A hospital site-oriented waste electronic component environmental protection recycling system and method

Abstract

This application provides an environmentally friendly recycling system and method for waste electronic components in hospital settings. By determining the spatial strength correlation of the waste equipment structure in connection heat maps and damage risk heat maps, and by establishing sensitive connections for electronic components within the waste equipment, risk compensation is applied to aging areas caused by long-term use, resulting in a tiered dismantling path. A synchronous transmission infrared laser is used to scan and heat dismantle the non-sensitive connection areas of the waste equipment along the tiered dismantling path, while a robotic arm is controlled to perform low-frequency vibration separation on the sensitive connection areas within the tiered dismantling path. All electronic components undergo electrical performance testing and are classified into reusable grades, with those meeting the reusable grade being entered into a reusable component library. Using this solution, electronic components from waste hospital equipment can be dismantled non-destructively through internal and external damage correlation, thereby enabling tiered recycling of electronic components.

Description

Technical Field

[0001] This application relates to the field of environmental protection and recycling technology, and more specifically, to an environmental protection recycling system and method for waste electronic components in hospital settings. Background Technology

[0002] Hospitals frequently update and iterate various medical testing instruments and diagnostic equipment, resulting in a large number of discarded electronic devices containing precision components such as chips, sensors, and circuit boards. The electronic components in these discarded devices not only have high material and functional value, but the efficient recycling and reuse of discarded electronic components in hospitals has become an important need with both economic and environmental significance.

[0003] Current methods for dismantling discarded electronic equipment largely rely on manual dismantling or general mechanical dismantling, depending solely on operator experience or simple mechanical force. These methods lack specific design considerations for the precision and fragility of internal components in medical devices. Furthermore, the dismantling process fails to consider the correlation between differences in external connection strength and the risk of damage to internal components, and does not address material aging areas caused by long-term use, easily leading to component breakage. Simultaneously, the lack of a systematic electrical performance testing and grading mechanism makes it impossible to accurately select reusable components. Therefore, how to achieve non-destructive dismantling of electronic components from discarded hospital equipment by understanding the correlation between internal and external damage, and thus grade and reuse these components, has become a significant challenge for the industry. Summary of the Invention

[0004] This application provides an environmentally friendly recycling system and method for waste electronic components in hospital settings. It can disassemble waste electronic components in hospital equipment without damage by considering internal and external damage correlation, thereby enabling graded reuse of electronic components.

[0005] In a first aspect, this application provides an environmentally friendly method for the reuse of waste electronic components in hospital settings, comprising the following steps: Determine the external connection heatmap and the damage risk heatmap of the internal electronic components of the scrap equipment; The spatial strength correlation of the structure of the scrap equipment in the connection heat map and the damage risk heat map is determined, and the electronic components in the scrap equipment are sensitively connected. Then, risk compensation is performed on the aging area caused by long-term use based on the connection results to obtain the graded dismantling path of the scrap equipment. The non-sensitive connection areas of the waste equipment are scanned and heated along the graded dismantling path. At the same time, the robotic arm is controlled to perform low-frequency vibration separation on the sensitive connection areas of the waste equipment along the graded dismantling path. By monitoring and collecting the stress wave spectrum and local temperature generated by the laser during the disassembly process, the laser power and the force of the robotic arm can be adjusted to obtain multiple undamaged electronic components. All electronic components undergo electrical performance testing and are classified into reusable grades. Electronic components that meet the reusable grade are entered into the reusable component library.

[0006] In some embodiments, determining the external connection heatmap and the damage risk heatmap of the internal electronic components of the obsolete equipment specifically includes: An initial structural image was obtained by irradiating the outer shell of the hospital's discarded equipment with a multispectral light source. The waste equipment was 3D scanned using a line laser scanner to obtain the connection strength of each connection contour pixel in the initial structural image. Determine the external connection heat map of the scrapped equipment based on all connection strengths; A pulsed laser is activated to apply a weak pulsed excitation to the surface of the waste equipment, and the vibration signal is received. The internal structure of the scrapped equipment is analyzed based on the vibration signal to obtain a heat map of the damage risk of internal components.

[0007] In some embodiments, determining the spatial strength correlation of the obsolete equipment structure in the connection heatmap and the damage risk heatmap specifically includes: Determine the risk gradient field of the damage risk heatmap; Identify multiple dismantling points of the scrapped equipment in the connection heatmap; Based on the risk gradient field, the transmitted damage risk at each point to be disassembled is determined, and the damage sweep rate of the internal vulnerable components at each point to be disassembled is obtained. By using the connection strength of all points to be dismantled as the query vector and the damage sweep rate as the key vector for interactive decision-making, the spatial strength correlation of the scrap equipment structure is obtained.

[0008] In some embodiments, making sensitive connections to the electronic components in the discarded equipment specifically includes: Acquire prior knowledge about the disassembly of medical devices; Based on the prior knowledge and the spatial intensity correlation, multiple sensitive boundary lines are determined for the division of the sensitive region; Based on all sensitive boundary lines and spatial intensity correlations, the electronic components at each point to be disassembled are sensitively identified and connected.

[0009] In some embodiments, risk compensation is performed on aging areas caused by long-term use based on the connection results, and the graded dismantling path of the obsolete equipment specifically includes: Identify multiple characteristic signal regions of the aging area of ​​the discarded equipment in the collected multispectral data; Risk compensation for empirical degradation of all connection strengths across all characteristic signal regions; Risk optimization is performed on all sensitive connections and risk-compensated dismantling points, and then the connection strength of the remaining dismantling points is integrated to obtain the graded dismantling path of the scrap equipment.

[0010] In some embodiments, scanning and heating dismantling along the non-sensitive connection areas of the scrapped equipment in the graded dismantling path specifically includes: The synchronous transmission infrared laser is scheduled to reach a designated location in the non-sensitive connection area of ​​the scrap equipment in the graded dismantling path. Adjust the synchronous transmission infrared laser parameters according to the optical characteristics of the specified location; The synchronous transmission infrared laser beam is controlled to perform trajectory scanning and heating at the location according to the micro-geometry of the joint of the scrap equipment, so as to dismantle the location. The synchronously transmitted infrared laser is scheduled to reach the next position on the graded disassembly path, and the above operation is repeated.

[0011] In some embodiments, controlling the robotic arm to perform low-frequency vibration separation on the sensitive connection areas of the scrap equipment in the graded dismantling path specifically includes: Control the robotic arm to reach the dismantling point of the sensitive connection area of ​​the waste equipment in the graded dismantling path; Based on the damage sweep rate and material properties of the disassembly point, an ultrasonic cutting tool is selected and mounted on the robotic arm, and the robotic arm parameters are adjusted. The disassembly point is separated by low-frequency vibration based on the robotic arm and its ultrasonic cutting tool.

[0012] In some embodiments, monitoring and acquiring the stress wave spectrum and local temperature generated by the laser during the disassembly process, and then adjusting the laser power and the force of the robotic arm to obtain multiple undamaged electronic components, specifically including: Continuously collect broadband stress wave signals generated during the laser and robotic arm disassembly process; Real-time spectrum analysis was performed on the broadband stress wave signal to obtain the stress signal characteristics; The stress signal features are quickly compared with a pre-stored database of signal features during normal separation to obtain the signal deviation pattern; Based on the signal deviation pattern, a graded early warning command is generated and sent to the execution unit, thereby adjusting the execution parameters of the laser and the robotic arm to obtain multiple non-destructive electronic components.

[0013] In some embodiments, electrical performance testing and reusability classification of all electronic components specifically include: Perform electrical tests on all electronic components, including at least connectivity, basic electrical parameters, and core logic functions, and record the static performance data of each electronic component. Short-term electrical stress is applied to each electronic component of a chip, and the stability and drift of the key parameters of the corresponding electronic components under stress conditions are monitored to evaluate the reliability of each electronic component of the chip. Each electronic component is classified into a reusability class based on all static performance data and all reliability statuses.

[0014] Secondly, this application provides an environmentally friendly recycling system for waste electronic components in hospital settings, comprising: The acquisition module is used to determine the connection heat map of the exterior of the scrap equipment and the damage risk heat map of the electronic components inside the scrap equipment; The processing module is used to determine the spatial strength correlation of the structure of the scrap equipment in the connection heat map and the damage risk heat map, and to make sensitive connections to the electronic components in the scrap equipment. Then, based on the connection results, risk compensation is made for the aging area caused by long-term use, and the graded dismantling path of the scrap equipment is obtained. The processing module is also used to scan and heat the non-sensitive connection areas of the waste equipment along the graded dismantling path, and at the same time, control the robotic arm to perform low-frequency vibration separation on the sensitive connection areas of the waste equipment along the graded dismantling path. The processing module is also used to monitor and collect the stress wave spectrum and local temperature generated by the laser during the disassembly process, and then adjust the laser power and the force of the robotic arm to obtain multiple undamaged electronic components. The execution module is used to perform electrical performance tests and classify reusability levels for all electronic components, and to enter the reusable component library into the reusable component library for electronic components that meet the reusability level.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application provides an environmentally friendly recycling system and method for waste electronic components in hospital settings. First, it determines the connection heatmap of the external waste equipment and the damage risk heatmap of the internal electronic components. Then, it determines the spatial strength correlation of the waste equipment structure in the connection heatmap and the damage risk heatmap, and performs sensitive connections on the electronic components within the waste equipment. Based on the connection results, it compensates for the risks of aging areas caused by long-term use, obtaining a graded dismantling path for the waste equipment. Along the graded dismantling path, it performs scanning and heating dismantling of the non-sensitive connection areas of the waste equipment. Simultaneously, it controls a robotic arm to perform low-frequency vibration separation on the sensitive connection areas of the waste equipment along the graded dismantling path. It monitors and collects the stress wave spectrum and local temperature generated by the laser during the dismantling process, and then adjusts the laser power and the robotic arm force to obtain multiple undamaged electronic components. Finally, it performs electrical performance tests and classifies all electronic components into reusable grades, and enters the reusable component library into the reusable component library.

[0016] Therefore, in the process of this application's environmentally friendly recycling method for waste electronic components in hospital settings, firstly, multispectral imaging and laser scanning are performed on the waste equipment in the hospital to accurately obtain external connection heat maps and internal component damage risk heat maps, breaking the blindness of traditional dismantling that only looks at external connections and ignores internal vulnerabilities; secondly, the spatial intensity correlation between the connection heat map and the damage risk heat map is determined, and by constructing a risk gradient field and calculating the damage sweep rate of the dismantling point, interactive decision-making is carried out with connection strength as the query vector and damage sweep rate as the key value vector, clarifying the correlation between internal and external structures; By combining prior knowledge of medical equipment dismantling to delineate sensitive boundary lines, sensitivity identification and connection clustering are performed on each dismantling point. For aging areas resulting from long-term use, multispectral data is used to identify aging characteristic signal regions, and empirical compensation for connection strength degradation is applied. Finally, a scientific, tiered dismantling path is generated by integrating multi-dimensional risk and cost considerations. Synchronous transmission infrared lasers and robotic arms are then deployed to selectively dismantle non-sensitive and sensitive areas, respectively. Simultaneously, stress wave spectra and local temperatures are monitored in real time, and execution parameters are dynamically adjusted. Finally, qualified components are stored and reused through electrical performance testing and grading. This solution enables non-destructive dismantling of electronic components from discarded hospital equipment through internal and external damage correlation, thereby grading and environmentally friendly reuse of electronic components. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart illustrating an environmentally friendly method for the reuse of waste electronic components in hospital settings, according to some embodiments of this application. Figure 2 This is an exemplary flowchart illustrating the determination of spatial intensity associations according to some embodiments of this application; Figure 3This is an exemplary flowchart illustrating the acquisition of non-destructive electronic components according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of an environmentally friendly recycling system for waste electronic components in hospital settings, as shown in some embodiments of this application. Figure 5 This is a schematic diagram of the structure of a computer device that implements a method for the environmentally friendly reuse of waste electronic components in hospital settings, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a method for the environmentally friendly reuse of waste electronic components in hospital settings, according to some embodiments of this application. This method mainly includes the following steps: In step 101, a connection heatmap of the external parts of the scrap equipment and a damage risk heatmap of the internal electronic components of the scrap equipment are determined.

[0020] In some embodiments, determining the external connection heatmap and the damage risk heatmap of the internal electronic components of the obsolete equipment can be achieved by the following steps: An initial structural image was obtained by irradiating the outer shell of the hospital's discarded equipment with a multispectral light source. The waste equipment was 3D scanned using a line laser scanner to obtain the connection strength of each connection contour pixel in the initial structural image. Determine the external connection heat map of the scrapped equipment based on all connection strengths; A pulsed laser is activated to apply a weak pulsed excitation to the surface of the waste equipment, and the vibration signal is received. The internal structure of the scrapped equipment is analyzed based on the vibration signal to obtain a heat map of the damage risk of internal components.

[0021] In practice, obtaining an initial structural image by irradiating the casing of discarded hospital equipment with a multispectral light source can be achieved as follows: The casing is irradiated with a broadband light source containing near-infrared and short-wave infrared bands. Different materials (such as silicone rubber seals and stainless steel welds) have characteristic absorption and reflection spectra in specific bands. The reflected light is collected using a hyperspectral camera, and the continuous spectral curve of each pixel is calculated. This curve is then matched with a pre-stored spectral library of common medical equipment materials to identify the materials at the pixel level and delineate the contours of all welds, adhesive lines, and clips on the casing of the discarded equipment, forming a two-dimensional image with material labels—the initial structural image. Finally, a line laser scanner is used to perform a three-dimensional scan of the discarded equipment to obtain the initial structure. The connection strength of each connected contour pixel in the image can be achieved in the following way: control the line laser scanner to move along the surface of the waste equipment, emit laser lines and capture its deformation, construct a high-precision three-dimensional point cloud model through triangulation, spatially register this three-dimensional model with the initial structural image, and obtain multiple connected contour pixels and their corresponding spatial coordinates in the initial structural image; for each connected contour pixel, in its corresponding three-dimensional point cloud neighborhood, based on the inherent mechanical properties of its material (such as elastic modulus) and the geometric features of the region (such as gap width), the connection strength of the connected contour pixel is obtained by combining the above mechanical properties and geometric features through a micromechanical model (such as a composite sphere model); other embodiments may also use other methods to achieve this, which are not limited here.

[0022] In specific implementation, determining the external connection heatmap of the scrap equipment based on all connection strengths can be achieved in the following way: normalize the connection strength of all connection contour pixels to the range of 0 to 1, and use the normalized values ​​as the thermal values ​​of each connection contour pixel. Map all thermal values ​​onto a pseudo-color gradient color palette and render them according to the coordinates of each connection contour pixel to generate a connection heatmap. The color and brightness of each pixel in the connection heatmap represent the disassembly difficulty of the outer shell connection point at that location. Analyzing the internal structure of the scrap equipment based on the vibration signal to obtain a damage risk heatmap of internal components can be achieved in the following way: perform modal analysis and transfer function analysis on the collected vibration signals. The damage risk of internal electronic components (such as chips) within the scrap equipment can be considered. Cracks in the casing and poor weld joints can alter the stiffness and damping of the local structure, resulting in anomalies in vibration modal frequencies, mode shapes, and energy attenuation. Therefore, using the three-dimensional point cloud model of the waste equipment casing as the boundary, a voxelization algorithm is used to generate multiple regularly divided three-dimensional voxel meshes with uniform initial properties within the enclosed internal space. Then, a tomographic inversion algorithm is used to correlate the measured vibration field anomalies with the internal spatial location, calculate the deviation index of structural integrity at each internal three-dimensional coordinate point, map the deviation index onto the three-dimensional spatial mesh, and project it onto a two-dimensional view plane in the form of thermal values ​​to generate a damage risk heat map. The highlighted areas of the damage risk heat map indicate the locations of damaged or vulnerable components within the structure. Other embodiments may also employ other methods, which are not limited here.

[0023] It should be noted that the connection strength in this application is a model calculation value based on its material properties and three-dimensional geometric features, characterizing the physical resistance of a specific connection point (such as a solder joint or adhesive dot) to separation forces; the connection heat map is a two-dimensional image used to visualize and quantify the normalized connection strength coefficients of all connection points to be disassembled on the casing of the scrap equipment through color mapping, and its heat value directly corresponds to the degree of disassembly difficulty; the damage risk heat map refers to an image that inverts the health status of the internal structure of the scrap equipment by analyzing the casing vibration signal and identifies the spatial distribution of potential damage or highly vulnerable areas of internal components in the form of heat values.

[0024] In step 102, the spatial strength correlation of the scrap equipment structure in the connection heatmap and the damage risk heatmap is determined, and the electronic components in the scrap equipment are sensitively connected. Then, based on the connection results, risk compensation is performed on the aging areas caused by long-term use to obtain the graded dismantling path of the scrap equipment.

[0025] In some embodiments, reference Figure 2As shown, this figure is an exemplary flowchart for determining spatial strength correlation in some embodiments of this application. In this embodiment, determining the spatial strength correlation of the scrap equipment structure in the connection heatmap and the damage risk heatmap can be achieved by the following steps: First, in step 1021, the risk gradient field of the damage risk heatmap is determined; Secondly, in step 1022, multiple dismantling points of the scrap equipment in the connection heat map are determined; Furthermore, in step 1023, the risk of damage transmitted to each point to be disassembled is determined based on the risk gradient field, and the damage sweep rate of the internal vulnerable components at each point to be disassembled is obtained. Finally, in step 1024, the connection strength of all points to be dismantled is used as the query vector and the damage sweep rate is used as the key vector for interactive decision-making to obtain the spatial strength association of the scrap equipment structure.

[0026] In specific implementation, the risk gradient field of the damage risk heatmap can be determined in the following way: A scalar field is formed by combining all pixels and their thermal values ​​in the damage risk heatmap. The thermal value of each pixel in the damage risk heatmap represents the overall risk intensity below the pixel's projected location. Therefore, a gradient operator (such as the Sobel operator) in image processing is used to convolve this scalar field to obtain a new vector field. This vector field has direction and magnitude at each pixel. Its direction points to the direction of the fastest risk increase, and its magnitude represents the severity of risk change. This vector field is the risk gradient field. The determination of multiple dismantling points of the scrapped equipment in the connection heatmap can be achieved in the following way: A connection strength threshold (overall mean times 1.5 times standard deviation) is set on the connection heatmap. All pixels with thermal values ​​higher than this connection strength threshold are extracted. Each extracted point represents a physical connection location that needs to be separated. The image coordinates of these points are transformed to a unified coordinate system to form a set of dismantling points with spatial location and connection strength. Other embodiments can also use other methods, which are not limited here.

[0027] In specific implementation, the damage risk transmitted to each disassembly point is determined based on the risk gradient field. The damage sweep rate of the internal vulnerable components at each disassembly point can be obtained in the following way: On the damage risk heatmap, through the connected component analysis algorithm in image processing, all continuous pixel regions with thermal values ​​exceeding a preset threshold are identified and defined as high-risk regions; for each disassembly point, the magnitude of the gradient vector at that point (i.e., the risk change rate) is calculated, and the magnitude is weighted and fused with the distance from the disassembly point to the center of the nearest high-risk region. Then, this fused value is normalized to a probability value between 0 and 1 through an S-shaped function. This probability value is the damage sweep rate; where the weighted fusion formula can be: α * risk change rate + β * reciprocal of distance, where α and β are weight coefficients pre-calibrated based on a large amount of experimental data (satisfying α + β = ... 1) Damage sweep rate is a parameter value that quantifies the probability that stress or thermal disturbance will be transmitted to internal vulnerable components and cause damage during disassembly operations; other embodiments may also be implemented in other ways, which are not limited here.

[0028] In specific implementation, the spatial strength association of the scrap equipment structure is obtained by interactive decision-making using the connection strength of all points to be dismantled as the query vector and the damage sweep rate as the key vector. This can be achieved in the following way: the set of connection strengths of all points to be dismantled is regarded as the query vector, and the set of damage sweep rates corresponding to each point to be dismantled is regarded as the key vector and value vector. By calculating and normalizing the similarity between the query vector and the key vector of each dismantling point, a weight matrix is ​​obtained. This weight matrix represents the association strength between the dismantling requirement (strength) and the operational risk (sweep rate) of each dismantling point. Finally, the connection strength of each point to be dismantled is weighted and fused with this weight to obtain the spatial strength association value of each point to be dismantled. Thus, the set of all points to be dismantled and their spatial strength association values ​​is taken as the spatial strength association of the scrap equipment structure. Other embodiments may also use other methods to achieve this, which are not limited here.

[0029] It should be noted that the risk gradient field in this application is a vector field derived from the damage risk heatmap, characterizing the spatial trend and rate of change of internal vulnerability risk, with its vector direction pointing in the direction of increasing risk; the point to be dismantled refers to the set of specific spatial coordinates in the real physical world that need to be manipulated to separate the shell or structure, selected on the connection heatmap according to the strength threshold; the damage sweep rate is a quantitative assessment of the estimated probability that the mechanical or thermal effects of performing a dismantling action on a specific point to be dismantled will spread to and cause damage to nearby internal vulnerable components; spatial strength correlation refers to the interactive decision-making process simulated through an attention mechanism, linking the self-connectivity strength of the point to be dismantled with its potential risk to internal components. After fusion calculation, a comprehensive decision weight value is obtained. First, a risk gradient field is constructed using a gradient operator to build a damage risk heatmap, accurately capturing the spatial variation trend of internal damage. Then, a connection heatmap is used to screen points to be disassembled, and the damage sweep rate of each point to be disassembled is calculated using the risk gradient field. Finally, the connection strength is used as a query vector and the damage sweep rate is used as a key vector for interactive decision-making. This binds external disassembly requirements with internal vulnerability risks, solving the problem that traditional disassembly only focuses on external connection strength and ignores the internal damage transmission correlation. It is convenient to quantify the correspondence between the disassembly difficulty and damage risk of each point to be disassembled, avoid indirect damage to internal precision components during disassembly operations, and lay the foundation for non-destructive disassembly.

[0030] In some embodiments, sensitive connections to electronic components in the discarded equipment can be achieved using the following steps: Acquire prior knowledge about the disassembly of medical devices; Based on the prior knowledge and the spatial intensity correlation, multiple sensitive boundary lines are determined for the division of the sensitive region; Based on all sensitive boundary lines and spatial intensity correlations, the electronic components at each point to be disassembled are sensitively identified and connected.

[0031] In specific implementation, determining multiple sensitive boundary lines for the sensitive area based on the prior knowledge and the spatial intensity correlation can be achieved in the following way: First, read the disassembly prior knowledge for medical electronic devices from a structured knowledge base, such as: a 5mm area around a module containing biosensors is an extremely high-risk area, and the area below the shielding cover is usually the main chip area; second, map all spatial intensity correlation values ​​back to the three-dimensional surface model of the discarded equipment, and combine prior knowledge rules and image segmentation algorithms to divide the regions of different spatial intensity correlation values ​​and the regions of rule matching results, thereby generating multiple closed or open sensitive boundary lines. These lines delineate regions of different sensitivity levels on the model; for example, when the spatial intensity correlation value of a certain region is higher than a threshold and is located in the main chip area marked by prior knowledge, then the boundary of that region is marked as a level one sensitive boundary line; the setting of the spatial intensity correlation value range can be, for example: [0, 0.3) is the low-risk range, [0.3, 0.7) is the medium-risk range, and [0.7, 1.0] is the high-risk range; other embodiments can also be implemented in other ways, which are not limited here.

[0032] In specific implementation, the sensitivity judgment and connection of electronic components at each disassembly point based on all sensitive boundary lines and spatial intensity correlations can be achieved in the following way: traverse all disassembly points, using sensitive boundary lines as regional control boundaries and spatial intensity correlation values ​​corresponding to each disassembly point as the core risk judgment indicator, and perform graded sensitivity judgment: if the disassembly point falls within the area enclosed by any sensitive boundary line, or the spatial intensity correlation value of the disassembly point falls into the preset high-risk range [0.7, If a point is identified as a sensitive connection point, it is marked as such. Simultaneously, the spatial strength correlation value of this sensitive connection point is used as the core parameter for its dismantling risk level, and is bound and stored with the point's spatial coordinates, connection strength, and damage sweep rate. Sensitive connection points that are spatially adjacent, belong to the same sensitive boundary line control area, and have spatial strength correlation values ​​within the same risk range are all grouped into the same sensitive connection cluster. This means that points within the sensitive connection cluster are considered to have interconnected dismantling strategies, requiring a unified plan for a safe dismantling sequence and method. All remaining points to be dismantled are grouped into the same non-sensitive connection cluster. Other implementation methods can also be used in other embodiments, which are not limited here.

[0033] It should be noted that the sensitive boundary line in this application refers to the geometric boundary line on the 3D model or 2D projection drawing of the equipment, which is divided according to spatial strength correlation calculation and prior knowledge rules to identify different levels of disassembly sensitivity and risk areas; the sensitive connection cluster refers to the grouping of spatially adjacent sensitive connection points that are all judged to be high-risk, as a set of point units that require special and coherent safety strategies in disassembly path planning; among them, the sensitive components of hospital equipment have poor heat resistance and shock resistance, and there is no clear judgment standard for sensitive areas, making it easy for traditional disassembly to accidentally touch high-risk areas; however, this application, by combining prior knowledge of medical equipment disassembly and spatial strength correlation, can clearly define the boundaries of sensitive areas, cluster the scattered sensitive points to be disassembled, and solve the problems of ambiguous sensitive area division and lack of targeted disassembly strategies, thereby facilitating the accurate identification and clustered management of sensitive connection points, clarifying the areas and operational logic that need special protection during the disassembly process, avoiding direct or indirect damage to fragile components, and improving the safety and targeting of disassembly operations.

[0034] In some embodiments, risk compensation is performed on aging areas caused by long-term use based on the connection results. The graded dismantling path of the scrapped equipment can be achieved by the following steps: Identify multiple characteristic signal regions of the aging area of ​​the discarded equipment in the collected multispectral data; Risk compensation for empirical degradation of all connection strengths across all characteristic signal regions; Risk optimization is performed on all sensitive connections and risk-compensated dismantling points, and then the connection strength of the remaining dismantling points is integrated to obtain the graded dismantling path of the scrap equipment.

[0035] In specific implementation, identifying multiple characteristic signal regions of the aging area of ​​the discarded equipment in the collected multispectral data can be achieved in the following way: Analyze the original hyperspectral data cube obtained from the multispectral scan: perform differential transformation on the spectral curve of each pixel to find regions where there is a significant shift, broadening, or the appearance of new peaks at specific wavelengths (such as characteristic absorption peaks related to metal corrosion); then, using spectral angle mapping or anomaly detection algorithms, extract these pixel regions where the spectral features have undergone statistically significant changes and mark them as aging characteristic signal regions; risk compensation for empirical loss of all connectivity strengths in all characteristic signal regions can be adopted... This can be achieved in the following way: For each point to be disassembled located within the aging characteristic signal region, its previously calculated connection strength needs to be down-compensated: Based on the type of aging region (such as metal oxidation) and the degree of aging (spectral shift), the corresponding loss coefficient (e.g., 0.7, indicating 70% strength retention) is retrieved from an empirical loss comparison table established based on a large amount of historical aging strength data. This loss coefficient is multiplied by the connection strength of the point to be disassembled to obtain the risk-compensated connection strength of the point to be disassembled, and the spatial strength correlation value of the point to be disassembled is recalculated. Other methods can also be used in other embodiments, which are not limited here.

[0036] In specific implementation, risk optimization is performed on all sensitively connected and risk-compensated dismantling points, and then the connection strength of the remaining dismantling points is integrated to obtain the graded dismantling path of the scrap equipment. This can be achieved in the following way: A path planning graph is constructed using all dismantling points as nodes. The weights of the edges in the path planning graph are dynamically composed of distance and comprehensive risk cost. The risk cost includes: electronic component value factor, damage spread rate, and spatial correlation value (increasing the overall risk cost of the entire sensitive connection cluster). Then, an optimization algorithm combining dynamic programming (such as the improved A* algorithm) is used to find... A sequence of steps is generated, starting from the origin, traversing all non-sensitive points, and finally processing all sensitive clusters. The algorithm is set to prioritize paths with the lowest total cost and enforces that a path is only allowed to enter a sensitive cluster after all non-sensitive points around it have been processed. This ensures that when processing highly sensitive clusters, low-risk points around them have been cleared to create a safe space. The resulting sequence with a clear order is the hierarchical decomposition path. The hierarchical decomposition path clearly defines the decomposition order and risk level of each point. Other implementations may also use other methods, which are not limited here.

[0037] It should be noted that the aging characteristic signal region in this application refers to the local pixel region where the material's spectral characteristic curve has deviated from the standard new part characteristics, indicating that the material has undergone chemical degradation or physical deterioration, as detected by analyzing multispectral data. The risk-compensated connection strength is the original connection strength coefficient of the disassembly point located in the material aging region, which is empirically reduced and corrected according to its aging type and degree to obtain a strength estimate that is closer to the actual mechanical state. The graded disassembly path is a safe and efficient operation sequence and stage generated by a path optimization algorithm after comprehensively considering the spatial distance of disassembly points, the strength after risk compensation, the correlation of sensitive clusters, and the global risk cost. The system divides the work sequence into sections. In hospital equipment, aging after long-term use leads to decreased connection strength. Traditional disassembly methods, based on the original strength, are prone to over- or under-strength application of force. Furthermore, the lack of coordination between sensitive and non-sensitive areas in the disassembly sequence can easily trigger secondary risks. By correcting strength values ​​through aging compensation and integrating multi-dimensional risk and cost planning, the system can solve the problems of inaccurate strength assessment and disordered paths. The resulting tiered disassembly paths closely match the actual aging state of the equipment, prioritizing low-risk, non-sensitive areas and creating safe working space for sensitive connection clusters. This avoids component damage caused by misjudgment of aging strength, improves disassembly efficiency, and ensures the integrity and reusability of disassembled electronic components.

[0038] In step 103, the non-sensitive connection areas of the waste equipment are scanned and heated for dismantling along the graded dismantling path. At the same time, the robotic arm is controlled to perform low-frequency vibration separation on the sensitive connection areas of the waste equipment along the graded dismantling path.

[0039] In some embodiments, scanning and heating dismantling along the non-sensitive connection areas of the scrapped equipment in the graded dismantling path can be achieved using the following steps: The synchronous transmission infrared laser is scheduled to reach a designated location in the non-sensitive connection area of ​​the scrap equipment in the graded dismantling path. Adjust the synchronous transmission infrared laser parameters according to the optical characteristics of the specified location; The synchronous transmission infrared laser beam is controlled to perform trajectory scanning and heating at the location according to the micro-geometry of the joint of the scrap equipment, so as to dismantle the location. The synchronously transmitted infrared laser is scheduled to reach the next position on the graded disassembly path, and the above operation is repeated.

[0040] In specific implementation, scheduling the synchronous transmission infrared laser to a designated position in the non-sensitive connection area of ​​the scrap equipment in the graded dismantling path can be achieved in the following way: The central controller, according to the instructions of the graded dismantling path, positions the optical output head of the synchronous transmission infrared laser above the designated position of the non-sensitive connection point to be processed in the current graded dismantling path via a high-precision moving platform (e.g., a six-axis robot), while maintaining the laser beam focus perpendicular to the surface to be processed. Adjusting the synchronous transmission infrared laser parameters according to the optical characteristics of the designated position can be achieved in the following way: The material type identified in the initial multispectral scan at the designated position is queried, and an optimized parameter set for the combination of upper transparent material and lower absorbing material is retrieved from a preset laser process parameter library. The parameters include laser wavelength (e.g., 980nm), initial power, pulse frequency, and spot size; the synchronous transmission infrared laser is adjusted according to the above parameters; the synchronous transmission infrared laser beam is controlled to perform trajectory scanning and heating at the location according to the micro-geometry of the joint of the waste equipment, so that the location can be disassembled. This can be achieved by driving the beam of the synchronous transmission infrared laser so that its focal point moves along the micro-geometry of the connection point contour in a non-contact scanning motion. During this process, the laser energy penetrates the upper light-transmitting material, is selectively absorbed by the lower absorption layer (adhesive or specially added absorbent), and is converted into heat, causing the adhesive interface to melt locally, thereby achieving non-destructive separation of the joint, while the surface material is basically unaffected by heat. Other embodiments may also use other methods, which are not limited here.

[0041] In some embodiments, controlling the robotic arm to perform low-frequency vibration separation on the sensitive connection areas of the scrap equipment in the graded dismantling path can be achieved by the following steps: Control the robotic arm to reach the dismantling point of the sensitive connection area of ​​the waste equipment in the graded dismantling path; Based on the damage sweep rate and material properties of the disassembly point, an ultrasonic cutting tool is selected and mounted on the robotic arm, and the robotic arm parameters are adjusted. The disassembly point is separated by low-frequency vibration based on the robotic arm and its ultrasonic cutting tool.

[0042] In specific implementation, controlling the robotic arm to reach the dismantling point of the sensitive connection area of ​​the scrap equipment in the graded dismantling path can be achieved in the following way: the central controller schedules the high-precision robotic arm, carrying a universal tool quick-change interface, to move to a preset safe position above the sensitive connection dismantling point that needs to be processed in the graded dismantling path; selecting and loading an ultrasonic cutting tool onto the robotic arm based on the damage sweep rate and material properties of the dismantling point, and adjusting the robotic arm parameters can be achieved in the following way: based on the damage sweep rate of the dismantling point (high risk requires less force) and its material properties (e.g., brittle plastic or thin metal sheet), an ultrasonic cutting tool is selected from the tool library; the robotic arm automatically... The tool is loaded, and the upper limit of force control, approach speed, and vibration frequency and amplitude of the ultrasonic scalpel are set according to the damage sweep rate and material properties. The low-frequency vibration separation of the disassembly point by the robotic arm and its ultrasonic cutting tool can be achieved in the following way: the robotic arm controls the ultrasonic cutting tool to contact the connection point in force control mode. While the tool head vibrates ultrasonically, it may be supplemented with a specific prying or scraping low-frequency macroscopic motion trajectory (usually from a few hertz to tens of hertz). This composite motion can more effectively concentrate the vibration energy on the interface and guide the separation direction, achieving safe release of aging or fragile connections. Other embodiments may also use other methods, which are not limited here.

[0043] In step 104, the stress wave spectrum and local temperature generated by the laser during the disassembly process are monitored and collected, and then the laser power and the force of the robotic arm are adjusted to obtain multiple undamaged electronic components.

[0044] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart of obtaining non-destructive electronic components in some embodiments of this application. In this embodiment, monitoring and collecting the stress wave spectrum and local temperature generated by the laser during the disassembly process, and then adjusting the laser power and the force of the robotic arm to obtain multiple non-destructive electronic components can be achieved by the following steps: First, in step 1041, broadband stress wave signals generated during the laser and robotic arm disassembly process are continuously acquired; Secondly, in step 1042, the broadband stress wave signal is subjected to real-time spectrum analysis to obtain stress signal characteristics; Then, in step 1043, the stress signal features are quickly compared with the pre-stored signal feature library during normal separation to obtain the signal deviation pattern; Finally, in step 1044, a graded early warning command is generated based on the signal deviation pattern and sent to the execution unit, thereby adjusting the execution parameters of the laser and the robotic arm to obtain multiple non-destructive electronic components.

[0045] In specific implementation, the continuous acquisition of broadband stress wave signals generated during laser and robotic arm disassembly can be achieved in the following way: Multiple broadband acoustic emission sensors are deployed near the work area to continuously acquire elastic wave signals excited by events such as material fracture, friction, and thermal shock during disassembly, and convert them into continuous broadband stress wave electrical signals. Real-time spectrum analysis of the broadband stress wave signals to obtain stress signal characteristics can be achieved in the following way: A short-time Fourier transform is performed on the acquired stress wave signals in the time domain to obtain their time-varying spectrum. Key stress signal characteristics are extracted from the spectrum, including at least: the energy integral value of a specific frequency band (such as the characteristic frequency band corresponding to chip cracking), and the dominant frequency offset. The signal deviation pattern can be obtained by rapidly comparing the stress signal features with a pre-stored database of normal separation signals to determine the signal deviation pattern. This can be achieved by: forming a vector from the extracted stress signal features; calculating the cosine similarity of this vector with the pre-stored baseline feature templates of various normal separation events in the signal feature database; setting the signal deviation pattern to a level one warning when the cosine similarity is between 0.7 and 0.9; setting it to a level two warning when the cosine similarity is between 0.7 and 0.5; and setting it to a level three emergency stop when the cosine similarity is below 0.5, and recording the feature dimension with the largest deviation. Other implementation methods can also be used in other embodiments, which are not limited here.

[0046] In specific implementation, generating graded early warning commands based on the signal deviation mode and sending them to the execution unit to adjust the execution parameters of the laser and the robotic arm can be achieved in the following way: triggering a predefined graded response based on the type and severity of the signal deviation mode; for example, for a level one early warning, instructing the laser power to be reduced by 20% or the robotic arm force to be reduced by 30%; for a level three emergency stop, immediately shutting off the laser or ordering the robotic arm to withdraw; sending the graded early warning commands to the corresponding execution units in real time to achieve adaptive control based on process feedback; other methods can also be used in other embodiments, which are not limited here.

[0047] It should be noted that the broadband stress wave signal in this application refers to the transient elastic wave signal that is generated by the rapid release of internal energy of the material during the disassembly process and propagates in the structure, covering a range from low frequency to high frequency; the stress signal characteristics are a set of quantitative indicators used to extract from the time-frequency analysis results of the stress wave signal that can characterize the type of specific physical event (such as normal cutting or abnormal cracking); the signal deviation mode describes the type and degree of significant difference between the acoustic emission signal characteristics of the disassembly process monitored in real time and the pre-stored normal feature template, and is a criterion for judging whether the process is abnormal.

[0048] In step 105, all electronic components are subjected to electrical performance tests and classified into reusable grades. Electronic components that meet the reusable grades are entered into the reusable component library.

[0049] In some embodiments, the electrical performance testing and reusability classification of all electronic components can be achieved by the following steps: Perform electrical tests on all electronic components, including at least connectivity, basic electrical parameters, and core logic functions, and record the static performance data of each electronic component. Short-term electrical stress is applied to each electronic component of a chip, and the stability and drift of the key parameters of the corresponding electronic components under stress conditions are monitored to evaluate the reliability of each electronic component of the chip. Each electronic component is classified into a reusability class based on all static performance data and all reliability statuses.

[0050] In practice, performing electrical tests on all electronic components, including at least connectivity, basic electrical parameters, and core logic functions, and recording the static performance data of each electronic component, can be achieved as follows: After classifying the disassembled components, use automated testing equipment or flying probe testers to perform electrical tests according to the original specifications of the components. Test items should include at least: pin connectivity / insulation, resistance / capacitance / inductance values, diode / transistor characteristics, and for chips, basic logic function verification through boundary scan or test vectors. All results should be recorded as the static performance data archive of the component. Short-term electrical stress should be applied to each electronic component of the chip class, and the stability and drift of the key parameters of the corresponding electronic components under stress conditions should be monitored to evaluate the chip class. The reliability status of each electronic component can be achieved in the following way: For core chips such as microprocessors and memory, reliability screening is performed: they are placed on a temperature-controlled test socket and subjected to short-term high-temperature operating life tests or voltage bias tests. During the process, their core parameters (such as operating current, clock frequency, and output drive capability) are continuously monitored, and the drift of parameters during and after stress is recorded. Components with fully compliant static performance and minimal post-stress drift are set to reliable status; components with compliant main static performance indicators but slight signs of aging are set to degraded status; and components with only partial functionality are set to unreliable status. The reusability level of each electronic component based on all static performance data and all reliability statuses can be achieved in the following way: according to GB / T... Based on relevant standards such as GB / T 21474-2008 "Evaluation Guidelines for Reuse and Recycling Systems of Waste Electrical and Electronic Products" and industry practices, classification rules are established. For example: those with fully compliant static performance and minimal post-stress drift are classified as Grade A - direct reuse; those with compliant main static performance indicators but slight signs of aging are classified as Grade B - downgraded use; those with only partial functionality or used for teaching analysis are classified as Grade C - resource recycling / teaching. After classification, the component information is entered into the reusable component library. Other implementation methods can also be used, which are not limited here.

[0051] In another aspect, in some embodiments, this application provides an environmentally friendly recycling system for waste electronic components in hospital settings, as referenced. Figure 4 The figure is a schematic diagram of the structure of an environmentally friendly recycling system for waste electronic components in hospital settings, according to some embodiments of this application. This system includes an acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to determine the connection heat map of the external part of the waste equipment and the damage risk heat map of the internal electronic components of the waste equipment. Processing module 402, in this application, is used to determine the spatial strength correlation of the structure of the scrap equipment in the connection heat map and the damage risk heat map, and to make sensitive connections to the electronic components in the scrap equipment. Then, based on the connection results, risk compensation is made for the aging area caused by long-term use, and the graded dismantling path of the scrap equipment is obtained. It should be noted that the processing module 402 in this application is also used to scan and heat the non-sensitive connection area of ​​the waste equipment along the graded dismantling path, and at the same time, control the robotic arm to perform low-frequency vibration separation on the sensitive connection area of ​​the waste equipment in the graded dismantling path. In addition, it should be noted that the processing module 402 in this application is also used to monitor and collect the stress wave spectrum and local temperature generated by the laser during the disassembly process, and then adjust the laser power and the force of the robotic arm to obtain multiple non-destructive electronic components. The execution module 403 in this application is mainly used to perform electrical performance tests and classify the reusability level of all electronic components, and to enter the reusable component library into the reusable component library.

[0052] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, the processor being configured to acquire the code and execute the above-described method for the environmentally friendly reuse of waste electronic components in hospital settings.

[0053] In some embodiments, reference Figure 5 This figure is a schematic diagram of the structure of a computer device implementing a method for the environmentally friendly reuse of waste electronic components in hospital settings, according to some embodiments of this application. The environmentally friendly reuse method for waste electronic components in hospital settings described in the above embodiments can be achieved through... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0054] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0055] The communication bus 502 can be used to transmit information between the aforementioned components.

[0056] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0057] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0058] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0059] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0060] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0061] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for the environmentally friendly reuse of waste electronic components in hospital settings.

[0062] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0063] 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 the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for the environmentally friendly reuse of waste electronic components in hospital settings, characterized in that, Includes the following steps: Determine the external connection heatmap and the damage risk heatmap of the internal electronic components of the scrap equipment; The spatial strength correlation of the structure of the scrap equipment in the connection heat map and the damage risk heat map is determined, and the electronic components in the scrap equipment are sensitively connected. Then, risk compensation is performed on the aging area caused by long-term use based on the connection results to obtain the graded dismantling path of the scrap equipment. The non-sensitive connection areas of the waste equipment are scanned and heated along the graded dismantling path. At the same time, the robotic arm is controlled to perform low-frequency vibration separation on the sensitive connection areas of the waste equipment along the graded dismantling path. By monitoring and collecting the stress wave spectrum and local temperature generated by the laser during the disassembly process, the laser power and the force of the robotic arm can be adjusted to obtain multiple undamaged electronic components. All electronic components undergo electrical performance testing and are classified into reusable grades. Electronic components that meet the reusable grade are entered into the reusable component library.

2. The method as described in claim 1, characterized in that, Determining the external connection heatmap and the damage risk heatmap of the internal electronic components of the obsolete equipment specifically includes: An initial structural image was obtained by irradiating the outer shell of the hospital's discarded equipment with a multispectral light source. The waste equipment was 3D scanned using a line laser scanner to obtain the connection strength of each connection contour pixel in the initial structural image. Determine the external connection heat map of the scrapped equipment based on all connection strengths; A pulsed laser is activated to apply a weak pulsed excitation to the surface of the waste equipment, and the vibration signal is received. The internal structure of the scrapped equipment is analyzed based on the vibration signal to obtain a heat map of the damage risk of internal components.

3. The method as described in claim 1, characterized in that, Determining the spatial strength correlation of the scrapped equipment structure in the connection heatmap and the damage risk heatmap specifically includes: Determine the risk gradient field of the damage risk heatmap; Identify multiple dismantling points of the scrapped equipment in the connection heatmap; Based on the risk gradient field, the transmitted damage risk at each point to be disassembled is determined, and the damage sweep rate of the internal vulnerable components at each point to be disassembled is obtained. By using the connection strength of all points to be dismantled as the query vector and the damage sweep rate as the key vector for interactive decision-making, the spatial strength correlation of the scrap equipment structure is obtained.

4. The method as described in claim 1, characterized in that, Making sensitive connections to the electronic components in the aforementioned discarded equipment specifically includes: Acquire prior knowledge about the disassembly of medical devices; Based on the prior knowledge and the spatial intensity correlation, multiple sensitive boundary lines are determined for the division of the sensitive region; Based on all sensitive boundary lines and spatial intensity correlations, the electronic components at each point to be disassembled are sensitively identified and connected.

5. The method as described in claim 1, characterized in that, Based on the connection results, risk compensation is performed on aging areas caused by long-term use, resulting in a graded dismantling path for the obsolete equipment, which specifically includes: Identify multiple characteristic signal regions of the aging area of ​​the discarded equipment in the collected multispectral data; Risk compensation for empirical degradation of all connection strengths across all characteristic signal regions; Risk optimization is performed on all sensitive connections and risk-compensated dismantling points, and then the connection strength of the remaining dismantling points is integrated to obtain the graded dismantling path of the scrap equipment.

6. The method as described in claim 1, characterized in that, The scanning and heating dismantling of the non-sensitive connection areas of the scrap equipment along the graded dismantling path specifically includes: The synchronous transmission infrared laser is scheduled to reach a designated location in the non-sensitive connection area of ​​the scrap equipment in the graded dismantling path. Adjust the synchronous transmission infrared laser parameters according to the optical characteristics of the specified location; The synchronous transmission infrared laser beam is controlled to perform trajectory scanning and heating at the location according to the micro-geometry of the joint of the scrap equipment, so as to dismantle the location. The synchronously transmitted infrared laser is scheduled to reach the next position on the graded disassembly path, and the above operation is repeated.

7. The method as described in claim 1, characterized in that, The control of the robotic arm to perform low-frequency vibration separation on the sensitive connection areas of the scrap equipment in the graded dismantling path specifically includes: Control the robotic arm to reach the dismantling point of the sensitive connection area of ​​the waste equipment in the graded dismantling path; Based on the damage sweep rate and material properties of the disassembly point, an ultrasonic cutting tool is selected and mounted on the robotic arm, and the robotic arm parameters are adjusted. The disassembly point is separated by low-frequency vibration based on the robotic arm and its ultrasonic cutting tool.

8. The method as described in claim 1, characterized in that, By monitoring and collecting the stress wave spectrum and local temperature generated by the laser during the disassembly process, and then adjusting the laser power and the force of the robotic arm, multiple non-destructive electronic components can be obtained, including: Continuously collect broadband stress wave signals generated during the laser and robotic arm disassembly process; Real-time spectrum analysis was performed on the broadband stress wave signal to obtain the stress signal characteristics; The stress signal features are quickly compared with a pre-stored database of signal features during normal separation to obtain the signal deviation pattern; Based on the signal deviation pattern, a graded early warning command is generated and sent to the execution unit, thereby adjusting the execution parameters of the laser and the robotic arm to obtain multiple non-destructive electronic components.

9. The method as described in claim 1, characterized in that, The electrical performance testing and reusability classification of all electronic components specifically includes: Perform electrical tests on all electronic components, including at least connectivity, basic electrical parameters, and core logic functions, and record the static performance data of each electronic component. Short-term electrical stress is applied to each electronic component of a chip, and the stability and drift of the key parameters of the corresponding electronic components under stress conditions are monitored to evaluate the reliability of each electronic component of the chip. Each electronic component is classified into a reusability class based on all static performance data and all reliability statuses.

10. An environmentally friendly recycling system for waste electronic components in hospital settings, characterized in that, include: The acquisition module is used to determine the connection heat map of the exterior of the scrap equipment and the damage risk heat map of the electronic components inside the scrap equipment; The processing module is used to determine the spatial strength correlation of the structure of the scrap equipment in the connection heat map and the damage risk heat map, and to make sensitive connections to the electronic components in the scrap equipment. Then, based on the connection results, risk compensation is made for the aging area caused by long-term use, and the graded dismantling path of the scrap equipment is obtained. The processing module is also used to scan and heat the non-sensitive connection areas of the waste equipment along the graded dismantling path, and at the same time, control the robotic arm to perform low-frequency vibration separation on the sensitive connection areas of the waste equipment along the graded dismantling path. The processing module is also used to monitor and collect the stress wave spectrum and local temperature generated by the laser during the disassembly process, and then adjust the laser power and the force of the robotic arm to obtain multiple undamaged electronic components. The execution module is used to perform electrical performance tests and classify reusability levels for all electronic components, and to enter the reusable component library into the reusable component library for electronic components that meet the reusability level.

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