Waste treatment management method and system, computer equipment, medium and program product

Abstract

The invention relates to a waste treatment management method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring detection information obtained by detecting a to-be-processed target object, wherein the detection information comprises a detection image; the detection information is processed based on a preset recognition model, structural features of the target object are obtained, and the structural features comprise structural information of the target object and feature labeling information on the target object; on the basis of the structure information and the feature labeling information, determining disposal process parameters for disassembling the target object, wherein the disposal process parameters comprise a shearing blade gap, a breaking hammer point location and an element sorting position; and target equipment is driven based on the disposal process parameters to realize the disassembly processing procedure of the target object. By the adoption of the method, the sorting fineness of the waste can be improved, and the recovery efficiency and the resource conversion rate of waste treatment and recovery are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of waste disposal, and in particular to a waste treatment management method and device, computer equipment, a storage medium and a computer program product. BACKGROUND

[0002] With the acceleration of electronic product iteration, the global e-waste scale continues to rise. According to statistics, the total amount of global e-waste in 2023 has exceeded 60 million tons, but the recycling rate is less than 20%. Electronic waste contains not only rare metals such as gold, silver and copper, but also harmful substances such as lead, mercury and cadmium. If it is randomly buried or incinerated, it will lead to soil pollution, water toxicity and greenhouse gas emissions. On the contrary, through professional reverse recycling, not only can the environmental risk be reduced, but also the resource recycling can be realized, and the mining pressure can be alleviated. For example, the metal content of 1 ton of waste mobile phone circuit board is equivalent to the refining amount of 30 tons of primary ore, which has significant economic and environmental value.

[0003] The necessity of waste recycling and treatment is reflected in three aspects: first, environmental protection demand. The components such as brominated flame retardant and PVC plastic in electronic waste release dioxin in natural degradation, and standardized recycling can block such pollution chain. Second, resource security strategy. Many countries list key metals such as rare earth and cobalt in electronic waste as strategic reserves, and recycling and reuse can reduce dependence on external resources. Third, industry compliance requirements. The production standards of many industries require producers to bear the responsibility of waste recycling, and enterprises that violate the rules will face heavy fines.

[0004] In related technologies, the recycling and treatment of waste mainly includes classification collection, crushing and sorting, purification, harmless disposal and resource reuse, etc. Among them, crushing and sorting mainly relies on physical, chemical and manual methods to realize separation through physical differences, such as magnetic separation by using magnetic difference; using alternating magnetic field to separate non-ferrous metals; using wind power separation, water power layering for gravity separation to separate light materials from heavy materials; using the difference in electrical conductivity of different materials to realize electrostatic separation.

[0005] However, the current waste treatment management method has the following technical problems:

[0006] In the complete process of waste treatment, sorting as the core pretreatment link directly affects the subsequent recycling efficiency and resource purity. However, the existing method mainly relying on physical separation needs to be optimized. SUMMARY

[0007] Therefore, it is necessary to provide a waste treatment management method, device, computer equipment, computer readable storage medium and computer program product capable of improving the sorting accuracy of waste materials and improving the recycling efficiency and resource conversion rate of waste treatment and recycling.

[0008] In a first aspect, the present application provides a waste treatment management method. The method comprises:

[0009] obtaining detection information obtained by detecting a target object to be processed, the detection information comprising a detection image;

[0010] processing the detection information based on a preset recognition model to obtain structural features of the target object, the structural features comprising structural information of the target object and feature labeling information on the target object;

[0011] determining treatment process parameters for disassembling the target object based on the structural information and the feature labeling information, the treatment process parameters comprising a shear blade gap, a breaking hammer point position, and a component sorting position;

[0012] driving a target device to implement a disassembling process for the target object based on the treatment process parameters.

[0013] In one embodiment, the treatment process parameters for disassembling the target object based on the structural information and the feature labeling information, the treatment process parameters comprising a shear blade gap, a breaking hammer point position, and a component sorting position include:

[0014] determining the number of board layers and the size of the board layers of the target object based on the structural information, and determining the component position information on each component board layer based on the feature labeling information;

[0015] determining a first direction and a second direction corresponding to two axes of a preset plane coordinate system based on the plane coordinate system, obtaining an enclosing body enclosing a target component, the enclosing body being an axis-aligned rectangle with edges parallel to the first direction or the second direction, a preset interval being provided between the edges of the enclosing body and the target component, the preset interval being associated with the type of the target component;

[0016] determining the shear blade gap and the breaking hammer point position based on the distribution information of the enclosing body, the shear path of the shear blade avoiding the enclosing body, the breaking hammer point position avoiding the enclosing body, and the breaking hammer point position being located at the midpoint of the geometric center connecting line of the two enclosing bodies connected after shearing.

[0017] In one of the embodiments, the treatment process parameters for disassembling the target object are determined based on the structural information and the feature annotation information, and the treatment process parameters include a shear blade gap, a breaking hammer point position, and an element sorting position.

[0018] Spectrum imaging data and tomography data of the target object are acquired, and a preset material analysis model is used to process the spectrum imaging data and the tomography data to obtain a material probability matrix of the target object.

[0019] The breaking granularity of the target object is determined based on the material probability matrix, and the shear blade gap is adjusted based on the breaking granularity.

[0020] In one of the embodiments, the method further comprises:

[0021] Sample data including real PCB data and simulated PCB data are acquired, and the sample data are preprocessed to construct a sample data set.

[0022] An initial model is constructed based on a target detection framework and a segmentation model, and the initial model is trained to convergence by using the sample data set to obtain an identification model. The identification model takes detection information of a to-be-detected object as input and outputs the structural features, and the structural features further include a pollution risk indicator and a value indicator of a target element.

[0023] In one of the embodiments, the treatment process parameters for disassembling the target object are determined based on the structural information and the feature annotation information, and the treatment process parameters include a shear blade gap, a breaking hammer point position, and an element sorting position.

[0024] The pollution risk indicator and the value indicator of the target element are determined.

[0025] The disassembly sequence of the target element is determined by weighting based on the pollution risk indicator and the value indicator, wherein the higher the pollution risk indicator is, the higher the disassembly priority is, and the higher the value indicator is, the higher the disassembly priority is.

[0026] In one of the embodiments, after the disassembly processing procedure of the target object is driven by the target equipment based on the treatment process parameters, the method further comprises:

[0027] The disassembly return of the target object is predicted according to the structural features and a preset loss rate to obtain disassembly prediction information.

[0028] Actual return data after the disassembly processing procedure is compared with the disassembly prediction information, and the treatment process parameters are adjusted based on the comparison result.

[0029] In a second aspect, the present application further provides a waste treatment management device. The device comprises:

[0030] a detection module configured to acquire detection information obtained by detecting a target object to be processed, the detection information comprising a detection image;

[0031] an identification module configured to process the detection information based on a preset identification model, and acquire structural features of the target object, the structural features comprising structural information of the target object and feature annotation information on the target object;

[0032] a treatment parameter module configured to determine treatment process parameters for disassembling and processing the target object based on the structural information and the feature annotation information, the treatment process parameters comprising a shearing blade gap, a breaking hammer point position, and an element sorting position;

[0033] a treatment execution module configured to drive a target device to implement a disassembling and processing procedure for the target object based on the treatment process parameters.

[0034] In one embodiment, the treatment parameter module comprises:

[0035] an annotation information module configured to determine the number of board layers and the size of the board layers of the target object based on the structural information, and determine element position information on each element board layer based on the feature annotation information;

[0036] a bounding volume module configured to determine a first direction and a second direction corresponding to two axes of a preset plane coordinate system based on the plane coordinate system, and acquire a bounding volume surrounding a target element, the bounding volume being an axis-aligned rectangle with edges parallel to the first direction or the second direction, a preset interval being provided between the edges of the bounding volume and the target element, the preset interval being associated with the type of the target element;

[0037] a driving parameter module configured to determine the shearing blade gap and the breaking hammer point position based on distribution information of the bounding volume, the shearing path of the shearing blade avoiding the bounding volume, the breaking hammer point position avoiding the bounding volume, and the breaking hammer point position being located at the midpoint of a geometric center connecting line of two bounding volumes connected after shearing.

[0038] In one embodiment, the treatment parameter module comprises:

[0039] a material analysis module configured to acquire spectral imaging data and tomographic scanning data of the target object, process the spectral imaging data and the tomographic scanning data based on a preset material analysis model, and obtain a material probability matrix of the target object;

[0040] a granularity adjustment module configured to determine a crushing granularity of the target object based on the material probability matrix, and adjust the shear blade gap based on the crushing granularity.

[0041] In one of the embodiments, the device further comprises:

[0042] a sample data module configured to obtain sample data, the sample data comprising real PCB data and simulated PCB data, pre-process the sample data, and construct a sample data set;

[0043] a model training module configured to construct an initial model based on a target detection framework and a segmentation model, train the initial model to convergence by applying the sample data set, and obtain an identification model, the identification model taking detection information of a to-be-detected object as input and outputting the structural feature, the structural feature further comprising a pollution risk indicator and a value indicator of a target component.

[0044] In one of the embodiments, the disposal parameter module comprises:

[0045] an indicator data module configured to determine the pollution risk indicator and the value indicator of the target component;

[0046] a component sequencing module configured to determine a disassembly sequence of the target component by weighting based on the pollution risk indicator and the value indicator, wherein the higher the pollution risk indicator, the higher the disassembly priority, and the higher the value indicator, the higher the disassembly priority.

[0047] In one of the embodiments, the disposal execution module further comprises:

[0048] a return prediction module configured to predict a disassembly return of the target object according to the structural feature and a preset loss rate, and obtain disassembly prediction information;

[0049] a feedback adjustment module configured to compare actual return data after the disassembly processing procedure with the disassembly prediction information, and adjust disposal process parameters based on a comparison result.

[0050] In a third aspect, the present application further provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps in the waste processing management method according to any one of the embodiments of the first aspect when executing the computer program.

[0051] In a fourth aspect, the present application also provides a computer readable storage medium. The computer readable storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the steps of the waste treatment management method according to any one of the embodiments of the first aspect.

[0052] In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program, and the computer program, when executed by a processor, implements the steps of the waste treatment management method according to any one of the embodiments of the first aspect.

[0053] The waste treatment management method, device, computer device, storage medium and computer program product described above can achieve the following beneficial effects on the technical problems in the corresponding background art through the technical features in the claims:

[0054] The present application provides a waste treatment management method, which comprises obtaining detection information obtained by detecting a target object to be treated, the detection information comprising a detection image; processing the detection information based on a preset recognition model to obtain structural features of the target object, the structural features comprising structural information of the target object and feature labeling information on the target object; determining disposal process parameters for disassembling and processing the target object based on the structural information and the feature labeling information, the disposal process parameters comprising a shearing blade gap, a breaking hammer point position and an element sorting position; and driving a target device to implement a disassembling and processing procedure for the target object based on the disposal process parameters. In implementation, by obtaining detection information in multiple dimensions, the physical, chemical and other features of the target object are comprehensively captured, overcoming the defects in traditional sorting methods that rely only on a single physical characteristic and cannot identify internal structures or material components, which helps to improve the fineness of sorting control. Subsequently, the three-dimensional topology and material distribution of the target object are determined by processing the detection data in multiple dimensions through the recognition model, compared with traditional manual disassembling methods, the model is used for automatic feature extraction, which helps to accurately identify complex structures, is suitable for electronic waste of different specifications and improves the flexibility of the system. Finally, control parameters are intelligently generated based on the structural information to control the driving parameters of the device corresponding to the current target object, which helps to adjust the disassembling device for the current electronic waste and improves the resource recycling efficiency. In summary, the present application improves the sorting fineness of waste, the recycling efficiency and resource conversion rate of waste treatment and recycling. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained on the basis of these drawings without creative effort.

[0056] Figure 1 An application environment diagram of a waste treatment management method in an embodiment;

[0057] Figure 2 A first flowchart of a waste treatment management method in an embodiment;

[0058] Figure 3 A second flowchart of a waste treatment management method in another embodiment;

[0059] Figure 4 A third flowchart of a waste treatment management method in another embodiment;

[0060] Figure 5 A fourth flowchart of a waste treatment management method in another embodiment;

[0061] Figure 6 A fifth flowchart of a waste treatment management method in another embodiment;

[0062] Figure 7 A sixth flowchart of a waste treatment management method in another embodiment;

[0063] Figure 8 A structural block diagram of a waste treatment management device in an embodiment;

[0064] Figure 9 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0065] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0066] The waste treatment management method provided by the embodiments of the present application can be applied to, for example Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart TV, a smart air conditioner, a smart vehicle device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be a standalone physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0067] In one embodiment, as shown in Figure 2 , a waste treatment management method is provided. Taking the terminal in Figure 1 as an example, the method includes the following steps:

[0068] Step 202: Obtain detection information obtained by detecting a target object to be processed, the detection information including a detection image.

[0069] Exemplarily, the terminal can obtain the detection information of the target object to be processed based on visual sensors, infrared sensors, and other sensing detection devices, such as visible light images of multiple angles of the target object, X-ray imaging (XRT), near-infrared spectrum (NIR), etc. The visible light image is used to obtain the structural appearance information of the target object, the X-ray imaging is used to penetrate the inside of the object and detect the hidden structure of the object, such as multi-layer plate structure, metal welding structure, etc., and the near-infrared spectrum is used to analyze and identify the material composition.

[0070] Step 204: Process the detection information based on a preset recognition model to obtain structural features of the target object.

[0071] Among them, the structural features include structural information of the target object and feature annotation information on the target object. The structural information can refer to the structural parameters of the whole target object, and the feature annotation information can refer to the annotation information of the distributed components, external structures on the target object, such as the type, size, connection relationship, and recycling value of the components.

[0072] Step 206: Determine disposal process parameters for disassembling and processing the target object based on the structural information and the feature annotation information.

[0073] The disposal process parameters include a shearing blade gap, a breaking hammer point position, and an element sorting position. The disposal process parameters are used to drive the execution equipment to perform processing on the target object. The execution equipment can include a hammer crusher, a shearing crusher, an element disintegrator, a copper foil stripping machine, etc. Auxiliary devices such as a conveyor belt, a mechanical arm, a jig, a monitoring feedback device, etc. are also provided in the execution equipment, which will not be described here.

[0074] Step 208: driving a target device to implement a disassembly processing procedure on the target object based on the disposal process parameters.

[0075] In the above waste treatment management method, the technical features in the embodiments are reasonably deduced, and the beneficial effects of solving the technical problems in the background art can be achieved:

[0076] The application provides a waste treatment management method, which includes obtaining detection information obtained by detecting a target object to be processed, the detection information including a detection image; processing the detection information based on a preset recognition model to obtain structural features of the target object, the structural features including structural information of the target object and feature labeling information on the target object; determining disposal process parameters for disassembling and processing the target object based on the structural information and the feature labeling information, the disposal process parameters including a shearing blade gap, a breaking hammer point position, and an element sorting position; and driving a target device to implement a disassembly processing procedure on the target object based on the disposal process parameters. In implementation, by obtaining detection information in multiple dimensions, the physical, chemical, and other features of the target object are comprehensively captured, overcoming the defects of traditional sorting methods that rely only on a single physical characteristic and cannot identify internal structures or material components, which helps to improve the fineness of sorting control. Subsequently, the three-dimensional topology and material distribution of the target object are determined by processing multi-dimensional detection data through a recognition model, compared with traditional manual disassembly methods, the model is used for automatic feature extraction, which helps to accurately identify complex structures, is suitable for different specifications of electronic waste, and improves the flexibility of the system. Finally, control parameters are intelligently generated based on the structural information to control the device driving parameters corresponding to the current target object, which helps to adjust the disassembly equipment for the current electronic waste and improves the resource recycling efficiency. In summary, the present application improves the sorting fineness of waste, improves the recycling efficiency and resource conversion rate of waste treatment and recycling.

[0077] In one embodiment, as shown in Figure 3 The step 206 includes:

[0078] Step 302: determining the number of layers and the size of each layer of the target object based on the structural information, and determining the position information of each component on each component layer based on the feature labeling information.

[0079] Step 304: determining a first direction and a second direction corresponding to two axes of a preset plane coordinate system respectively, and obtaining an enclosing volume surrounding the target component based on the plane coordinate system.

[0080] The enclosing volume is an axis-aligned rectangle with edges parallel to the first direction or the second direction, and a preset interval is provided between the edges of the enclosing volume and the target component, and the preset interval is associated with the type of the target component. The plane coordinate system can be a rectangular coordinate system with a fixed jig plane of the target object as a reference plane.

[0081] For example, the preset interval can be pre-set by a technician, which can be a fixed distance value or a fixed proportion of the size of the component, such as 5%, 10%, etc.

[0082] Step 306: determining the shearing blade gap and the breaking hammer point based on the distribution information of the enclosing volume.

[0083] The shearing path of the shearing blade avoids the enclosing volume, and the breaking hammer point also avoids the enclosing volume, and the breaking hammer point is located at the midpoint of the geometric center line connecting two enclosing volumes connected after shearing.

[0084] For example, the terminal can divide the target object into grids distributed along the X-axis and the Y-axis, mark the grids covered by the enclosing volume as impassable, and finally determine the shortest shearing path in the grids of the target object based on the shortest path algorithm. At this time, the distribution of the shearing line corresponding to the shortest shearing path is the position of the shearing blade.

[0085] In this embodiment, by recognizing the position of the target component and intelligently avoiding the target component, it is helpful to reduce the possibility of damage to the target component during disassembly, which may cause loss of recycling value, and on the other hand, it is also helpful to reduce the possibility of material pollution caused by leakage of electrolyte, pump body, medium liquid, etc.

[0086] In one of the embodiments, as shown in Figure 4 The step 206 includes:

[0087] Step 402: obtaining spectral imaging data and tomographic scanning data of the target object, and processing the spectral imaging data and the tomographic scanning data based on a preset material analysis model to obtain a material probability matrix of the target object.

[0088] The spectral imaging data can obtain information of molecular vibration (such as C-H, O-H, N-H bond) of the material by measuring the absorption or reflection spectrum of the material in the near-infrared band (usually 780-2500 nm). The tomography data can refer to the three-dimensional structure (such as pore, density distribution) inside the material by penetrating the material with X-rays and obtaining projection data at different angles. The gray value reflects the linear attenuation coefficient of the material, which is related to the density and atomic number.

[0089] Exemplarily, the terminal can input the CNN as two independent channels with NIR (spectral) and XRT (image), the model can extract chemical and structural features respectively, and then combine them through a fusion layer to generate a material probability matrix, which represents the probability of each pixel / region belonging to different material categories, and output a multi-classification probability.

[0090] Step 404: determining the crushing granularity of the target object based on the material probability matrix, and adjusting the shear blade gap based on the crushing granularity.

[0091] Exemplarily, if a high-value material (such as copper) concentration area is detected, a coarse granularity crushing is used to reduce metal loss; if a mixed impurity area is detected, a fine granularity crushing is used to improve the sorting purity. Correspondingly, the smaller the blade gap → the finer the crushing granularity (suitable for hard materials); the larger the blade gap → the coarser the crushing granularity (suitable for soft or brittle materials).

[0092] In this embodiment, the crushing granularity of the target object is intelligently determined through material analysis, which helps to realize fine crushing control, reduce the possibility of over-crushing or under-crushing, improve the recovery efficiency of high-value metals, and thus improve the sorting efficiency.

[0093] In one of the embodiments, as shown in Figure 5 the method further comprises:

[0094] Step 502: obtaining sample data, the sample data including real PCB data, simulated PCB data, and pre-processing the sample data to construct a sample data set.

[0095] Exemplarily, the sample data can include images and reports obtained in real PCB disassembly scenes, public data sets, and synthetic simulation data. The terminal can pre-process the sample data, such as data labeling, data enhancement, etc. The terminal can perform geometric transformation, light interference, and defect synthesis on the sample image to realize the expansion of the sample data.

[0096] Step 504: based on the target detection framework and the segmentation model, an initial model is constructed, and the initial model is trained to convergence by applying the sample data set to obtain a recognition model, the recognition model taking detection information of a to-be-detected object as input and outputting the structural feature, the structural feature further including a pollution risk indicator and a value indicator of the target element.

[0097] Exemplarily, the terminal can add an FPN (Feature Pyramid Network) to process devices of different sizes (such as 0402 chip resistors vs. large electrolytic capacitors), and can also introduce a CBAM or SE module to enhance the focusing ability on small devices.

[0098] In this embodiment, by collecting diversified labeled images, the robustness of the model is enhanced, a detection / segmentation framework is selected, multi-scale processing is improved, and finally the recognition model is obtained, which helps to improve the recognition and analysis efficiency and effect of the target object.

[0099] In one of the embodiments, as shown in Figure 6 The step 206 includes:

[0100] Step 602: determining the pollution risk indicator and the value indicator of the target element.

[0101] Exemplarily, the components have a pollution risk due to the presence of toxic substances such as lead and mercury, electrolyte leakage, and the risk of breaking of glass, ceramic, etc. A quantitative pollution risk indicator can be determined according to the following formula: HS (risk indicator) = w1 toxicity + w2 volatility + w3 physical harm, where w1, w2, and w3 are the preset weights of each indicator. The value indicator is quantifiable due to the recycling value of precious metals and the reusable characteristics of the components. A quantitative value indicator can be determined according to the following formula: VS (value indicator) = w4 metal content + w5 rarity + w6 reusability, where w4, w5, and w6 are the preset weights of each indicator.

[0102] Step 604: based on the pollution risk indicator and the value indicator, the disassembly order of the target element is determined by weighting, wherein the higher the pollution risk indicator, the higher the disassembly priority, and the higher the value indicator, the higher the disassembly priority.

[0103] In this embodiment, the disassembly priority is adjusted based on the value indicator and the pollution risk indicator, which helps to preferentially disassemble high-value and high-pollution-risk components, thereby preferentially obtaining high-value components and preferentially disposing high-pollution-risk components, and helps to improve the safety and recycling efficiency of disassembly control.

[0104] In one of the embodiments, as shown in Figure 7 The step 208 further includes:

[0105] Step 702: predicting the disassembly return of the target object according to the structural features and a preset loss rate, to obtain disassembly prediction information.

[0106] The loss rate can be determined according to historical metal crushing loss rate, plastic pollution loss rate, etc.

[0107] Step 704: comparing the actual return data after the disassembly processing procedure with the disassembly prediction information, and adjusting the disposal process parameters based on the comparison result.

[0108] For example, the terminal can adjust the disposal process parameters of electronic waste of the same model or similar disassembly process according to the feedback comparison result, such as adjusting the crushing particle size, adjusting the leaching time, etc.

[0109] In this embodiment, the disassembly disposal process parameters are adjusted based on the actual disassembly return data, which helps to optimize the disassembly process and improve the disassembly and recycling efficiency.

[0110] It should be understood that although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0111] Based on the same inventive concept, the embodiments of the present application also provide a waste treatment management device for implementing the above-mentioned waste treatment management method. The problem-solving implementation scheme provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more waste treatment management device embodiments provided below can refer to the limitations of the waste treatment management method described above, and will not be repeated here.

[0112] In one embodiment, as shown in Figure 8 a waste treatment management device is provided, comprising: a detection module, an identification module, a disposal parameter module and a disposal execution module, wherein:

[0113] A detection module is configured to acquire detection information obtained by detecting the target object to be processed, the detection information including a detection image;

[0114] An identification module is configured to process the detection information based on a preset identification model, and acquire structural features of the target object, the structural features including structural information of the target object and feature annotation information on the target object;

[0115] A treatment parameter module is configured to determine treatment flow parameters for disassembling the target object based on the structural information and the feature annotation information, the treatment flow parameters including a shear blade gap, a breaking hammer point, and an element sorting position;

[0116] A treatment execution module is configured to drive a target device to implement a disassembling treatment process for the target object based on the treatment flow parameters.

[0117] In one of the embodiments, the treatment parameter module includes:

[0118] An annotation information module is configured to determine the number of board layers and the size of the board layers of the target object based on the structural information, and determine element position information on each element board layer based on the feature annotation information;

[0119] A bounding volume module is configured to determine a first direction and a second direction corresponding to two axes of a preset plane coordinate system based on the plane coordinate system, and acquire a bounding volume surrounding a target element, the bounding volume being an axis-aligned rectangle with edges parallel to the first direction or the second direction, a preset interval being provided between the edges of the bounding volume and the target element, the preset interval being associated with the type of the target element;

[0120] A driving parameter module is configured to determine the shear blade gap and the breaking hammer point based on distribution information of the bounding volume, the shear path of the shear blade avoiding the bounding volume, the breaking hammer point avoiding the bounding volume, and the breaking hammer point being located at the midpoint of a geometric center connecting line of two bounding volumes connected after shearing.

[0121] In one of the embodiments, the treatment parameter module includes:

[0122] A material analysis module is configured to acquire spectral imaging data and tomographic scanning data of the target object, process the spectral imaging data and the tomographic scanning data based on a preset material analysis model, and obtain a material probability matrix of the target object;

[0123] A granularity adjustment module is configured to determine a breaking granularity of the target object based on the material probability matrix, and adjust the shear blade gap based on the breaking granularity.

[0124] In one of the embodiments, the device further comprises:

[0125] a sample data module, configured to acquire sample data, the sample data comprising real PCB data and simulation PCB data, pre-process the sample data, and construct a sample data set;

[0126] a model training module, configured to construct an initial model based on a target detection framework and a segmentation model, train the initial model to convergence by applying the sample data set, and obtain an identification model, the identification model taking detection information of a to-be-detected object as input and outputting the structural feature, the structural feature further comprising a pollution risk index and a value index of a target component.

[0127] In one of the embodiments, the disposal parameter module comprises:

[0128] an index data module, configured to determine the pollution risk index and the value index of the target component;

[0129] a component sequencing module, configured to determine a disassembly sequence of the target component based on the pollution risk index and the value index, wherein the higher the pollution risk index, the higher the disassembly priority, and the higher the value index, the higher the disassembly priority.

[0130] In one of the embodiments, the disposal execution module further comprises:

[0131] a return prediction module, configured to predict a disassembly return of the target object according to the structural feature and a preset loss rate, and obtain disassembly prediction information;

[0132] a feedback adjustment module, configured to compare actual return data after the disassembly processing procedure with the disassembly prediction information, and adjust disposal process parameters based on a comparison result.

[0133] The above various modules in the waste treatment management device can be all or partially realized by software, hardware, and a combination thereof. The above various modules can be embedded in or independent of a processor in a computer device in a hardware form, or can be stored in a memory in the computer device in a software form, so as to be called and executed by a processor to perform operations corresponding to the above various modules.

[0134] In one of the embodiments, a computer device is provided, which can be a terminal, and an internal structure diagram of the computer device can be as shown in Figure 9The computer device shown in the figure includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by the processor to realize a waste treatment management method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0135] Those skilled in the art can understand that, Figure 9 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0136] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in each of the above method embodiments.

[0137] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to realize the steps in each of the above method embodiments.

[0138] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to realize the steps in each of the above method embodiments.

[0139] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of the country and region.

[0140] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of each method can be included. In the embodiments provided in the present application, any reference to memory, database or other medium can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0141] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0142] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A waste treatment management method characterized by, The method comprises: obtaining detection information obtained by detecting a target object to be processed, the detection information comprising a detection image; processing the detection information based on a preset recognition model to obtain structural features of the target object, the structural features comprising structural information of the target object and feature labeling information on the target object; determining treatment process parameters for disassembling the target object based on the structural information and the feature labeling information, the treatment process parameters comprising a shearing blade gap, a breaking hammer point position and a component sorting position; driving a target device to implement a disassembling process of the target object based on the treatment process parameters.

2. The method of claim 1, wherein, The treatment process parameters for disassembling the target object based on the structural information and the feature labeling information, the treatment process parameters comprising a shearing blade gap, a breaking hammer point position and a component sorting position comprise: determining the number of board layers and the size of the board layers of the target object based on the structural information, and determining the position information of components on each component board layer based on the feature labeling information; determining a first direction and a second direction corresponding to two axes of a preset plane coordinate system based on the plane coordinate system, obtaining an enclosing body surrounding a target component, the enclosing body being an axis-aligned rectangle with edges parallel to the first direction or the second direction, a preset interval being provided between the edges of the enclosing body and the target component, the preset interval being associated with the type of the target component; determining the shearing blade gap and the breaking hammer point position based on the distribution information of the enclosing body, the shearing path of the shearing blade avoiding the enclosing body, the breaking hammer point position avoiding the enclosing body, and the breaking hammer point position being located at the midpoint of the geometric center connecting line of two enclosing bodies connected after shearing.

3. The method of claim 1, wherein, The treatment process parameters for disassembling the target object based on the structural information and the feature labeling information, the treatment process parameters comprising a shearing blade gap, a breaking hammer point position and a component sorting position comprise: obtaining spectral imaging data and tomographic scanning data of the target object, processing the spectral imaging data and the tomographic scanning data based on a preset material analysis model to obtain a material probability matrix of the target object; determining a breaking granularity of the target object based on the material probability matrix, and adjusting the shearing blade gap based on the breaking granularity.

4. The method of claim 1, wherein, The method further comprises: obtaining sample data, the sample data comprising real PCB data and simulated PCB data, pre-processing the sample data to construct a sample data set; constructing an initial model based on a target detection framework and a segmentation model, training the initial model to convergence by applying the sample data set to obtain a recognition model, the recognition model taking detection information of an object to be detected as input and outputting the structural features, the structural features further comprising a pollution risk indicator and a value indicator of a target component.

5. The method of claim 4, wherein, The treatment process parameters for disassembling the target object are determined based on the structure information and the feature labeling information, and the treatment process parameters include a shear blade gap, a breaking hammer point position, and an element sorting position. The pollution risk indicator and the value indicator of the target element are determined. Based on the pollution risk indicator and the value indicator, the disassembly order of the target element is determined by weighting, wherein the higher the pollution risk indicator, the higher the disassembly priority, and the higher the value indicator, the higher the disassembly priority.

6. The method of claim 1, wherein, After the disassembly process of the target object is driven by the treatment process parameters, the method further includes: According to the structure feature and the preset loss rate, the disassembly return of the target object is predicted to obtain disassembly prediction information. The actual return data after the disassembly process is compared with the disassembly prediction information, and the treatment process parameters are adjusted based on the comparison result.

7. A waste treatment management apparatus, characterized by, The device includes: A detection module configured to obtain detection information obtained by detecting a target object to be processed, the detection information including a detection image; An identification module configured to process the detection information based on a preset identification model to obtain structure features of the target object, the structure features including structure information of the target object and feature labeling information on the target object; A treatment parameter module configured to determine treatment process parameters for disassembling the target object based on the structure information and the feature labeling information, the treatment process parameters including a shear blade gap, a breaking hammer point position, and an element sorting position; A treatment execution module configured to drive a target device to implement a disassembly process of the target object based on the treatment process parameters.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

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