and sorting automotive metal recovery system characterized by, Steps for implementing the high-efficiency crushing and sorting automobile metal recovery method according to any one of claims 1-7, the high-efficiency crushing and sorting automobile metal recovery system comprising: A feature determination unit configured to determine optical sorting features and crushing features for automobile metals based on alloy series categories, wherein the features exist in categories corresponding to distinctive features; A management module construction unit configured to construct a decision tree based on the optical sorting features and crushing features, and train an integrated management module, wherein the integrated management module includes a bidirectional interactive optical sorting unit and a crushing management unit, the optical sorting unit is based on an optical recognition system, and the crushing management unit is based on a crusher system; A recovery analysis unit configured to identify pre-recovery metals, perform composite sorting and coupled recovery analysis based on the integrated management module and a pre-sorting-crushing-sorting baseline, and determine an initialization recovery policy, wherein sorting includes physical sorting and chemical sorting, and the initialization recovery policy is a state policy; A recovery strategy determination unit configured to perform sorting verification and compensation on the initialization recovery policy based on process phase changes, perform parameter-control conversion based on production line configuration characteristics, and determine a pre-recovery policy; A recovery control unit configured to perform metal recovery control on the pre-recovery metals based on the pre-recovery policy.

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