Equipment parameter adjusting method and equipment based on intelligent aggregate classification

Abstract

The invention relates to the technical field of asphalt mixture recovery, in particular to an equipment parameter adjusting method and equipment based on intelligent aggregate classification. The invention discloses an equipment parameter adjusting method based on intelligent aggregate classification. According to the method, an image sampling assembly is installed on aggregate regeneration equipment, aggregate types and flow are monitored in real time, data analysis and classification are carried out in combination with a deep learning algorithm, the aggregate types are intelligently recognized, and crushing power and feeding flow are dynamically adjusted according to real-time monitoring results. According to the method, the type of the aggregate can be identified in real time, the content of false particles and gray materials in the regenerated aggregate can be accurately judged, equipment parameters are automatically adjusted, the quality and production efficiency of the regenerated aggregate are improved, and the problems of grading loss, grading error and the like caused by the change of flow and water content in the traditional asphalt regeneration process are solved.

Description

Technical Field

[0001] The invention relates to the technical field of asphalt mixture recycling, and in particular to an equipment parameter adjustment method based on intelligent aggregate classification and equipment thereof. Background Art

[0002] The processing of recycled asphalt mixture is a key link in asphalt pavement regeneration technology. Its core lies in effectively breaking up pseudo particles through the stripping machine, fully stripping the aggregate and asphalt in the mixture, so as to achieve the recycling of aggregate. However, in the actual regeneration process, multiple factors such as the moisture content of aggregate and feed flow rate will significantly affect the regeneration effect. Specifically, when the frequency of the crushing machine increases or the feed flow rate decreases, the aggregate may be over-crushed, resulting in grading loss of the finished material; when the moisture content is high or the feed flow rate is too large, the aggregate may not be fully crushed, and the pseudo particles are not effectively removed. These problems directly affect the overall regeneration effect of the regeneration equipment, resulting in unstable performance of the recycled product, which in turn affects the durability and service life of the pavement.

[0003] At present, the solutions to these problems mainly rely on manual intervention or simple image processing technology, lacking real-time and accurate detection of flow and aggregate type, which makes it impossible for the equipment to dynamically adjust according to material changes, resulting in fluctuations in the quality of the finished product. Therefore, a new type of intelligent detection method is urgently needed to meet the challenges faced in the processing of recycled asphalt mixtures, so as to improve the quality and production efficiency of recycled aggregates. Summary of the invention

[0004] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description and other drawings of the description.

[0005] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide an equipment parameter adjustment method and equipment based on intelligent aggregate classification. By installing an image sampling component on the aggregate regeneration equipment, the type and flow of the mixed material are monitored in real time, and data analysis and classification are performed in combination with a deep learning algorithm to ensure that normal aggregates, incompletely crushed false particles, over-crushed white materials and other types of particles can be accurately identified during the regeneration process; a closed-loop feedback control mechanism is adopted to dynamically adjust the crushing power and feed flow of the stripping host according to the real-time monitoring results, optimize the production process, avoid excessive or insufficient crushing, and improve the performance and stability of the recycled aggregate; compared with the prior art, the present invention can identify the type of particles in real time, accurately judge the content of false particles and white materials in the regenerated particles, and automatically adjust the equipment parameters to improve the quality and production efficiency of the recycled aggregate, and solve the problems of grading loss and grading error caused by changes in flow and moisture content in the traditional asphalt regeneration process.

[0006] The present invention provides a method for adjusting equipment parameters based on intelligent aggregate classification and equipment thereof, comprising: S1. During the process of recycled materials in asphalt mixture being recycled by aggregate recycling equipment, the image sampling component is used to sample the image of recycled material particles, and the collected images are sent to the computer processing end. At the computer processing end, the collected images are manually annotated using labelme software, different types of particle labels are added, and a data set is established; S2. Build a Mask RCNN instance segmentation network to segment different types of collected particle images, use the characteristics of different particle samples in the data set for supervised learning, and establish a mature intelligent aggregate classification model after deep learning; S3. Use a mature intelligent aggregate classification model to monitor the recycled material particles in the equipment in real time to determine the current particle size distribution and whether it meets the crushing standards; In steps S4 and S1, the particle image is obtained while the current operating rate of the aggregate regeneration equipment is collected. The current particle flow is calculated and obtained in combination with the image sampling component. A control system is established and a closed-loop feedback mechanism is adopted. According to the particle gradation detected in step S3, the operating rate of the aggregate regeneration equipment is automatically adjusted to prevent imbalance in aggregate gradation.

[0007] In some embodiments, in step S1, the image sampling component includes an industrial camera, a depth camera, and a fill light LED lamp, and the sampling frequency is 10-15 frames per second.

[0008] In some embodiments, the aggregate regeneration equipment includes a variable frequency belt conveyor, a finished product belt conveyor and a return material belt conveyor. The image sampling component is installed on the variable frequency belt conveyor, the finished product belt conveyor and the return material belt conveyor for real-time monitoring of the type of granular material. The current height of the granular material is measured by a depth camera and the current flow rate of the granular material is calculated in combination with the feed speed.

[0009] In some embodiments, in step S1, the material particle labels specifically include: normal aggregate, incompletely crushed pseudo particles, and over-crushed white material.

[0010] In some embodiments, in step S2, the characteristics of different material particle samples in the data set specifically include color, size, ratio of major axis to minor axis of equivalent ellipse, irregularity, shape factor and convexity.

[0011] In some embodiments, in step S3, the specific segmentation processing of the Mask RCNN instance segmentation network includes: the original image first passes through a convolutional neural network including a residual layer and multi-scale feature fusion to fully extract the original features in the image, and then finds the image area that may contain the target in the region proposal network and generates a detection box, enters the fully connected layer to determine the position and category of each target through classification and recursion, and finally performs instance segmentation.

[0012] In some embodiments, during the model training process of the Mask RCNN instance segmentation network, data augmentation technology is applied to generate diverse training samples through random cropping, rotation, scaling and color jittering, and category loss, bounding box regression loss and Mask loss are used as the total loss function during training.

[0013] In some embodiments, the aggregate regeneration equipment further includes a stripping main machine for crushing asphalt mixtures. According to the real-time monitoring results obtained in step S3, a closed-loop feedback mechanism is used to automatically adjust the power of the stripping main machine and the speed of the variable frequency belt conveyor.

[0014] A device for adjusting device parameters based on intelligent aggregate classification includes an aggregate regeneration device, the aggregate regeneration device including: A recycling silo, which is used to store asphalt mixture that needs to be recycled; The stripping machine is used to break up the pseudo particles in the asphalt mixture, strip the asphalt, and obtain the recycled material particles; A variable frequency belt conveyor, a feeding belt conveyor and an elevator, which are used to convey the asphalt mixture to the stripping main machine; A screening component is arranged behind the stripping main machine and is used to screen the recycled material particles to obtain aggregates of different grades; The return conveyor is used to convey the aggregates that do not meet the finished product standards back to the feeding conveyor; Finished product conveyor, which is used to output aggregates that meet finished product standards; An image sampling component is arranged on the variable frequency belt conveyor, the finished product belt conveyor and the return material belt conveyor, and the image sampling component specifically includes an industrial camera, a depth camera and a fill light LED lamp; The computer processing end is used to receive data from the image sampling component and control the speed of the stripping host and the variable frequency belt conveyor.

[0015] In some embodiments, the aggregate regeneration equipment also includes a dust removal system arranged between the stripping main unit and the screening component, the screening component includes a vibrating screen for initial screening of aggregate particles larger than 10 mm and a high-frequency screen for rescreening, and the finished product belt conveyor specifically includes a 0-3mm output belt conveyor, a 3-5mm output belt conveyor and a 5-10mm output belt conveyor.

[0016] By adopting the above technical solution, the beneficial effects of the present invention are: The present invention installs an image sampling component on the aggregate regeneration equipment to monitor the type and flow of the mixed material in real time, and combines the deep learning algorithm to perform data analysis and classification to ensure that various types of aggregates, such as normal aggregates, incompletely crushed false particles, and over-crushed white materials, can be accurately identified during the regeneration process; a closed-loop feedback control mechanism is adopted to dynamically adjust the crushing power and feed flow of the stripping host according to the real-time monitoring results, optimize the production process, avoid excessive or insufficient crushing, and improve the performance and stability of the recycled aggregate; compared with the prior art, the present invention can identify the type of aggregate in real time, accurately determine the content of false particles and white materials in the regenerated aggregates, and automatically adjust the equipment parameters to improve the quality and production efficiency of the recycled aggregate, and solve the problems of grading loss and grading error caused by changes in flow and moisture content in the traditional asphalt regeneration process.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

[0018] Undoubtedly, these and other objects of the present invention will become more apparent after the following detailed description of the preferred embodiment described with reference to various figures and drawings.

[0019] In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, one or several preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation of the present invention.

[0021] In the drawings, the same reference numerals are used for the same components and the drawings are schematic and not necessarily drawn to scale.

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only one or several embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on such drawings without paying creative work.

[0023] Figure 1 It is a schematic diagram of the overall structure of aggregate regeneration equipment in some embodiments of the present invention; Figure 2 The figure is a schematic diagram of the overall flow of a method for adjusting equipment parameters based on intelligent aggregate classification in some embodiments of the present invention.

[0024] Description of main reference numerals: 1. Regeneration silo; 2. Elevator; 3. Peeling machine; 4. Dust removal system; 5. Return belt conveyor; 6. 0-3mm output belt conveyor; 7. 3-5mm output belt conveyor; 8. 5-10mm output belt conveyor; 9. High frequency screen; 10. Vibrating screen; 11. Feeding belt conveyor; 12. Frequency conversion belt conveyor. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but are not used to limit the present invention.

[0026] In addition, in the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0027] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral body; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. However, if it is indicated as a direct connection, it means that the two connected bodies are not connected through a transition structure, but are connected to form a whole through a connecting structure. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0028] In the present invention, unless otherwise clearly specified and limited, the first feature "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0029] Reference Figure 1 and Figure 2 , Figure 1 It is a schematic diagram of the overall structure of aggregate regeneration equipment in some embodiments of the present invention; Figure 2 The figure is a schematic diagram of the overall flow of a method for adjusting equipment parameters based on intelligent aggregate classification in some embodiments of the present invention.

[0030] A method for adjusting equipment parameters based on intelligent aggregate classification and equipment thereof, comprising: S1. During the recycling process of the recycled materials in the asphalt mixture by the aggregate recycling equipment, the image sampling component is used to sample the image of the recycled material particles, and the collected images are sent to the computer processing end. The collected images are manually annotated by using the labelme software at the computer processing end, and different types of particle labels are added to establish a data set. The particle labels specifically include: normal aggregate, incompletely crushed false particles, and over-crushed white materials; Specifically, the image sampling component includes an industrial camera, a depth camera and a fill light LED lamp, with a sampling frequency of 10-15 frames per second; the current image sampling component can accurately capture the shape, color and height information of the material under different lighting conditions, using a resolution of at least 4800 pixels to ensure that the detailed features of the granular material are captured, the sampling frequency adapts to the current conveyor belt speed, and the configured fill light LED lamp can reduce the interference of shadows and light spots, further improving the image quality; The aggregate recycling equipment includes a variable frequency belt conveyor 12, a finished product belt conveyor and a return material belt conveyor 5. The image sampling component is installed on the variable frequency belt conveyor 12, the finished product belt conveyor and the return material belt conveyor 5 to monitor the type of granular materials in real time. The current height of the granular materials is measured by a depth camera and the current flow rate of the granular materials is calculated in combination with the feed speed. The specific calculation formula for the current flow rate of the granular materials is:

[0031] Among them, Q is the current particle flow rate, v is the feed rate of the regeneration silo 1, and h 1 is the original height of the depth camera from the belt, h 2 The current height detected by the depth camera in real time.

[0032] S2. Build a Mask RCNN instance segmentation network to segment different types of collected particle images, use the characteristics of different particle samples in the data set for supervised learning, and establish a mature intelligent aggregate classification model after deep learning; The characteristics of different particle samples in the data set specifically include color, size, ratio of the major and minor axes of the equivalent ellipse, irregularity, shape factor, and convexity; The specific segmentation processing of the Mask RCNN instance segmentation network includes: the original image first passes through a convolutional neural network containing a residual layer and multi-scale feature fusion to fully extract the original features in the image, then finds the image area that may contain the target in the region proposal network and generates a detection box, enters the fully connected layer to determine the position and category of each target through classification and recursion, and finally performs instance segmentation; During the model training process of the Mask RCNN instance segmentation network, data augmentation technology is applied to generate diverse training samples through random cropping, rotation, scaling and color jittering to improve the generalization ability of the model. Category loss, bounding box regression loss and Mask loss are used as the total loss function during training.

[0033] S3. Use a mature intelligent aggregate classification model to monitor the recycled material particles in the equipment in real time to determine the current particle size distribution and whether it meets the crushing standards; When the aggregate recycling equipment is running, the image sampling component continuously captures the material image on the conveyor belt. After the real-time image is normalized and image segmentation preprocessed, the segmented image is input into the trained Mask RCNN model for real-time recognition of the type of aggregate. The system can intelligently determine the proportion of abnormal materials such as false particles and white materials in the current flow based on the recognition results, and record the power of the stripping host 3. In the feedback mechanism, the system feeds back the real-time detection results to the computer processing end to monitor the current material flow and changes in the proportion of aggregates.

[0034] In steps S4 and S1, the operating speed of the aggregate regeneration equipment at the current moment is collected while obtaining the particle image, and the current particle flow is calculated and obtained in combination with the image sampling component, and a control system is established. A closed-loop feedback mechanism is adopted to automatically adjust the operating speed of the aggregate regeneration equipment according to the particle gradation detected in step S3 to prevent imbalance in aggregate gradation; The aggregate recycling equipment also includes a stripping host 3 for crushing asphalt mixture. According to the real-time monitoring results obtained in step S3, a closed-loop feedback mechanism is adopted to automatically adjust the power of the stripping host 3 and the speed of the variable frequency belt conveyor 12; the closed-loop feedback control mechanism is adopted to dynamically optimize the crushing and screening process according to the real-time detected data to ensure that the recycled aggregate meets the design standards; By monitoring the height and speed of the material on the variable frequency belt conveyor 12, the flow volume is calculated in real time. According to the detected flow and type of granules, the power of the stripping host 3 is automatically adjusted. When the flow is large and the proportion of false particles is high, the crushing power is increased to ensure that the false particles are fully broken up; otherwise, the power is reduced to prevent excessive crushing. The speed of the variable frequency belt conveyor 12 in the equipment will also be adjusted to meet the processing requirements under different flow rates. The computer processing end can also provide a friendly user interface to display the current test results, equipment status and alarm information in real time, allowing the operator to adjust the equipment settings at any time.

[0035] like Figure 1 As shown, a device for adjusting device parameters based on intelligent aggregate classification includes an aggregate regeneration device, and the aggregate regeneration device includes: A regeneration silo 1, which is used to store asphalt mixture that needs to be regenerated; The stripping host 3 is used to break up the pseudo particles in the asphalt mixture, strip the asphalt, and obtain the recycled material particles; The variable frequency belt conveyor 12, the feeding belt conveyor 11 and the elevator 2 are used to convey the asphalt mixture to the stripping main machine 3; A screening component, which is arranged behind the stripping main unit 3 and is used to screen the recycled material particles to obtain aggregates of different grades; The return belt conveyor 5 is used to convey the aggregates that do not meet the finished product standards back to the feeding belt conveyor 11; Finished product conveyor, which is used to output aggregates that meet finished product standards; An image sampling component is arranged on the variable frequency belt conveyor 12, the finished product belt conveyor and the return material belt conveyor 5, and the image sampling component specifically includes an industrial camera, a depth camera and a fill light LED lamp; The computer processing end is used to receive data from the image sampling component and control the speed of the stripping host 3 and the variable frequency belt conveyor 12.

[0036] The aggregate regeneration equipment also includes a dust removal system 4 arranged between the stripping host 3 and the screening component, the screening component includes a vibrating screen 10 for primary screening of aggregate particles larger than 10 mm and a high-frequency screen 9 for secondary screening, and the finished product belt conveyor specifically includes a 0-3 mm output belt conveyor 6, a 3-5 mm output belt conveyor 7 and a 5-10 mm output belt conveyor 8.

[0037] Asphalt mixture is stored in the recycled material bin 1, and is transported to the stripping host 3 through the frequency conversion belt conveyor 12, the feeding belt conveyor 11 and the elevator 2. Aggregates larger than 10-10mmm are preliminarily screened through the vibrating screen 10, and then the aggregates below 10mm are finely screened and divided into aggregate grades through the high-frequency screen 9. Specifically, the grading of aggregates can be divided into 0-3mm, 3-5mm, 5-10mm, 10-16mm, 16-25mm and above 25mm according to the particle size. Among them, the finished materials below 25mm are output through the finished product belt conveyor, and the over-limit materials above 25mm do not meet the finished product standards and will be returned to the feeding belt conveyor 11 through the return belt conveyor 5.

[0038] It should be understood that the embodiments disclosed in the present invention are not limited to the specific processing steps or materials disclosed herein, but should be extended to equivalent substitutions of such features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0039] The "embodiment" mentioned in the specification means that the specific features or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0040] In addition, the described features or characteristics may be combined in one or more embodiments in any other suitable manner. In the above description, some specific details, such as thickness, quantity, etc., are provided to provide a comprehensive understanding of the embodiments of the present invention. However, those skilled in the relevant art will understand that the present invention can be implemented without one or more of the above specific details or can also be implemented using other methods, components, materials, etc.

Claims

1. A method for adjusting equipment parameters based on intelligent aggregate classification, It is characterized in that include: S1. During the process of recycled materials in asphalt mixture being recycled by aggregate recycling equipment, the image sampling component is used to sample the image of recycled material particles, and the collected images are sent to the computer processing end. At the computer processing end, the collected images are manually annotated using labelme software, different types of particle labels are added, and a data set is established; S2. Build a Mask RCNN instance segmentation network to segment different types of collected particle images, use the characteristics of different particle samples in the data set for supervised learning, and establish a mature intelligent aggregate classification model after deep learning; S3. Use a mature intelligent aggregate classification model to monitor the recycled material particles in the equipment in real time to determine the current particle size distribution and whether it meets the crushing standards; In steps S4 and S1, the particle image is obtained while the current operating rate of the aggregate regeneration equipment is collected. The current particle flow is calculated and obtained in combination with the image sampling component. A control system is established and a closed-loop feedback mechanism is adopted. According to the particle gradation detected in step S3, the operating rate of the aggregate regeneration equipment is automatically adjusted to prevent imbalance in aggregate gradation.

2. The equipment parameter adjustment method based on intelligent aggregate classification according to claim 1, It is characterized in that In step S1, the image sampling component includes an industrial camera, a depth camera and a fill light LED, and the sampling frequency is 10-15 frames per second.

3. The equipment parameter adjustment method based on intelligent aggregate classification according to claim 2, It is characterized in that The aggregate recycling equipment includes a variable frequency belt conveyor, a finished product belt conveyor and a return material belt conveyor. The image sampling component is installed on the variable frequency belt conveyor, the finished product belt conveyor and the return material belt conveyor for sampling and real-time monitoring of the type of granular materials. The current height of the granular materials is measured by a depth camera and the current flow rate of the granular materials is calculated in combination with the feed speed.

4. The equipment parameter adjustment method based on intelligent aggregate classification according to claim 1, It is characterized in that In step S1, the material particle labels specifically include: normal aggregate, incompletely crushed false particles, and over-crushed white materials.

5. The equipment parameter adjustment method based on intelligent aggregate classification according to claim 1, It is characterized in that In step S2, the characteristics of different particle samples in the data set specifically include color, size, ratio of the major and minor axes of the equivalent ellipse, irregularity, shape factor, and convexity.

6. The equipment parameter adjustment method based on intelligent aggregate classification according to claim 1, It is characterized in that In step S3, the specific segmentation processing of the Mask RCNN instance segmentation network includes: the original image first passes through a convolutional neural network that includes a residual layer and multi-scale feature fusion to fully extract the original features in the image, then finds the image area that may contain the target in the region proposal network and generates a detection box, enters the fully connected layer to determine the position and category of each target through classification and recursion, and finally performs instance segmentation.

7. The equipment parameter adjustment method based on intelligent aggregate classification according to claim 1, It is characterized in that During the model training process of the MaskRCNN instance segmentation network, data augmentation technology is applied to generate diverse training samples through random cropping, rotation, scaling and color jittering. Category loss, bounding box regression loss and Mask loss are used as the total loss function during training.

8. The equipment parameter adjustment method based on intelligent aggregate classification according to claim 3, It is characterized in that The aggregate recycling equipment also includes a stripping main machine for crushing asphalt mixture. According to the real-time monitoring results obtained in step S3, a closed-loop feedback mechanism is adopted to automatically adjust the power of the stripping main machine and the speed of the variable frequency belt conveyor.

9. A device for adjusting device parameters based on intelligent aggregate classification, It is characterized in that The invention comprises an aggregate recycling device, wherein the aggregate recycling device comprises: A recycling silo, which is used to store asphalt mixture that needs to be recycled; The stripping machine is used to break up the pseudo particles in the asphalt mixture, strip the asphalt, and obtain the recycled material particles; A variable frequency belt conveyor, a feeding belt conveyor and an elevator, which are used to convey the asphalt mixture to the stripping main machine; A screening component is arranged behind the stripping main machine and is used to screen the recycled material particles to obtain aggregates of different grades; The return conveyor is used to convey the aggregates that do not meet the finished product standards back to the feeding conveyor; Finished product conveyor, which is used to output aggregates that meet finished product standards; An image sampling component is arranged on the variable frequency belt conveyor, the finished product belt conveyor and the return material belt conveyor, and the image sampling component specifically includes an industrial camera, a depth camera and a fill light LED lamp; The computer processing end is used to receive data from the image sampling component and control the speed of the stripping host and the variable frequency belt conveyor.

10. The device according to claim 9, wherein the device parameter adjustment method based on intelligent aggregate classification is It is characterized in that The aggregate regeneration equipment also includes a dust removal system arranged between the stripping main unit and the screening component, the screening component includes a vibrating screen for initial screening of aggregate particles larger than 10 mm and a high-frequency screen for secondary screening, and the finished product belt conveyor specifically includes a 0-3 mm output belt conveyor, a 3-5 mm output belt conveyor and a 5-10 mm output belt conveyor.

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