Construction waste resource recycling system and method

Abstract

The invention provides a construction waste resource recycling system and method. The classification particle size of construction waste during primary sorting is determined according to the shape characteristics of the construction waste subjected to crushing treatment; carrying out vibration sorting on the crushed construction wastes by using graded particle sizes to obtain sorting resources with different particle sizes; the spectrum sensors are arranged along the conveying belt in a segmented mode according to the particle size fractions, then differential imaging is conducted on the sorting resources of all the particle size fractions, spectrum images of the sorting resources of all the particle size fractions are obtained, and component difference characteristics of the sorting resources of all the particle size fractions are extracted from all the spectrum images; and the grading granularity of secondary sorting of the sorting resources is subjected to cooperative constraint through the correlation degree between the difference characteristics of all the components and the grading screen residue, the constraint condition of the grading granularity in secondary sorting is obtained, and then the constraint condition is used for secondary sorting of the to-be-treated construction waste resources. Based on the scheme, multi-stage particle size sorting of construction waste resources can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of spectral visual analysis, and more specifically, to a system and method for recycling construction waste resources. Background Art

[0002] The recycling of construction waste resources is an important measure to achieve sustainable development. By sorting, crushing, screening and other treatments of construction waste, it can be converted into recycled aggregates for the production of recycled concrete, recycled bricks, road base materials, etc. This can not only reduce the pollution of the environment by construction waste and reduce land occupation, but also save natural sand and gravel resources and reduce the production cost of building materials.

[0003] Traditional methods have poor sorting effects on fine-grained materials (such as powders and light organic matter). Fine-grained materials are easy to pass through the screen during traditional vibration screening, causing them to mix with coarse-grained materials and making it difficult to achieve effective separation. At the same time, light organic matter (such as plastics and wood fragments) in fine-grained materials are similar to powders (such as concrete powder) in physical properties, and traditional screening methods cannot distinguish them, resulting in a large amount of resource-recyclable materials being mistakenly landfilled or discarded. In addition, fine-grained materials are prone to generate dust during the sorting process, which not only causes environmental pollution, but also increases the difficulty of subsequent processing, which in turn leads to low resource utilization of fine-grained materials, wasting a large amount of recyclable resources, and increasing the burden on landfills. Therefore, how to achieve multi-level particle size sorting of construction waste resources has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a system and method for recycling construction waste resources, which can realize multi-level particle size sorting of construction waste resources.

[0005] In a first aspect, the present application provides a method for sorting construction waste resources, comprising: The construction waste to be processed is crushed, and then the grading particle size of the construction waste is determined according to the shape characteristics of the construction waste after the crushing process; Using the classification particle size, the crushed construction waste is vibrated and sorted to obtain sorted resources of different particle sizes, and at the same time, the classification screen residue in the vibration sorting process is collected; The spectral sensors are segmented along the conveyor belt according to the particle size, and then the segmented spectral sensors are used to perform differential imaging on the sorting resources of each particle size level to obtain the spectral images of the sorting resources of each particle size level, and then the component difference characteristics of the sorting resources of each particle size level are extracted from each spectral image; The grading particle size of the secondary sorting of the sorted resources is collaboratively constrained by the correlation between the difference characteristics of each component and the grading residue, so as to obtain the constraint conditions of the grading particle size in the secondary sorting, and then the secondary sorting of the construction waste resources to be processed is carried out using the constraint conditions.

[0006] In some embodiments, the crushed construction waste is vibrated and sorted using the graded particle size to obtain sorted resources of different particle sizes, specifically including: Adjusting the mesh size of the three-layer vibrating screen by the graded particle size; The crushed construction waste is transported to a three-layer vibrating screen with adjusted screen aperture to obtain sorted resources of different particle sizes.

[0007] In some embodiments, performing differential imaging on the sorting resources of each particle size level by using the segmented spectral sensor to obtain the spectral images of the sorting resources of each particle size level specifically includes: For each granularity-level sorting resource, a spectral imaging strategy is generated based on the detection key points of the sorting resource; The sorting resources are imaged by the spectral imaging strategy to obtain spectral images of the granularity-level sorting resources, and then spectral images of the sorting resources of each granularity level are obtained.

[0008] In some embodiments, extracting the component difference characteristics of each particle size sorting resource from each spectral image specifically includes: For each granularity-grade sorted resource, obtaining a plurality of component parameters of the granularity-grade sorted resource; Extract characteristic difference vectors of various component parameters from the spectral images of size-grade sorted resources. The composition difference characteristics of the granularity-grade sorting resources are determined through all the characteristic difference vectors, and then the composition difference characteristics of the sorting resources of each granularity-grade are obtained.

[0009] In some embodiments, the classification granularity of the secondary sorting of the sorted resources is collaboratively constrained by the correlation between the various component difference characteristics and the classification screen residue, and the constraint conditions of the classification granularity in the secondary sorting specifically include: Calculate the correlation between the difference characteristics of each component, and then determine the constraint correlation characteristics of each particle size level in the secondary sorting through all the correlations; Determining the weight coefficient of each particle size grade in the secondary sorting according to the graded screen residue; The constraint conditions for the classification granularity in the secondary sorting are generated according to the constraint association characteristics and each weight coefficient.

[0010] In some embodiments, a jaw crusher is used to crush the construction waste to be processed.

[0011] In some embodiments, the spectral sensor is a near infrared spectral sensor.

[0012] In a second aspect, the present application provides a construction waste resource recycling system, comprising a sorting unit, wherein the sorting unit comprises: A pre-processing module is used to crush the construction waste to be processed, and then determine the classification particle size of the construction waste for the first sorting according to the shape characteristics of the construction waste after the crushing process; A processing module is used to perform vibration sorting on the crushed construction waste using the classification particle size to obtain sorting resources of different particle sizes, and at the same time collect the classification screen residue during the vibration sorting process; The processing module is also used to set the spectral sensors in sections along the conveyor belt according to the particle size level, and then use the spectral sensors after the segmented setting to perform differential imaging on the sorting resources of each particle size level to obtain the spectral images of the sorting resources of each particle size level, and then extract the component difference characteristics of the sorting resources of each particle size level from each spectral image; The execution module is used to collaboratively constrain the grading particle size of the secondary sorting of the sorted resources through the correlation between the difference characteristics of each component and the grading residue, obtain the constraint conditions of the grading particle size in the secondary sorting, and then use the constraint conditions to perform secondary sorting on the construction waste resources to be processed.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned method for sorting construction waste resources.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned method for sorting construction waste resources when executing the computer.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In a construction waste resource recycling system and method provided by the present application, the construction waste to be processed is crushed, and then the grading particle size of the construction waste during the first sorting is determined according to the shape characteristics of the construction waste after the crushing process; the crushed construction waste is vibrated and sorted using the grading particle size to obtain sorting resources of different particle sizes, and the grading residue in the vibration sorting process is collected at the same time; the spectral sensor is segmented along the conveyor belt according to the particle size, and then the spectral sensor after the segmentation is used to perform differential imaging on the sorting resources of each particle size level to obtain the spectral image of the sorting resources of each particle size level, and then the component difference characteristics of the sorting resources of each particle size level are extracted from each spectral image; the grading particle size of the secondary sorting of the sorting resources is collaboratively constrained by the correlation between the various component difference characteristics and the grading residue, to obtain the constraint conditions of the grading particle size in the secondary sorting, and then the constraint conditions are used to perform secondary sorting of the construction waste resources to be processed.

[0016] It can be seen that in this application, the grading particle size of the secondary sorting of the sorted resources is collaboratively constrained by the correlation between the difference characteristics of each component and the grading residue, so as to obtain the constraint conditions of the grading particle size in the secondary sorting, and then the constraint conditions are used to perform secondary sorting of the construction waste resources to be treated; firstly, the grading residue can be determined to obtain the amount of material remaining after the materials of each particle size pass through the screen during the vibration screening process, and the distribution ratio of the materials of each particle size can be quantified, so as to provide data support for the parameter optimization of the sorting equipment. If the coarse particle size residue is too high, it indicates that the screen aperture may be too small, and the screen size needs to be dynamically adjusted to improve the sorting efficiency. At the same time, the grading residue can reflect the degree of mixing of the materials during the sorting process. If the fine particle size residue increases abnormally, it may indicate that fine particle components are mixed in the coarse particle material, and the crushing process or screening parameters need to be optimized. In addition, the grading screen Real-time monitoring and feedback of the remainder can achieve closed-loop control of the sorting process and ensure the stability of the sorting efficiency and resource utilization rate; then, by determining the component difference characteristics, the spectral characteristics used to distinguish the components of different materials can be obtained, which is helpful to accurately identify the different components in each particle size grade material and provide a scientific basis for sorting decisions. Among them, the correlation analysis of the component difference characteristics can quantify the degree of component mixing between materials of different particle sizes and provide constraints for the particle size optimization of secondary sorting. If the component correlation between coarse-grained and fine-grained materials is high, it indicates that there is mixing in the sorting process and the classification particle size or screening parameters need to be adjusted. Through the precise extraction and correlation analysis of the component difference characteristics, the multi-level particle size sorting of construction waste can be more efficient and reliable, and the resource utilization rate and environmental protection benefits can be significantly improved; in summary, based on the above scheme, multi-level particle size sorting of construction waste resources can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application 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 some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 is an exemplary flow chart of a method for sorting construction waste resources according to some embodiments of the present application; Figure 2 It is a schematic diagram of the process of construction waste treatment according to some embodiments of the present application; Figure 3 is a schematic diagram of a process for determining constraint conditions according to some embodiments of the present application; Figure 4 is a schematic diagram of the structure of a sorting unit according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing a method for sorting construction waste resources according to some embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0020] refer to Figure 1 , which is an exemplary flow chart of a method for sorting construction waste resources according to some embodiments of the present application, and the method for sorting construction waste resources mainly includes the following steps: In step 101, the construction waste to be processed is crushed, and then the grading particle size of the construction waste during the first sorting is determined according to the shape characteristics of the construction waste after the crushing.

[0021] It should be noted that the grading granularity represents the size threshold of the granularity level; in specific implementation, a jaw crusher is used to crush the construction waste to be processed, and then the shape characteristics of the crushed material are detected in real time through an image acquisition device, the shape characteristics include the aspect ratio, angularity index and surface roughness of the material, if the aspect ratio ≥ 3 and the angularity index ≥ 0.8, it is determined to be a long strip of metal material, and the grading granularity of the long strip of metal material is set to 30mm; if the aspect ratio ≤ 1.5 and the surface roughness ≥ 0.6, it is determined to be a block concrete material, and the grading granularity of the block concrete material is set to 50mm; if 1.5 < aspect ratio < 3 and the angularity index < 0.8, it is determined to be a sheet brick and tile material, and the grading granularity of the sheet brick and tile material is set to 20mm, so that the grading granularity of the construction waste when it is sorted once can be obtained.

[0022] In some embodiments, reference Figure 2 The figure is a schematic diagram of the process of construction waste treatment according to some embodiments of the present application, namely: first, the construction waste is sorted, then magnetically separated, and then crushed once, after which the materials are divided into light materials, mineral materials and heavy materials by gravity sorting, and the light materials and heavy materials are crushed for the second time respectively. Next, the mineral materials, fiber materials, low-water cement and fly ash are mixed to make pulp, and a water reducer and an early strength agent are added at the same time. The mixed slurry is vibrated into shape by a mold, and then naturally cured to finally obtain the product resource.

[0023] In step 102, the crushed construction waste is vibrated and sorted using the grading particle size to obtain sorted resources of different particle sizes, and the grading screen residue in the vibration sorting process is collected.

[0024] In some embodiments, the crushed construction waste is vibrated and sorted using the graded particle size to obtain sorted resources of different particle sizes, which can be achieved by the following steps: Adjusting the mesh size of the three-layer vibrating screen by the graded particle size; The crushed construction waste is transported to a three-layer vibrating screen with adjusted screen aperture to obtain sorted resources of different particle sizes.

[0025] It should be noted that in the present application, the sorting resources are materials classified by particle size during the construction waste sorting process; in specific implementation, first, the mesh aperture of the three-layer vibrating screen can be adjusted by the grading particle size, which can be achieved in the following manner, namely: the three-layer vibrating screen includes a coarse-grained screen, a medium-grained screen and a fine-grained screen. For the coarse-grained screen, the mesh aperture is adjusted by a hydraulic drive device (101) to ensure that the mesh spacing matches the target particle size. If the grading particle size is 50 mm (i.e., the sorting resources are mainly block concrete materials), the mesh aperture is set to 50 mm; if the grading particle size is 30 mm (i.e., the sorting resources are mainly long strip metal materials), the mesh aperture is set to 30 mm; if the grading particle size is 20 mm (i.e., the sorting resources are mainly sheet brick and tile materials), an electric adjustment mechanism (102) is used, according to the above The aperture of the layer screen automatically adjusts the spacing of the middle layer screen, and the aperture of the screen is set to 20mm; for the medium-sized screen, if the classification particle size is 50mm, the aperture of the screen is set to 20mm to separate the medium-sized materials; if the classification particle size is 30mm, the aperture of the screen is set to 10mm; if the classification particle size is 20mm, the aperture of the screen is set to 5mm; for the fine-sized screen, the aperture of the screen is accurately controlled by the stepper motor (103) to ensure the effective separation of the fine-sized materials. If the classification particle size is 50mm, the aperture of the screen is set to 5mm (for separating the fine-sized materials); if the classification particle size is 30mm, the aperture of the screen is set to 3mm; if the classification particle size is 20mm, the aperture of the screen is set to 1mm, and the aperture of the screen in the three-layer vibrating screen can be adjusted. The aperture of the screen represents the size of the screen hole on the vibrating screen.

[0026] Then, in the specific implementation, the crushed construction waste is transported to the three-layer vibrating screen after the mesh aperture is adjusted, and the sorting resources of different particle sizes can be obtained in the following way, namely: the crushed construction waste is transported to the three-layer vibrating screen after the mesh aperture is adjusted, the material output by the fine-particle screen is used as the fine-particle sorting resource, the material output by the medium-particle screen is used as the medium-particle sorting resource, and the material output by the coarse-particle screen is used as the coarse-particle sorting resource, so that sorting resources of different particle sizes can be obtained. In order to improve the screening accuracy, the powder-grade particle size screening process can be added in other embodiments to improve the accuracy of the first sorting.

[0027] In some embodiments, the collection of the graded screen residue during the vibration sorting process can be achieved in the following manner, namely: after each sorting is completed on the three-layer vibration screen, a weighing instrument is used to collect the screen residue value after the sorting is completed, so that the set of all screen residue values ​​can be used as the value range of the graded screen residue, and the graded screen residue during the vibration sorting process can be obtained. It should be noted that in this application, the graded screen residue represents the remaining amount of materials of each particle size grade.

[0028] In step 103, the spectral sensors are segmented along the conveyor belt according to the particle size level, and then the segmented spectral sensors are used to perform differential imaging on the sorting resources of each particle size level to obtain spectral images of the sorting resources of each particle size level, and then the component difference characteristics of the sorting resources of each particle size level are extracted from each spectral image.

[0029] It should be noted that, in the present application, the spectral sensor is a near-infrared spectral sensor. In specific implementation, near-infrared spectral sensors are respectively arranged in the coarse particle level, medium particle level, fine particle level and powder level of the conveyor belt. Each near-infrared spectral sensor is kept at a distance of 10-15 cm from the surface of the material and is equipped with an isolation baffle to prevent cross-contamination of particle size levels. The spectral scanning frequency is adjusted according to the conveyor belt speed to satisfy the spectral scanning frequency ≥ conveyor belt speed / sensor segment interval, wherein the sensor segment interval represents the physical distance between adjacent spectral sensors; the spectral scanning frequency represents the number of times the spectral sensor collects spectral images per unit time.

[0030] In some embodiments, the spectral sensor after segmentation is used to perform differential imaging on the sorting resources of each particle size level, and the spectral image of the sorting resources of each particle size level can be obtained by the following steps: For each granularity-level sorting resource, a spectral imaging strategy is generated based on the detection key points of the sorting resource; The sorting resources are imaged by the spectral imaging strategy to obtain spectral images of the granularity-level sorting resources, and then spectral images of the sorting resources of each granularity level are obtained.

[0031] It should be noted that in this application, spectral image refers to the spectral image of construction waste collected by particle size segmentation; spectral band refers to the spectral data collected by the spectral sensor within a specific wavelength range; detection key points refer to the characteristic points in the spectral image used to identify material components; spectral imaging strategy is an imaging method for sorting resources of different particle size levels.

[0032] In the specific implementation, firstly, for the sorting resources of each particle size level, the spectral imaging strategy based on the detection key points of the sorting resources can be realized in the following way, that is, for the sorting resources of each particle size level, if the sorting resources are coarse-grained materials, spectral imaging in the 2000-2500nm band is used, focusing on detecting metal reflectivity and concrete silicate characteristics (i.e., detection key points); if the sorting resources are medium-grained materials, spectral imaging in the 900-1700nm band is used, focusing on detecting organic absorption peaks and plastic characteristic waves (i.e., detection key points); if the sorting resources are fine-grained materials, spectral imaging in the 1400-2500nm band is used, focusing on detecting Detect light organic matter residues and powder components (i.e., key detection points). If the sorting resource is a powder-grade material, short-wave near-infrared (700-1100nm) spectral imaging is used to quickly screen the metal oxide content in the powder (i.e., key detection points). The sorting resources are imaged by the spectral imaging strategy to obtain spectral images of the particle-grade sorting resources, and then spectral images of the sorting resources of each particle-grade can be obtained. The following method can be used to achieve this, namely, the spectral imaging strategy is used as the imaging strategy of the spectral sensor at the sorting resource, and the imaging result can be used as the spectral image of the particle-grade sorting resource. The spectral images of the sorting resources of each particle-grade can be obtained by the above method.

[0033] In some embodiments, extracting the component difference characteristics of each particle size sorting resource from each spectral image can be achieved by using the following steps: For each granularity-grade sorted resource, obtaining a plurality of component parameters of the granularity-grade sorted resource; Extract characteristic difference vectors of various component parameters from the spectral images of size-grade sorted resources. The composition difference characteristics of the granularity-grade sorting resources are determined through all the characteristic difference vectors, and then the composition difference characteristics of the sorting resources of each granularity-grade are obtained.

[0034] In the specific implementation, first, for each particle size sorting resource, multiple component parameters of the particle size sorting resource are obtained, wherein the component parameters in the coarse particle size spectrum image are the metal reflectance peak and the silicate characteristic wavelength intensity, the component parameters in the medium particle size spectrum image are the organic matter absorption peak area and the plastic characteristic wavelength intensity, the component parameters in the fine particle size spectrum image are the light organic matter residue and the powder composition characteristics, and the component parameters in the powder grade spectrum image are the metal oxide content and the inorganic mineral characteristics; then, extracting the characteristic difference vector of each component parameter from the spectrum image of the particle size sorting resource can be implemented in the following way, namely: using spectrum analysis software (for example: MATLAB) to extract the metal reflectance peak and the silicate characteristic wavelength intensity from the coarse particle size spectrum image, and using chemometrics software (for example: Unscrambler) to extract the metal reflectance peak and the silicate characteristic wavelength intensity from the coarse particle size spectrum image. The absorption peak area of ​​organic matter and the intensity of characteristic wavelength of plastic are extracted from the spectral image of the particle size. The light organic matter residue and powder composition characteristics are extracted from the spectral image of the fine particle size using a machine learning platform (for example, Scikit-learn based on Python). The metal oxide content and inorganic mineral characteristics are extracted from the spectral image of the powder grade using a spectral database and matching algorithm (for example, USGS spectral library and HySpex software), and the characteristic difference vectors of each component parameter can be obtained. Then, the composition difference characteristics of the particle size sorting resources are determined through all the characteristic difference vectors, and then the composition difference characteristics of the particle size sorting resources can be obtained in the following way, that is, the set of all the characteristic difference vectors is used as the composition difference characteristics of the particle size sorting resources, and the composition difference characteristics of the particle size sorting resources can be obtained in the above way.

[0035] It should be noted that, in the present application, the composition difference feature is a spectral feature used to distinguish different material components; the composition parameter represents a specific indicator that describes the characteristics of the material composition; and the characteristic difference vector is a vector that quantifies the degree of composition difference between materials of different particle sizes.

[0036] In step 104, the grading particle size of the secondary sorting of the sorted resources is collaboratively constrained by the correlation between the various component difference characteristics and the grading residue, so as to obtain the constraint conditions of the grading particle size in the secondary sorting, and then the constraint conditions are used to perform secondary sorting on the construction waste resources to be processed.

[0037] In some embodiments, the correlation between the different characteristics of each component and the graded screen residue are used to collaboratively constrain the graded granularity of the sorted resource secondary sorting, and the constraint conditions of the graded granularity in the secondary sorting are obtained. Figure 3 The figure is a schematic diagram of a process for determining constraint conditions in some embodiments of the present application. In this embodiment, determining constraint conditions can be implemented by the following steps: In step 1041, the correlation between the different features of each component is calculated, and then the constraint correlation features of each particle size level in the secondary sorting are determined through all the correlations; In step 1042, the weight coefficients of each particle size class in the secondary sorting are determined according to the graded screen residue; In step 1043, the constraint conditions of the classification granularity in the secondary sorting are generated according to the constraint association characteristics and each weight coefficient; In the specific implementation, first, the correlation between the difference characteristics of each component is calculated, and then the constraint correlation characteristics of each particle size level in the secondary sorting are determined by all the correlations. This can be achieved in the following way, namely: the Pearson correlation matrix can be used to calculate the Pearson correlation coefficient between the difference characteristics of each component as the correlation, so as to obtain the correlation between the difference characteristics of each component, and then the set of all correlations can be used as the constraint correlation characteristics of each particle size level in the secondary sorting; then, the weight coefficient of each particle size level in the secondary sorting is determined according to the graded residue. This can be achieved in the following way, namely: for each particle size level in the secondary sorting, the residue value after sorting of the particle size level in the graded residue is compared with all the residues. The ratio of the sum of the values ​​is used as the weight coefficient of the granularity level. The weight coefficient of each granularity level in the secondary sorting can be obtained by the above method; finally, the constraint conditions for the graded granularity in the secondary sorting are generated according to the constraint association characteristics and the various weight coefficients. The following method is adopted, namely: initialize a multi-objective optimization model based on weight constraints, use the constraint association characteristics as the parameters of the objective function in the multi-objective optimization model, use the various weight coefficients as the target weights in the multi-objective optimization model, use the multi-objective optimization model to perform particle swarm solving on the graded granularity in the secondary sorting, and thus use the conditions obtained by performing particle swarm solving on the multi-objective optimization model as the constraint conditions for the graded granularity in the secondary sorting.

[0038] It should be noted that, in the present application, the constraint condition is a parameter rule used to limit or optimize the grading granularity range; the correlation degree represents the strength of the correlation between the component difference characteristics of sorting resources of different granularity levels; the constraint correlation characteristic represents the correlation parameter affecting the grading granularity; the weight coefficient is a proportional factor used to measure the importance of different constraint correlation characteristics; and the multi-objective optimization model.

[0039] In some embodiments, the secondary sorting of the construction waste resources to be processed using the constraint conditions can be achieved by the following steps: Get the particle size constraint threshold for secondary sorting; The constraint condition is compared with the granularity constraint threshold, so as to adjust the secondary sorting parameters of each sorting resource based on the comparison result.

[0040] In the specific implementation, first, obtaining the particle size constraint threshold of the secondary sorting can be achieved in the following manner, namely: setting the particle size constraint threshold of the secondary sorting to 2 by default. In other embodiments, in order to improve the adjustment accuracy of the secondary sorting parameters, the particle size constraint threshold can be adjusted in combination with historical experience, which is not limited here; then, comparing the constraint condition with the particle size constraint threshold, and adjusting the secondary sorting parameters of each sorting resource based on the comparison result can be achieved in the following manner, namely: when the constraint condition is greater than or equal to the particle size constraint threshold, the crusher power is increased by 10%-15% and the vibration screening time is shortened by 5%-10%; when the constraint condition is less than the particle size constraint threshold, the current sorting parameters are kept unchanged.

[0041] In addition, in another aspect of the present application, in some embodiments, the present application provides a construction waste resource recycling system, the construction waste resource recycling system includes a sorting unit, reference Figure 4 , which is a schematic diagram of the structure of a sorting unit according to some embodiments of the present application, the sorting unit includes: a pre-processing module 201, a processing module 202 and an execution module 203, which are described as follows: Pre-processing module 201, in the present application, the pre-processing module 201 is mainly used to crush the construction waste to be processed, and then determine the classification particle size of the construction waste during the first sorting according to the shape characteristics of the construction waste after the crushing process; Processing module 202, in the present application, the processing module 202 is used to perform vibration sorting on the crushed construction waste using the classification particle size to obtain sorting resources of different particle sizes, and at the same time collect the classification screen residue during the vibration sorting process; It should be noted that the processing module 202 is also used to set the spectral sensors along the conveyor belt in sections according to the particle size, and then use the spectral sensors after the section setting to perform differential imaging on the sorting resources of each particle size level, to obtain the spectral images of the sorting resources of each particle size level, and then extract the component difference characteristics of the sorting resources of each particle size level from each spectral image; Execution module 203. In the present application, execution module 203 is mainly used to collaboratively constrain the grading particle size of the secondary sorting of the sorted resources through the correlation between the difference characteristics of each component and the grading residue, obtain the constraint conditions of the grading particle size in the secondary sorting, and then use the constraint conditions to perform secondary sorting of the construction waste resources to be processed.

[0042] The above describes in detail the examples of the construction waste resource recycling system and method provided by the embodiments of the present application. It can be understood that in order to realize the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0043] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned method for sorting construction waste resources.

[0044] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or the module is optional, and the figure is a schematic diagram of the structure of a computer device for implementing a method for sorting construction waste resources according to an embodiment of the present application. The method for sorting construction waste resources described in the above embodiment can be Figure 5 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0045] The processor 301 may be a general-purpose processor or a special-purpose processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data of the software programs. The computer device may also include a communication unit 305 to implement signal input (reception) and output (transmission).

[0046] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

[0047] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0048] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 performs the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read the data stored in the memory 302, and the data can be stored at the same storage address as the program 304, or the data can be stored at a different storage address from the program 304.

[0049] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0050] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0051] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0052] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned method for sorting construction waste resources when executing.

[0053] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0054] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for sorting construction waste resources, used in a construction waste resource recycling system for sorting construction waste, characterized in that: The steps include: The construction waste to be processed is crushed, and then the grading particle size of the construction waste is determined according to the shape characteristics of the construction waste after the crushing process; Using the classification particle size, the crushed construction waste is vibrated and sorted to obtain sorted resources of different particle sizes, and at the same time, the classification screen residue in the vibration sorting process is collected; The spectral sensors are segmented along the conveyor belt according to the particle size, and then the segmented spectral sensors are used to perform differential imaging on the sorting resources of each particle size level to obtain the spectral images of the sorting resources of each particle size level, and then the component difference characteristics of the sorting resources of each particle size level are extracted from each spectral image; The grading particle size of the secondary sorting of the sorted resources is collaboratively constrained by the correlation between the difference characteristics of each component and the grading residue, so as to obtain the constraint conditions of the grading particle size in the secondary sorting, and then the secondary sorting of the construction waste resources to be processed is carried out using the constraint conditions.

2. The method according to claim 1, characterized in that The crushed construction waste is vibrated and sorted using the classification particle size, and the sorted resources of different particle sizes are specifically: Adjusting the mesh size of the three-layer vibrating screen by the graded particle size; The crushed construction waste is transported to a three-layer vibrating screen with adjusted screen aperture to obtain sorted resources of different particle sizes.

3. The method according to claim 1, characterized in that The spectral sensor after segmentation setting performs differential imaging on the sorting resources of each particle size level, and the spectral images of the sorting resources of each particle size level are obtained, which specifically include: For each granularity-level sorting resource, a spectral imaging strategy is generated based on the detection key points of the sorting resource; The sorting resources are imaged by the spectral imaging strategy to obtain spectral images of the granularity-level sorting resources, and then spectral images of the sorting resources of each granularity level are obtained.

4. The method according to claim 1, characterized in that The composition difference characteristics of each particle size sorting resource extracted from each spectral image specifically include: For each granularity-grade sorted resource, obtaining a plurality of component parameters of the granularity-grade sorted resource; Extract characteristic difference vectors of various component parameters from the spectral images of size-grade sorted resources. The composition difference characteristics of the granularity-grade sorting resources are determined through all the characteristic difference vectors, and then the composition difference characteristics of the sorting resources of each granularity-grade are obtained.

5. The method according to claim 1, characterized in that The classification granularity of the secondary sorting of the sorted resources is collaboratively constrained by the correlation between the difference characteristics of each component and the classification screen residue, and the constraint conditions of the classification granularity in the secondary sorting specifically include: Calculate the correlation between the difference characteristics of each component, and then determine the constraint correlation characteristics of each particle size level in the secondary sorting through all the correlations; Determining the weight coefficient of each particle size grade in the secondary sorting according to the graded screen residue; The constraint conditions for the classification granularity in the secondary sorting are generated according to the constraint association characteristics and each weight coefficient.

6. The method according to claim 1, characterized in that Use jaw crusher to crush the construction waste to be processed.

7. The method according to claim 1, characterized in that The spectral sensor is a near-infrared spectral sensor.

8. A construction waste resource recycling system, the construction waste resource recycling system includes a sorting unit, characterized in that: The sorting unit comprises: A pre-processing module is used to crush the construction waste to be processed, and then determine the classification particle size of the construction waste for the first sorting according to the shape characteristics of the construction waste after the crushing process; A processing module is used to perform vibration sorting on the crushed construction waste using the classification particle size to obtain sorting resources of different particle sizes, and at the same time collect the classification screen residue during the vibration sorting process; The processing module is also used to set the spectral sensors in sections along the conveyor belt according to the particle size level, and then use the spectral sensors after the segmented setting to perform differential imaging on the sorting resources of each particle size level to obtain the spectral images of the sorting resources of each particle size level, and then extract the component difference characteristics of the sorting resources of each particle size level from each spectral image; The execution module is used to collaboratively constrain the grading particle size of the secondary sorting of the sorted resources through the correlation between the difference characteristics of each component and the grading residue, obtain the constraint conditions of the grading particle size in the secondary sorting, and then use the constraint conditions to perform secondary sorting on the construction waste resources to be processed.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the method for sorting construction waste resources as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the method for sorting construction waste resources as described in any one of claims 1 to 7.

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