An intelligent identification system and method for waste textiles based on image processing

Abstract

The present invention provides an intelligent identification system and method for waste textiles based on image processing, which perform thermal infrared image recognition on waste textiles during sorting operations to obtain information on the existence status of non-fiber substances, thereby adjusting the operating state of the sorting operation; perform visible light image recognition on waste textiles during washing operations to obtain information on the state change of washing water in the washing operation, and judge whether the washing operation is in a limit state, thereby adjusting the operating state of the washing operation; perform visible light image recognition on waste textiles during sorting operations to obtain appearance feature information of waste textiles, thereby determining a matching classification and recycling channel for waste textiles, and accurately performing classification and recycling screening on waste textiles; and adjust the transfer operation state of waste textiles based on the textile transportation status information of the matching classification and recycling channel, thereby improving the recycling efficiency and automatic intelligence level of waste textiles.

Description

Technical Field

[0001] The invention relates to the field of textile processing, and in particular to an intelligent identification system and method for waste textiles based on image processing. Background Art

[0002] Textiles such as clothes and bedding are fast-moving consumer goods, and their replacement speed is relatively fast, making waste textiles an important source of solid waste. Waste textiles contain a large number of chemicals and dyes, especially artificial fiber textiles, which are difficult to degrade under natural conditions. If the discarded textiles are directly landfilled, they will not only occupy land space, but also pollute the soil and groundwater. In order to avoid environmental pollution caused by waste textiles, waste textiles need to be recycled. However, in addition to fiber materials, waste textiles also include non-fiber materials such as metal components. In order to improve the recycling efficiency of waste textiles, it is necessary to comprehensively screen and classify waste textiles, and to clean waste textiles, so as to ensure that different types of waste textiles can obtain matching recycling operations. Most of the traditional recycling operations of waste textiles rely on manual methods, which cannot quickly and efficiently screen and recycle massive amounts of waste textiles, reducing the recycling efficiency of waste textiles. Summary of the invention

[0003] The object of the present invention is to provide an intelligent identification system and method for waste textiles based on image processing, which performs thermal infrared image recognition on waste textiles during sorting operation to obtain information on the existence state of non-fiber substances, thereby adjusting the operating state of the sorting operation, accurately positioning and sorting the non-fiber substances inside the waste textiles, and reducing the impurity content of the waste textiles; performs visible light image recognition on waste textiles during washing operation to obtain information on the state change of washing water in the washing operation, and judges whether the washing operation is in a limit state, thereby adjusting the operating state of the washing operation, improving the washing efficiency of waste textiles and preventing the growth of bacteria and other microorganisms inside the waste textiles; also performs visible light image recognition on waste textiles during classification operation to obtain appearance feature information of waste textiles, thereby determining the classification and recycling channels matched by the waste textiles, and accurately classifying and recycling the waste textiles; and based on the textile transportation state information of the matching classification and recycling channels, adjusts the transfer operation state of the waste textiles, and improves the recycling efficiency and automatic intelligence of the waste textiles.

[0004] The present invention is achieved through the following technical solutions:

[0005] An intelligent identification system for waste textiles based on image processing, comprising:

[0006] A thermal infrared recognition module is used to perform thermal infrared image recognition on waste textiles in the sorting operation to obtain information on the existence status of non-fiber substances inside the waste textiles;

[0007] A sorting operation adjustment module, used for adjusting the operation state of the sorting operation based on the non-fiber material existence state information;

[0008] A first visible light visual recognition module is used to perform first visible light image recognition on the waste textiles in a washing operation to obtain information on a change in the state of washing water of the waste textiles in the washing operation;

[0009] a washing operation adjustment module, configured to determine whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operation state of the washing operation;

[0010] A second visible light visual recognition module is used to perform a second visible light image recognition on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles;

[0011] A transfer operation adjustment module is used to determine the classification and recycling channel that matches the waste textiles based on the appearance feature information; and to adjust the transfer operation state of transferring the waste textiles to the matching classification and recycling channel based on the textile transportation state information of the matching classification and recycling channel.

[0012] Optionally, the thermal infrared recognition module is used to perform thermal infrared image recognition on the waste textiles in the sorting operation to obtain the existence status information of the non-fiber substances inside the waste textiles, including:

[0013] Performing thermal infrared scanning imaging on waste textiles in a sorting line to obtain a thermal infrared image of the waste textiles; performing spectral conversion and recognition on the thermal infrared image to obtain thermal infrared absorption spectrum distribution information of the waste textiles; and determining the content and distribution position of non-fiber substances in the waste textiles based on the thermal infrared absorption spectrum distribution information;

[0014] The sorting operation adjustment module is used to adjust the operation state of the sorting operation based on the non-fiber material existence state information, including:

[0015] Based on the content of the non-fibrous matter, it is determined whether the waste textiles are recyclable waste textiles; if they are recyclable waste textiles, the operation position of sorting and removing the non-fibrous matter of the waste textiles is adjusted based on the distribution position of the non-fibrous matter; if they are not recyclable waste textiles, the waste textiles are directly removed from the sorting operation line.

[0016] Optionally, based on the distribution position of the non-fibrous matter, adjusting the operation position of sorting and removing the non-fibrous matter of the waste textiles includes:

[0017] Step S1, assuming that the width of the non-fiber material to be sorted and removed is W, and the height relative to the horizontal operating platform is H, then in order to ensure that the robot can successfully grab the non-fiber material with a single grab, the optimal grab angle of the robot is:

[0018]

[0019] In the above formula (1), θ is the optimal gripping angle of the robot, is an angle correction function corresponding to the distribution density D of the non-fibrous material, which is related to the distribution density of the non-fibrous material and can be determined based on historical experience data of grabbing the non-fibrous material;

[0020] Step S2, assuming that the area of ​​the non-fiber material to be sorted and removed is A, and the perimeter is C, then in order to ensure that the robot can successfully grab the non-fiber material with a single grab, the optimal force applied by the robot to the non-fiber material is:

[0021] F=k1A m C n (2)

[0022] In the above formula (2), F is the optimal force applied by the manipulator to the non-fibrous material, k1, m, and n are respectively a preset force adjustment coefficient, a preset area constant, and a preset perimeter adjustment coefficient;

[0023] Step S3, based on the calculation results of the above steps S1 and S2, determine the actual grasping force of the manipulator.

[0024] F s =Fcos(θ)(3)

[0025] In the above formula (3), F s is the actual grasping force of the manipulator, η is the preset friction coefficient;

[0026] Then the robot arm can grasp the s and the optimal grabbing angle θ, and sorting and removing the non-fiber materials of the target waste textiles.

[0027] Optionally, the first visible light visual recognition module is used to perform first visible light image recognition on the waste textiles in a washing operation to obtain the washing water state change information of the waste textiles in the washing operation, including:

[0028] Performing dynamic visible light photography on the waste textiles in the washing operation to obtain a corresponding dynamic washing image; performing frame processing on the dynamic washing image to obtain a washing picture frame queue; performing pixel chromaticity recognition on the washing picture frame queue to obtain chromaticity change information of washing water during the washing operation;

[0029] The washing operation adjustment module is used to determine whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operation state of the washing operation, including:

[0030] Based on the chromaticity change information, estimate the change information of the precipitation speed of the stain particles of the washing water during the washing operation; then based on the change information of the precipitation speed of the stain particles, judge whether the washing operation is in a limit state; if it is in a limit state, increase the clean water delivery flow rate of the washing operation; if it is not in a limit state, keep the current clean water delivery flow rate of the washing operation unchanged.

[0031] Optionally, the second visible light visual recognition module is used to perform a second visible light image recognition on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles, including:

[0032] Performing binocular photography of the waste textiles in the classification operation with visible light to obtain corresponding binocular images; generating corresponding three-dimensional images based on binocular parallax of the binocular images; performing pixel contour recognition on the three-dimensional images to obtain appearance contour feature information of the waste textiles; obtaining the completeness of the weaving structure of the waste textiles based on the appearance contour feature information, thereby determining a classification and recycling channel matching the waste textiles;

[0033] The transfer operation adjustment module is used to determine the classification and recycling channel matched with the waste textiles based on the appearance feature information; and adjust the transfer operation state of transferring the waste textiles to the matched classification and recycling channel based on the textile transportation state information of the matched classification and recycling channel, including:

[0034] The textile transport capacity of the matching classified recycling channel is identified to obtain the remaining transportable capacity of the matching classified recycling channel; based on the remaining transportable capacity, the transfer and compression operation parameters for transferring the waste textiles to the matching classified recycling channel are adjusted; wherein the transfer and compression operation parameters include the compression amount of the folding and compression operation on the waste textiles.

[0035] An intelligent identification method for waste textiles based on image processing, comprising:

[0036] Performing thermal infrared image recognition on waste textiles in a sorting operation to obtain information on the existence status of non-fiber substances inside the waste textiles; and adjusting the operation status of the sorting operation based on the information on the existence status of non-fiber substances;

[0037] Performing first visible light image recognition on the waste textiles in a washing operation to obtain washing water state change information of the waste textiles in the washing operation; judging whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operating state of the washing operation;

[0038] A second visible light image recognition is performed on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles; based on the appearance feature information, a classification and recycling channel matching the waste textiles is determined; and based on the textile transportation status information of the matching classification and recycling channel, a transfer operation status of transferring the waste textiles to the matching classification and recycling channel is adjusted.

[0039] Optionally, thermal infrared image recognition is performed on waste textiles in a sorting operation to obtain information on the existence status of non-fiber substances inside the waste textiles; and based on the information on the existence status of non-fiber substances, an operation status of the sorting operation is adjusted, including:

[0040] Performing thermal infrared scanning imaging on waste textiles in a sorting line to obtain a thermal infrared image of the waste textiles; performing spectral conversion and recognition on the thermal infrared image to obtain thermal infrared absorption spectrum distribution information of the waste textiles; and determining the content and distribution position of non-fiber substances in the waste textiles based on the thermal infrared absorption spectrum distribution information;

[0041] Based on the content of the non-fibrous matter, it is determined whether the waste textiles are recyclable waste textiles; if they are recyclable waste textiles, the operation position of sorting and removing the non-fibrous matter of the waste textiles is adjusted based on the distribution position of the non-fibrous matter; if they are not recyclable waste textiles, the waste textiles are directly removed from the sorting operation line.

[0042] Optionally, performing first visible light image recognition on the waste textiles in a washing operation to obtain washing water state change information of the waste textiles in the washing operation; judging whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operating state of the washing operation, comprises:

[0043] Performing dynamic visible light photography on the waste textiles in the washing operation to obtain a corresponding dynamic washing image; performing frame processing on the dynamic washing image to obtain a washing picture frame queue; performing pixel chromaticity recognition on the washing picture frame queue to obtain chromaticity change information of washing water during the washing operation;

[0044] Based on the chromaticity change information, estimate the change information of the precipitation speed of the stain particles of the washing water during the washing operation; then based on the change information of the precipitation speed of the stain particles, judge whether the washing operation is in a limit state; if it is in a limit state, increase the clean water delivery flow rate of the washing operation; if it is not in a limit state, keep the current clean water delivery flow rate of the washing operation unchanged.

[0045] Optionally, performing a second visible light image recognition on the waste textiles in the sorting operation to obtain appearance feature information of the waste textiles; determining a sorting and recycling channel matching the waste textiles based on the appearance feature information; and adjusting a transfer operation state of transferring the waste textiles to the matching sorting and recycling channel based on the textile transportation state information of the matching sorting and recycling channel, including:

[0046] Performing binocular photography of the waste textiles in the classification operation with visible light to obtain corresponding binocular images; generating corresponding three-dimensional images based on binocular parallax of the binocular images; performing pixel contour recognition on the three-dimensional images to obtain appearance contour feature information of the waste textiles; obtaining the completeness of the weaving structure of the waste textiles based on the appearance contour feature information, thereby determining a classification and recycling channel matching the waste textiles;

[0047] The textile transport capacity of the matching classified recycling channel is identified to obtain the remaining transportable capacity of the matching classified recycling channel; based on the remaining transportable capacity, the transfer and compression operation parameters for transferring the waste textiles to the matching classified recycling channel are adjusted; wherein the transfer and compression operation parameters include the compression amount of the folding and compression operation on the waste textiles.

[0048] Optionally, after the transfer operation of transferring the waste textiles to the matching classification recycling channel is completed, the method further includes:

[0049] Performing color visual identification on all waste textiles to determine the color characteristic information of all waste textiles; then classifying all waste textiles based on the color characteristic information to obtain a number of waste textile sets; wherein the difference in chromaticity values ​​of all waste textiles under each waste textile set is within a preset range.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The waste textile intelligent identification system and method based on image processing provided in the present application performs thermal infrared image recognition on the waste textiles during the sorting operation to obtain the existence status information of non-fiber substances, thereby adjusting the operating state of the sorting operation, accurately positioning and sorting the non-fiber substances inside the waste textiles, and reducing the impurity content of the waste textiles; performs visible light image recognition on the waste textiles during the washing operation to obtain the state change information of the washing water in the washing operation, and judges whether the washing operation is in the limit state, thereby adjusting the operating state of the washing operation, improving the washing efficiency of the waste textiles and preventing the growth of bacteria and other microorganisms inside the waste textiles; also performs visible light image recognition on the waste textiles during the classification operation to obtain the appearance feature information of the waste textiles, thereby determining the matching classification and recycling channels for the waste textiles, and accurately classifying and recycling the waste textiles; and based on the textile transportation status information of the matching classification and recycling channels, adjusts the transfer operation state of the waste textiles, and improves the recycling efficiency and automatic intelligence of the waste textiles. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0053] Figure 1 A schematic structural diagram of an intelligent identification system for waste textiles based on image processing provided by the present invention.

[0054] Figure 2 A schematic flow chart of an intelligent identification method for waste textiles based on image processing provided by the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some structures related to the present application are shown in the accompanying drawings, rather than all structures. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0056] The terms "including" and "having" and any variations thereof in this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0057] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] See also Figure 1 As shown, an embodiment of the present application provides an intelligent identification system for waste textiles based on image processing. The intelligent identification system for waste textiles based on image processing includes:

[0059] A thermal infrared recognition module is used to perform thermal infrared image recognition on waste textiles in the sorting operation to obtain information on the existence status of non-fiber substances inside the waste textiles;

[0060] A sorting operation adjustment module, used for adjusting the operation state of the sorting operation based on the non-fiber material existence state information;

[0061] A first visible light visual recognition module is used to perform first visible light image recognition on the waste textile in the washing operation to obtain the washing water state change information of the waste textile in the washing operation;

[0062] A washing operation adjustment module, used to determine whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operation state of the washing operation;

[0063] A second visible light visual recognition module is used to perform a second visible light image recognition on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles;

[0064] The transfer operation adjustment module is used to determine the classification and recycling channel that matches the waste textile based on the appearance feature information; and adjust the transfer operation state of transferring the waste textile to the matching classification and recycling channel based on the textile transportation state information of the matching classification and recycling channel.

[0065] The beneficial effects of the above embodiments are as follows: the waste textile intelligent identification system based on image processing performs thermal infrared image recognition on the waste textiles during the sorting operation to obtain the existence status information of non-fiber substances, thereby adjusting the operating state of the sorting operation, accurately positioning and sorting the non-fiber substances inside the waste textiles, and reducing the impurity content of the waste textiles; during the washing operation, visible light image recognition is performed on the waste textiles to obtain the state change information of the washing water in the washing operation, and judge whether the washing operation is in the limit state, thereby adjusting the operating state of the washing operation, improving the washing efficiency of the waste textiles and preventing the growth of bacteria and other microorganisms inside the waste textiles; during the classification operation, visible light image recognition is performed on the waste textiles to obtain the appearance feature information of the waste textiles, thereby determining the matching classification and recycling channel of the waste textiles, and accurately classifying and recycling the waste textiles; and based on the textile transportation status information of the matching classification and recycling channel, the transfer operation state of the waste textiles is adjusted to improve the recycling efficiency and automatic intelligence of the waste textiles.

[0066] In another embodiment, the thermal infrared recognition module is used to perform thermal infrared image recognition on waste textiles in a sorting operation to obtain information on the existence status of non-fiber substances in the waste textiles, including:

[0067] Performing thermal infrared scanning imaging on waste textiles in a sorting line to obtain a thermal infrared image of the waste textiles; performing spectral conversion and recognition on the thermal infrared image to obtain thermal infrared absorption spectrum distribution information of the waste textiles; and determining the content and distribution position of non-fiber substances in the waste textiles based on the thermal infrared absorption spectrum distribution information;

[0068] The sorting operation adjustment module is used to adjust the operation state of the sorting operation based on the non-fiber material existence state information, including:

[0069] Based on the content of the non-fiber matter, it is determined whether the waste textile is recyclable waste textile; if it is recyclable waste textile, the operation position of sorting and removing the non-fiber matter of the waste textile is adjusted based on the distribution position of the non-fiber matter; if it is not recyclable waste textile, the waste textile is directly removed from the sorting operation line.

[0070] The beneficial effects of the above embodiments are that the recycling process of waste textiles mainly includes sorting operations, washing operations and classification operations in sequence; wherein, the sorting operation is used to sort the metal components (such as metal buttons, etc.) inside the waste textiles. The recycling procedures of these metal components are different from those of textiles. In order to avoid the metal components affecting the normal recycling of waste textiles and to achieve the separate classification and recycling of metal components, it is necessary to sort the metal components out of the waste textiles; the washing operation is usually to clean the waste textiles with detergents in a high-temperature water environment, so that the stains and microorganisms such as bacteria in the waste textiles can be effectively removed and disinfected, and the secondary pollution caused by the growth of bacteria inside the waste textiles can be avoided; the classification operation is usually classified according to the integrity of the fiber structure of the waste textiles. If the fiber structure of the waste textiles is intact, the corresponding waste textiles can be disassembled and rewoven subsequently; if the fiber structure of the waste textiles is already very incomplete, the corresponding waste textiles can be subsequently subjected to high-temperature oxidation decomposition and other operations, and waste textiles with different fiber structures can be treated in a differentiated manner to achieve effective recycling of waste textiles. Specifically, since the fiber component materials and metal materials contained in the waste textiles have different absorption rates for thermal infrared light, the thermal infrared absorption spectrum intensities presented in the thermal infrared images are different. When the waste textiles are transported on the sorting line, thermal infrared scanning imaging is performed on the waste textiles to obtain thermal infrared images of the waste textiles, and the thermal infrared images are spectrally converted and identified to obtain corresponding thermal infrared absorption spectrum distribution information. The thermal infrared absorption spectrum distribution information is analyzed by using the difference in absorption rates of the fiber component materials and metal materials for thermal infrared light to determine the content and distribution position of non-fiber materials (such as the content and distribution position of metal materials) inside the waste textiles. The non-fiber material content is then compared with the preset content threshold. If the non-fiber material content exceeds the preset content threshold, it is determined that the waste textiles do not belong to recyclable waste textiles; otherwise, it is determined that the waste textiles belong to recyclable waste textiles, thereby accurately eliminating the waste textiles that are not recyclable, and avoiding the waste textiles that are not recyclable from affecting the subsequent recycling operation. Based on the distribution position of the non-fibrous matter, the corresponding robot is used to sort and remove the non-fibrous matter, and the non-fibrous matter in the corresponding position inside the waste textiles is cut off and removed, thereby reducing the content of impurities formed by the non-fibrous matter in the waste textiles.

[0071] In another embodiment, based on the distribution position of the non-fibrous material, adjusting the operation position of sorting and removing the non-fibrous material of the waste textiles includes:

[0072] Step S1, assuming that the width of the non-fiber material to be sorted and removed is W, and the height relative to the horizontal operating platform is H, then in order to ensure that the robot can successfully grab the non-fiber material with a single grab, the optimal grab angle of the robot is:

[0073]

[0074] In the above formula (1), θ is the optimal gripping angle of the robot, is the angle correction function corresponding to the distribution density D of the non-fibrous material, which is related to the distribution density of the non-fibrous material and can be determined based on historical experience data of grabbing the non-fibrous material;

[0075] Step S2, assuming that the area of ​​the non-fiber material to be sorted and removed is A, and the perimeter is C, then in order to ensure that the robot can successfully grab the non-fiber material with a single grab, the optimal force applied by the robot to the non-fiber material is:

[0076] F=k1A m C n (2)

[0077] In the above formula (2), F is the optimal force applied by the manipulator to the non-fibrous material, k1, m, and n are respectively a preset force adjustment coefficient, a preset area constant, and a preset perimeter adjustment coefficient;

[0078] Step S3, based on the calculation results of the above steps S1 and S2, determine the actual grasping force of the manipulator.

[0079] F s =Fcos(θ)(3)

[0080] In the above formula (3), F s is the actual grasping force of the manipulator, η is the preset friction coefficient;

[0081] Then the robot arm can grasp the s and the optimal grabbing angle θ, and sorting and removing the non-fiber materials of the target waste textiles.

[0082] The beneficial effect of the above embodiment is that since waste textiles such as old clothes contain non-fibrous materials such as metal buttons and zippers, in the actual sorting and removal process, it is necessary to use a robot to grab the non-fibrous materials to ensure that the non-fibrous materials can be successfully grabbed in a single grab, so as to avoid affecting the processing efficiency of the entire process due to the sorting and removal of non-fibrous materials. Therefore, in order to improve the efficiency of sorting and removing non-fibrous materials from waste textiles, the robot is controlled during the sorting and removal of non-fibrous materials from the waste textiles; according to the width and height information and perimeter area of ​​the target non-fibrous material and its friction coefficient with the robot, the optimal grabbing angle of the robot during the operation and the actual grabbing force that should be applied are determined, ensuring that the non-fibrous materials can be successfully grabbed in a single grab, so as to avoid affecting the processing efficiency of the entire process due to the sorting and removal of non-fibrous materials.

[0083] In another embodiment, the first visible light visual recognition module is used to perform first visible light image recognition on the waste textile in the washing operation to obtain the washing water state change information of the waste textile in the washing operation, including:

[0084] Performing dynamic visible light photography on the waste textiles in the washing operation to obtain a corresponding dynamic washing image; performing frame processing on the dynamic washing image to obtain a washing picture frame queue; performing pixel chromaticity recognition on the washing picture frame queue to obtain chromaticity change information of the washing water during the washing operation;

[0085] The washing operation adjustment module is used to determine whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operation state of the washing operation, including:

[0086] Based on the chromaticity change information, the change information of the stain particle precipitation speed of the cleaning water during the washing operation is estimated; then based on the stain particle precipitation speed change information, it is determined whether the washing operation is in a limit state; if it is in a limit state, the clean water delivery flow rate of the washing operation is increased; if it is not in a limit state, the current clean water delivery flow rate of the washing operation is maintained unchanged.

[0087] The beneficial effect of the above embodiment is that after the sorting operation of all waste textiles is completed, the waste textiles are placed in a washing device such as a drum for washing, wherein the waste textiles can be placed in hot water and detergent is added for rolling washing. As the duration of the washing operation increases, the originally clean hot water becomes turbid due to the precipitation of the stain particles in the waste textiles, and there is an upper limit to the degree of dissolution of the stain particles by the original volume of hot water. When the hot water has reached a saturation state for the dissolution of the stain particles, the waste textiles will not be able to continue to be effectively cleaned. At this time, the water used for washing needs to be replaced in order to further clean the waste textiles. For this purpose, the waste textiles in the washing operation are dynamically photographed with visible light to obtain the corresponding dynamic washing images, and the dynamic washing images are framed into a washing picture frame queue, and the washing picture frame queue is subjected to pixel chromaticity recognition to obtain the chromaticity change information of the washing water during the washing operation. Generally speaking, when the washing water has reached a saturation state for the dissolution of the stain particles, the chromaticity of the washing water will not change more deeply, indicating that the washing water needs to be replaced in order to further clean the waste textiles. Specifically, based on the chromaticity change information, the change information of the precipitation speed of the stain particles of the washing water during the washing operation is estimated, so as to determine whether the washing operation is in a limit state, that is, whether the dissolution of the stain particles by the washing water has reached a saturation state; if it is in a limit state, the clean water delivery flow rate of the washing operation is increased; if it is not in a limit state, the current clean water delivery flow rate of the washing operation is maintained unchanged, which can improve the washing efficiency of waste textiles.

[0088] In another embodiment, the second visible light visual recognition module is used to perform a second visible light image recognition on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles, including:

[0089] Performing binocular photography of the waste textiles in the classification operation with visible light to obtain a corresponding binocular image; generating a corresponding three-dimensional image based on the binocular parallax of the binocular image; performing pixel contour recognition on the three-dimensional image to obtain appearance contour feature information of the waste textiles; obtaining the completeness of the weaving structure of the waste textiles based on the appearance contour feature information, thereby determining a classification and recycling channel matching the waste textiles;

[0090] The transfer operation adjustment module is used to determine the classification and recycling channel that matches the waste textile based on the appearance feature information; and adjust the transfer operation state of transferring the waste textile to the matching classification and recycling channel based on the textile transportation state information of the matching classification and recycling channel, including:

[0091] The textile transport capacity of the matching classified recycling channel is identified to obtain the remaining transportable capacity of the matching classified recycling channel; based on the remaining transportable capacity, the transfer and compression operation parameters of transferring the waste textiles to the matching classified recycling channel are adjusted; wherein the transfer and compression operation parameters include the compression amount of the folding and compression operation on the waste textiles.

[0092] The beneficial effect of the above embodiment is that after the washing operation of all waste textiles is completed, the waste textiles are placed in the corresponding transportation line for classification operation, and the waste textiles are photographed by binocular visible light during transportation to obtain a three-dimensional image of the waste textiles, and then the pixel contour of the three-dimensional image is recognized to obtain the appearance contour feature information of the waste textiles, so that the degree of integrity of the weaving structure of the waste textiles can be quantitatively calibrated; wherein, the degree of integrity of the weaving structure of the waste textiles can be but is not limited to the proportion of the surface area with a complete weaving structure in the waste textiles. When the degree of integrity of the weaving structure of the waste textile exceeds the preset degree threshold, it is determined that the waste textiles should be allocated to the classification and recycling channel for subsequent disassembly and re-weaving; otherwise, it is determined that the waste textiles should be allocated to the classification and recycling channel for subsequent high-temperature oxidation decomposition. The textile transport capacity of the matching classified recycling channel is identified to obtain the remaining transportable capacity of the matching classified recycling channel, and then based on the remaining transportable capacity, the transfer and compression operation parameters of the waste textiles to the matching classified recycling channel are adjusted. In this way, the volume of the waste textiles can be effectively compressed and transferred to the matching classified recycling channel, and the matching classified recycling channel can be used to the maximum extent to classify and transport suitable waste textiles, thereby improving the recycling efficiency of waste textiles.

[0093] See also Figure 2 As shown, an embodiment of the present application provides a method for intelligently identifying waste textiles based on image processing. The method for intelligently identifying waste textiles based on image processing includes:

[0094] Performing thermal infrared image recognition on waste textiles in a sorting operation to obtain information on the existence status of non-fiber substances inside the waste textiles; and adjusting the operation status of the sorting operation based on the information on the existence status of non-fiber substances;

[0095] Performing first visible light image recognition on the waste textile in a washing operation to obtain washing water state change information of the waste textile in the washing operation; judging whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operation state of the washing operation;

[0096] A second visible light image recognition is performed on the waste textile in the classification operation to obtain appearance feature information of the waste textile; based on the appearance feature information, a classification and recycling channel matching the waste textile is determined; and based on the textile transportation state information of the matching classification and recycling channel, a transfer operation state of transferring the waste textile to the matching classification and recycling channel is adjusted.

[0097] The beneficial effects of the above embodiments are as follows: the method for intelligent identification of waste textiles based on image processing performs thermal infrared image recognition on waste textiles during sorting operation to obtain information on the existence status of non-fiber substances, thereby adjusting the operating state of the sorting operation, accurately positioning and sorting the non-fiber substances inside the waste textiles, and reducing the impurity content of the waste textiles; performs visible light image recognition on waste textiles during washing operation to obtain information on the state change of washing water in the washing operation, and judges whether the washing operation is in a limit state, thereby adjusting the operating state of the washing operation, improving the washing efficiency of waste textiles and preventing the growth of bacteria and other microorganisms inside the waste textiles; also performs visible light image recognition on waste textiles during classification operation to obtain appearance feature information of waste textiles, thereby determining the matching classification and recycling channel for waste textiles, and accurately classifying and recycling the waste textiles; and based on the textile transportation status information of the matching classification and recycling channel, adjusts the transfer operation state of waste textiles, thereby improving the recycling efficiency and automatic intelligence of waste textiles.

[0098] In another embodiment, thermal infrared image recognition is performed on waste textiles in a sorting operation to obtain information on the existence status of non-fiber substances in the waste textiles; and based on the information on the existence status of non-fiber substances, the operation status of the sorting operation is adjusted, including:

[0099] Performing thermal infrared scanning imaging on waste textiles in a sorting line to obtain a thermal infrared image of the waste textiles; performing spectral conversion and recognition on the thermal infrared image to obtain thermal infrared absorption spectrum distribution information of the waste textiles; and determining the content and distribution position of non-fiber substances in the waste textiles based on the thermal infrared absorption spectrum distribution information;

[0100] Based on the content of the non-fiber matter, it is determined whether the waste textile is recyclable waste textile; if it is recyclable waste textile, the operation position of sorting and removing the non-fiber matter of the waste textile is adjusted based on the distribution position of the non-fiber matter; if it is not recyclable waste textile, the waste textile is directly removed from the sorting operation line.

[0101] The beneficial effects of the above embodiments are that the recycling process of waste textiles mainly includes sorting operations, washing operations and classification operations in sequence; wherein, the sorting operation is used to sort the metal components (such as metal buttons, etc.) inside the waste textiles. The recycling procedures of these metal components are different from those of textiles. In order to avoid the metal components affecting the normal recycling of waste textiles and to achieve the separate classification and recycling of metal components, it is necessary to sort the metal components out of the waste textiles; the washing operation is usually to clean the waste textiles with detergents in a high-temperature water environment, so that the stains and microorganisms such as bacteria in the waste textiles can be effectively removed and disinfected, and the secondary pollution caused by the growth of bacteria inside the waste textiles can be avoided; the classification operation is usually classified according to the integrity of the fiber structure of the waste textiles. If the fiber structure of the waste textiles is intact, the corresponding waste textiles can be disassembled and rewoven subsequently; if the fiber structure of the waste textiles is already very incomplete, the corresponding waste textiles can be subsequently subjected to high-temperature oxidation decomposition and other operations, and waste textiles with different fiber structures can be treated in a differentiated manner to achieve effective recycling of waste textiles. Specifically, since the fiber component materials and metal materials contained in the waste textiles have different absorption rates for thermal infrared light, the thermal infrared absorption spectrum intensities presented in the thermal infrared images are different. When the waste textiles are transported on the sorting line, thermal infrared scanning imaging is performed on the waste textiles to obtain thermal infrared images of the waste textiles, and the thermal infrared images are spectrally converted and identified to obtain corresponding thermal infrared absorption spectrum distribution information. The thermal infrared absorption spectrum distribution information is analyzed by using the difference in absorption rates of the fiber component materials and metal materials for thermal infrared light to determine the content and distribution position of non-fiber materials (such as the content and distribution position of metal materials) inside the waste textiles. The non-fiber material content is then compared with the preset content threshold. If the non-fiber material content exceeds the preset content threshold, it is determined that the waste textiles do not belong to recyclable waste textiles; otherwise, it is determined that the waste textiles belong to recyclable waste textiles, thereby accurately eliminating the waste textiles that are not recyclable, and avoiding the waste textiles that are not recyclable from affecting the subsequent recycling operation. Based on the distribution position of the non-fibrous matter, the corresponding robot is used to sort and remove the non-fibrous matter, and the non-fibrous matter in the corresponding position inside the waste textiles is cut off and removed, thereby reducing the content of impurities formed by the non-fibrous matter in the waste textiles.

[0102] In another embodiment, performing first visible light image recognition on the waste textile in a washing operation to obtain washing water state change information of the waste textile in the washing operation; judging whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operation state of the washing operation, includes:

[0103] Performing dynamic visible light photography on the waste textiles in the washing operation to obtain a corresponding dynamic washing image; performing frame processing on the dynamic washing image to obtain a washing picture frame queue; performing pixel chromaticity recognition on the washing picture frame queue to obtain chromaticity change information of the washing water during the washing operation;

[0104] Based on the chromaticity change information, the change information of the stain particle precipitation speed of the cleaning water during the washing operation is estimated; then based on the stain particle precipitation speed change information, it is determined whether the washing operation is in a limit state; if it is in a limit state, the clean water delivery flow rate of the washing operation is increased; if it is not in a limit state, the current clean water delivery flow rate of the washing operation is maintained unchanged.

[0105] The beneficial effect of the above embodiment is that after the sorting operation of all waste textiles is completed, the waste textiles are placed in a washing device such as a drum for washing, wherein the waste textiles can be placed in hot water and detergent is added for rolling washing. As the duration of the washing operation increases, the originally clean hot water becomes turbid due to the precipitation of the stain particles in the waste textiles, and there is an upper limit to the degree of dissolution of the stain particles by the original volume of hot water. When the hot water has reached a saturation state for the dissolution of the stain particles, the waste textiles will not be able to continue to be effectively cleaned. At this time, the water used for washing needs to be replaced in order to further clean the waste textiles. For this purpose, the waste textiles in the washing operation are dynamically photographed with visible light to obtain the corresponding dynamic washing images, and the dynamic washing images are framed into a washing picture frame queue, and the washing picture frame queue is subjected to pixel chromaticity recognition to obtain the chromaticity change information of the washing water during the washing operation. Generally speaking, when the washing water has reached a saturation state for the dissolution of the stain particles, the chromaticity of the washing water will not change more deeply, indicating that the washing water needs to be replaced in order to further clean the waste textiles. Specifically, based on the chromaticity change information, the change information of the precipitation speed of the stain particles of the washing water during the washing operation is estimated, so as to determine whether the washing operation is in a limit state, that is, whether the dissolution of the stain particles by the washing water has reached a saturation state; if it is in a limit state, the clean water delivery flow rate of the washing operation is increased; if it is not in a limit state, the current clean water delivery flow rate of the washing operation is maintained unchanged, which can improve the washing efficiency of waste textiles.

[0106] In another embodiment, a second visible light image recognition is performed on the waste textile in the classification operation to obtain appearance feature information of the waste textile; based on the appearance feature information, a classification recycling channel matching the waste textile is determined; and based on the textile transportation state information of the matching classification recycling channel, a transfer operation state of transferring the waste textile to the matching classification recycling channel is adjusted, including:

[0107] Performing binocular photography of the waste textiles in the classification operation with visible light to obtain a corresponding binocular image; generating a corresponding three-dimensional image based on the binocular parallax of the binocular image; performing pixel contour recognition on the three-dimensional image to obtain appearance contour feature information of the waste textiles; obtaining the completeness of the weaving structure of the waste textiles based on the appearance contour feature information, thereby determining a classification and recycling channel matching the waste textiles;

[0108] The textile transport capacity of the matching classified recycling channel is identified to obtain the remaining transportable capacity of the matching classified recycling channel; based on the remaining transportable capacity, the transfer and compression operation parameters of transferring the waste textiles to the matching classified recycling channel are adjusted; wherein the transfer and compression operation parameters include the compression amount of the folding and compression operation on the waste textiles.

[0109] The beneficial effect of the above embodiment is that after the washing operation of all waste textiles is completed, the waste textiles are placed in the corresponding transportation line for classification operation, and the waste textiles are photographed by binocular visible light during transportation to obtain a three-dimensional image of the waste textiles, and then the pixel contour of the three-dimensional image is recognized to obtain the appearance contour feature information of the waste textiles, so that the degree of integrity of the weaving structure of the waste textiles can be quantitatively calibrated; wherein, the degree of integrity of the weaving structure of the waste textiles can be but is not limited to the proportion of the surface area with a complete weaving structure in the waste textiles. When the degree of integrity of the weaving structure of the waste textile exceeds the preset degree threshold, it is determined that the waste textiles should be allocated to the classification and recycling channel for subsequent disassembly and re-weaving; otherwise, it is determined that the waste textiles should be allocated to the classification and recycling channel for subsequent high-temperature oxidation decomposition. The textile transport capacity of the matching classified recycling channel is identified to obtain the remaining transportable capacity of the matching classified recycling channel, and then based on the remaining transportable capacity, the transfer and compression operation parameters of the waste textiles to the matching classified recycling channel are adjusted. In this way, the volume of the waste textiles can be effectively compressed and transferred to the matching classified recycling channel, and the matching classified recycling channel can be used to the maximum extent to classify and transport suitable waste textiles, thereby improving the recycling efficiency of waste textiles.

[0110] In another embodiment, after completing the transfer operation of transferring the waste textile to the matching classification recycling channel, the method further includes:

[0111] Perform color visual identification on all waste textiles to determine the color characteristic information of all waste textiles; then classify all waste textiles based on the color characteristic information to obtain a number of waste textile sets; wherein the difference in chromaticity values ​​of all waste textiles under each waste textile set is within a preset range.

[0112] The beneficial effect of the above embodiment is to perform color visual recognition on all waste textiles, determine the color feature information of all waste textiles, and classify all waste textiles, so as to ensure that waste textiles with the same or similar colors can be classified into the same category, thereby improving the subsequent recycling efficiency of waste textiles.

[0113] In general, the waste textile intelligent identification system and method based on image processing performs thermal infrared image recognition on waste textiles during sorting operation to obtain information on the existence status of non-fiber substances, thereby adjusting the operating state of the sorting operation, accurately positioning and sorting the non-fiber substances inside the waste textiles, and reducing the impurity content of the waste textiles; performs visible light image recognition on waste textiles during washing operation to obtain information on the change of the state of washing water in the washing operation, and judges whether the washing operation is in a limit state, thereby adjusting the operating state of the washing operation, improving the washing efficiency of waste textiles and preventing the growth of bacteria and other microorganisms inside the waste textiles; also performs visible light image recognition on waste textiles during classification operation to obtain appearance feature information of waste textiles, thereby determining the matching classification and recycling channels for waste textiles, and accurately classifying and recycling the waste textiles; and based on the textile transportation status information of the matching classification and recycling channels, adjusts the transfer operation state of waste textiles, and improves the recycling efficiency and automatic intelligence of waste textiles.

[0114] The above is only a specific implementation of the present invention, and any other improvements made based on the concept of the present invention are considered to be within the protection scope of the present invention.

Claims

1. An intelligent identification system for waste textiles based on image processing, comprising: A thermal infrared recognition module is used to perform thermal infrared image recognition on waste textiles in the sorting operation to obtain information on the existence status of non-fiber substances inside the waste textiles; A sorting operation adjustment module, used for adjusting the operation state of the sorting operation based on the non-fiber material existence state information; A first visible light visual recognition module is used to perform first visible light image recognition on the waste textiles in a washing operation to obtain information on a change in the state of washing water of the waste textiles in the washing operation; a washing operation adjustment module, configured to determine whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operation state of the washing operation; A second visible light visual recognition module is used to perform a second visible light image recognition on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles; A transfer operation adjustment module, used to determine the classification and recycling channel that matches the waste textiles based on the appearance feature information; and to adjust the transfer operation state of transferring the waste textiles to the matching classification and recycling channel based on the textile transportation state information of the matching classification and recycling channel; The thermal infrared recognition module is used to perform thermal infrared image recognition on the waste textiles in the sorting operation to obtain the information on the existence status of non-fiber substances in the waste textiles, including: Performing thermal infrared scanning imaging on waste textiles in a sorting line to obtain a thermal infrared image of the waste textiles; performing spectral conversion and recognition on the thermal infrared image to obtain thermal infrared absorption spectrum distribution information of the waste textiles; and determining the content and distribution position of non-fiber substances in the waste textiles based on the thermal infrared absorption spectrum distribution information; The sorting operation adjustment module is used to adjust the operation state of the sorting operation based on the non-fiber material existence state information, including: Based on the content of the non-fiber matter, determining whether the waste textiles are recyclable waste textiles; if they are recyclable waste textiles, adjusting the operation position of sorting and removing the non-fiber matter of the waste textiles based on the distribution position of the non-fiber matter; if they are not recyclable waste textiles, directly removing the waste textiles from the sorting operation line; Based on the distribution position of the non-fibrous matter, adjusting the operation position of sorting and removing the non-fibrous matter of the waste textiles comprises: Step S1, assuming that the width of the non-fiber material to be sorted and removed is W, and the height relative to the horizontal operating platform is H, then in order to ensure that the robot can successfully grab the non-fiber material with a single grab, the optimal grab angle of the robot is: （1） In the above formula (1), is the optimal gripping angle of the robot, is an angle correction function corresponding to the distribution density D of the non-fibrous material, which is related to the distribution density of the non-fibrous material and can be determined based on historical experience data of grabbing the non-fibrous material; Step S2, assuming that the area of ​​the non-fiber material to be sorted and removed is A, and the perimeter is C, then in order to ensure that the robot can successfully grab the non-fiber material with a single grab, the optimal force applied by the robot to the non-fiber material is: （2） In the above formula (2), is the optimal force applied by the manipulator to the non-fibrous material, , m, and n are respectively a preset force adjustment coefficient, a preset area constant, and a preset perimeter adjustment coefficient; Step S3, based on the calculation results of the above steps S1 and S2, determine the actual grasping force of the manipulator. （3） In the above formula (3), is the actual grasping force of the manipulator, is the preset friction coefficient; Then the robot arm can grasp the And the best grip angle , sorting and removing non-fiber materials of target waste textiles; The first visible light visual recognition module is used to perform first visible light image recognition on the waste textiles in the washing operation to obtain the washing water state change information of the waste textiles in the washing operation, including: Performing dynamic visible light photography on the waste textiles in the washing operation to obtain a corresponding dynamic washing image; performing frame processing on the dynamic washing image to obtain a washing picture frame queue; performing pixel chromaticity recognition on the washing picture frame queue to obtain chromaticity change information of washing water during the washing operation; The washing operation adjustment module is used to determine whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operation state of the washing operation, including: Based on the chromaticity change information, estimate the change information of the dirt particle precipitation speed of the washing water during the washing operation; and then based on the dirt particle precipitation speed change information, determine whether the washing operation is in a limit state; if it is in a limit state, increase the clean water delivery flow rate of the washing operation; if it is not in a limit state, keep the current clean water delivery flow rate of the washing operation unchanged; The second visible light visual recognition module is used to perform a second visible light image recognition on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles, including: Performing binocular photography of the waste textiles in the classification operation with visible light to obtain corresponding binocular images; generating corresponding three-dimensional images based on binocular parallax of the binocular images; performing pixel contour recognition on the three-dimensional images to obtain appearance contour feature information of the waste textiles; obtaining the completeness of the weaving structure of the waste textiles based on the appearance contour feature information, thereby determining a classification and recycling channel matching the waste textiles; The transfer operation adjustment module is used to determine the classification and recycling channel matched with the waste textiles based on the appearance feature information; and adjust the transfer operation state of transferring the waste textiles to the matched classification and recycling channel based on the textile transportation state information of the matched classification and recycling channel, including: The textile transport capacity of the matching classified recycling channel is identified to obtain the remaining transportable capacity of the matching classified recycling channel; based on the remaining transportable capacity, the transfer and compression operation parameters for transferring the waste textiles to the matching classified recycling channel are adjusted; wherein the transfer and compression operation parameters include the compression amount of the folding and compression operation on the waste textiles.

2. A method for intelligent identification of waste textiles based on image processing, characterized in that: The waste textile intelligent identification system based on image processing according to claim 1 comprises: Performing thermal infrared image recognition on waste textiles in a sorting operation to obtain information on the existence status of non-fiber substances inside the waste textiles; and adjusting the operation status of the sorting operation based on the information on the existence status of non-fiber substances; Performing first visible light image recognition on the waste textiles in a washing operation to obtain washing water state change information of the waste textiles in the washing operation; judging whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operating state of the washing operation; Performing a second visible light image recognition on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles; determining a classification recycling channel matching the waste textiles based on the appearance feature information; and adjusting a transfer operation state of transferring the waste textiles to the matching classification recycling channel based on the textile transportation state information of the matching classification recycling channel; Performing thermal infrared image recognition on waste textiles in a sorting operation to obtain information on the existence status of non-fiber substances inside the waste textiles; and adjusting the operation status of the sorting operation based on the information on the existence status of non-fiber substances, including: Performing thermal infrared scanning imaging on waste textiles in a sorting line to obtain a thermal infrared image of the waste textiles; performing spectral conversion and recognition on the thermal infrared image to obtain thermal infrared absorption spectrum distribution information of the waste textiles; and determining the content and distribution position of non-fiber substances in the waste textiles based on the thermal infrared absorption spectrum distribution information; Based on the content of the non-fiber matter, determining whether the waste textiles are recyclable waste textiles; if they are recyclable waste textiles, adjusting the operation position of sorting and removing the non-fiber matter of the waste textiles based on the distribution position of the non-fiber matter; if they are not recyclable waste textiles, directly removing the waste textiles from the sorting operation line; Performing first visible light image recognition on the waste textiles in a washing operation to obtain washing water state change information of the waste textiles in the washing operation; judging whether the washing operation is in a limit state based on the washing water state change information, thereby adjusting the operating state of the washing operation, including: Performing dynamic visible light photography on the waste textiles in the washing operation to obtain a corresponding dynamic washing image; performing frame processing on the dynamic washing image to obtain a washing picture frame queue; performing pixel chromaticity recognition on the washing picture frame queue to obtain chromaticity change information of washing water during the washing operation; Based on the chromaticity change information, estimate the change information of the dirt particle precipitation speed of the washing water during the washing operation; and then based on the dirt particle precipitation speed change information, determine whether the washing operation is in a limit state; if it is in a limit state, increase the clean water delivery flow rate of the washing operation; if it is not in a limit state, keep the current clean water delivery flow rate of the washing operation unchanged; Performing a second visible light image recognition on the waste textiles in the classification operation to obtain appearance feature information of the waste textiles; determining a classification recycling channel matching the waste textiles based on the appearance feature information; and adjusting a transfer operation state of transferring the waste textiles to the matching classification recycling channel based on the textile transportation state information of the matching classification recycling channel, including: Performing binocular photography of the waste textiles in the classification operation with visible light to obtain corresponding binocular images; generating corresponding three-dimensional images based on binocular parallax of the binocular images; performing pixel contour recognition on the three-dimensional images to obtain appearance contour feature information of the waste textiles; obtaining the completeness of the weaving structure of the waste textiles based on the appearance contour feature information, thereby determining a classification and recycling channel matching the waste textiles; Identify the textile transport capacity of the matched classified recycling channel to obtain the remaining transportable capacity of the matched classified recycling channel; based on the remaining transportable capacity, adjust the transfer and compression operation parameters for transferring the waste textiles to the matched classified recycling channel; wherein the transfer and compression operation parameters include the compression amount of the folding and compression operation on the waste textiles; After the transfer operation of transferring the waste textiles to the matching classification recycling channel is completed, the method further includes: Performing color visual identification on all waste textiles to determine the color characteristic information of all waste textiles; then classifying all waste textiles based on the color characteristic information to obtain a number of waste textile sets; wherein the difference in chromaticity values ​​of all waste textiles under each waste textile set is within a preset range.

Images