Intelligent recycling system and method for renewable resources

Abstract

The invention discloses a renewable resource intelligent recovery system and method, and belongs to the technical field of renewable resources, and the method comprises the steps: building a recovery device information dependency model based on a plurality of renewable resource intelligent recovery devices; providing an optimal recovery device for a user based on the dependent model; acquiring an original image of a to-be-recycled article based on the recycling device; preprocessing the original image to obtain a preprocessed image; and carrying out contour matching on the preprocessed image to obtain a final classification result.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable resources, and in particular relates to a renewable resource intelligent recycling system and method. Background Art

[0002] Although the intelligent recycling system of renewable resources plays an important role in promoting environmental protection and resource recycling, there are still some defects and challenges at the technical level. First, the problem of recognition accuracy. Although modern intelligent recycling devices are usually equipped with high-definition cameras and advanced image processing algorithms, it is still difficult to accurately identify renewable resources of different materials, shapes and colors in complex environments, especially when the objects are partially blocked or the lighting conditions are not ideal. Secondly, the problem of efficiency and processing speed. When processing a large amount of renewable resources, the intelligent recycling device needs to complete classification and storage quickly and accurately, but the existing technology may be difficult to cope with the immediate processing needs of a large number of items during peak hours, resulting in a decrease in processing efficiency. In addition, the problem of user interaction experience. Although the intelligent recycling system is designed with user-friendly functions such as touch screen and voice interaction, the intuitiveness, ease of use and multi-language support of the user interface still need to be further optimized to adapt to users of different ages and cultural backgrounds. Finally, the problem of data security and privacy. In the process of collecting and processing user data by the intelligent recycling system, how to ensure the security of data and the non-infringement of user privacy is an issue that must be considered in technological development.

[0003] In summary, the technical defects of the intelligent recycling system for renewable resources mainly include recognition accuracy, processing efficiency, user experience, environmental adaptability and data security. Solving these problems requires interdisciplinary technological innovation and continuous optimization and improvement to achieve more efficient, intelligent and user-friendly recycling solutions. Therefore, a renewable resource intelligent recycling system and method is urgently needed. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a renewable resource intelligent recycling system and method to solve the problems existing in the above prior art.

[0005] To achieve the above object, the present invention provides a method for intelligent recycling of renewable resources, comprising:

[0006] Constructing a dependency model of recycling device information based on several renewable resource intelligent recycling devices;

[0007] Providing an optimal recycling device to the user based on the dependency model;

[0008] Acquiring an original image of the object to be recycled based on the recycling device;

[0009] Preprocessing the original image to obtain a preprocessed image;

[0010] Contour matching is performed on the preprocessed image to obtain a final classification result.

[0011] Optionally, the process of constructing a dependency model of recycling device information includes:

[0012] Establishing a recycling device topology model including a plurality of recycling device nodes and a central node;

[0013] Constructing a recycling device network model based on the recycling device topology model;

[0014] Constructing information network model based on scale-free information network;

[0015] The recycling device network model and the information network model are fused based on structural entropy to obtain a dependency model.

[0016] Optionally, the process of preprocessing the original image to obtain a preprocessed image includes:

[0017] The original images are grayed by using a weighted average method to obtain grayed images;

[0018] Performing two-dimensional median filtering on the grayscale image to obtain a filtered image;

[0019] The filtered image is binarized to obtain a preprocessed image.

[0020] Optionally, before performing contour matching based on the preprocessed image, a contour extraction process is also included, wherein the contour extraction process includes:

[0021] Using the Canny operator to extract edges of the preprocessed image to obtain an initial contour;

[0022] The initial contour is shielded from unnecessary edge data based on a mathematical morphology method to obtain a final contour.

[0023] A renewable resource intelligent recycling system, comprising: a device management module, a user module, a renewable resource identification module and a safety and maintenance module;

[0024] Wherein, the device management module is used to collect device information and transmit real-time information to each device;

[0025] The user module is used to display operation options for users and obtain feedback information;

[0026] The renewable resource identification module is used to identify and classify a number of renewable resources;

[0027] The security and maintenance module is used to maintain user information security and normal operation of the device.

[0028] Optionally, the device management module includes a communication unit, a database unit and a recycling device management unit;

[0029] Wherein, the communication unit is used to obtain the real-time status information of each recycling device based on the Internet of Things technology;

[0030] The database unit is used to collect and store real-time status information of each recovery device;

[0031] The recycling device management unit is used to construct a recycling device information dependency model.

[0032] Optionally, the user module includes a login unit, a service unit and a safe logout unit;

[0033] Wherein, the login unit is used for the user to log in to the renewable resource intelligent recycling client;

[0034] The service unit is used to provide users with additional functions in the recycling process of renewable resources;

[0035] The safety exit unit is used to safely exit the renewable resource intelligent recycling device when the user ends the recycling service.

[0036] Optionally, the renewable resource identification module includes an image acquisition unit, an image processing unit and an image recognition unit;

[0037] Wherein, the image acquisition unit is used to collect images of the items to be recycled acquired by the camera in the partition storage device;

[0038] The image processing unit is used to process the object image to obtain a pre-processed image;

[0039] The image recognition unit is used to recognize and classify the pre-processed image.

[0040] Compared with the prior art, the present invention has the following advantages and technical effects:

[0041] The design of the intelligent recycling system for renewable resources covers the device management module, user module, renewable resource identification module and safety and maintenance module, realizing comprehensive management of the device and full service for users. The device management module uses the Internet of Things technology to realize the real-time collection and processing of the status information of each recycling device; the user module provides an integrated operation experience of login, service and safe exit; the renewable resource identification module realizes the rapid and accurate classification of items through image acquisition, processing and recognition.

[0042] In summary, the technical effects of the present invention are mainly reflected in the following aspects: First, the recycling efficiency and accuracy of renewable resources are improved. Through automated classification and storage, manual intervention is reduced and recycling costs are reduced; second, the user experience is optimized. Through a friendly human-computer interaction interface and simple operation procedures, users can easily participate in the recycling process of renewable resources; third, the reliability and safety of the system are enhanced. Through stable power supply design and comprehensive data management, the continuous operation of the device and the security of user information are guaranteed; fourth, the concept of environmental protection and resource recycling is promoted. Through intelligent recycling methods, the society's understanding and utilization of the value of renewable resources are promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0044] Figure 1 Schematic diagram of a renewable resource intelligent recycling system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0047] Embodiment 1

[0048] The present invention proposes an innovative intelligent recycling method for renewable resources, which optimizes the recycling process and improves recycling efficiency and accuracy by constructing a complex dependency model. The method first develops a detailed dependency model based on a series of intelligent recycling devices for renewable resources, which comprehensively considers the network structure and information flow characteristics of the recycling devices. Using this model, the system can intelligently recommend the optimal recycling device to the user to ensure the efficiency of the recycling process.

[0049] After the user successfully logs in through the mobile terminal and selects the recycling service, the system will provide the optimal and alternative recycling device options based on the constructed dependency model, and intelligently plan the best traffic route to guide the user to complete the delivery of the recycled items. Subsequently, the image acquisition unit in the recycling device is activated to obtain the original image of the items to be recycled. These images then undergo a series of preprocessing steps, including grayscale processing, two-dimensional median filtering, and binarization processing to eliminate image noise and highlight the contour features of the items.

[0050] In order to further improve the accuracy of classification, this method adds a key contour extraction step before contour matching. This step first uses the Canny operator to detect the edges of the preprocessed image and extract the initial contour of the object. Then, with the help of mathematical morphology methods, the initial contour is refined to shield unnecessary edge data and make the contour clearer and more accurate. Finally, the extracted contour is compared with the standard contour through contour matching technology to complete the accurate classification of the object.

[0051] like Figure 1 As shown, based on the above-mentioned renewable resource intelligent recycling method in this embodiment, this embodiment also provides a renewable resource intelligent recycling system, including: a device management module, a user module, a renewable resource identification module and a safety and maintenance module.

[0052] As a preferred solution of this embodiment, the specific implementation process includes: the device management module constructs an information network model for all recycling devices in a certain area. Based on the information network model, the user module provides the user with the optimal intelligent recycling device through the user's mobile terminal. When the user is transported to the designated intelligent recycling device and successfully puts the items to be recycled into the recycling device, the separated storage device will be started to implement item identification and classification, wherein the separated storage device implements this function based on the renewable resource identification module, and the renewable resource identification module processes the image of the items to be recycled to obtain the classification result. When the recycling is completed, the voice device will remind the user to exit safely in the intelligent recycling device and disconnect the connection between the intelligent recycling device and the mobile device. When the user forgets to exit safely, the safety and maintenance module automatically disconnects after the user is 10 meters away from the device, and sends a disconnection prompt message to the user's mobile terminal. The user can also feedback the abnormal situation of the recycling device to the device feedback unit in the mobile terminal user interface to facilitate daily maintenance of the recycling device.

[0053] Furthermore, the device management module includes a communication unit, a database unit and a recycling device management unit; the communication unit is the data acquisition unit of the management module, which uses the Internet of Things technology to maintain real-time communication connection with each recycling device, and continuously obtains data such as the device's operating status, user interaction information, and environmental parameters. These data are transmitted back to the central system through the Internet of Things network, ensuring the real-time and accuracy of the information.

[0054] As the information storage center of the entire management module, the database unit is responsible for collecting the real-time status information obtained by the communication unit and storing it securely in the database. This unit not only ensures the integrity and consistency of the data, but also enables the stored information to be quickly retrieved and analyzed through efficient data management strategies to provide support for decision-making.

[0055] The recycling device management unit is the intelligent core of the management module. It builds a dependency model of recycling device information based on the collected data. This model can analyze the relationship and influence between various recycling devices, predict potential problems and needs, and thus achieve optimized management of the entire recycling network. Through this model, managers can more effectively monitor the operating status of recycling devices, respond to maintenance needs in a timely manner, and improve the response speed and quality of services.

[0056] In addition, the device management module also has a user interface, allowing managers to intuitively understand the status of each recycling device through a graphical interface and perform corresponding operations and management. The module also supports remote upgrades and maintenance, ensuring the sustainable operation of the system and the continuous updating of functions.

[0057] Furthermore, the user module includes a login unit, a service unit and a safe logout unit.

[0058] The login unit is the starting point for users to interact with the smart recycling device. It allows users to authenticate and log in through the renewable resource smart recycling client. This unit supports multiple login methods, including but not limited to account password, fingerprint recognition, facial recognition, etc., to meet the needs and preferences of different users and provide a personalized login experience.

[0059] The service unit provides users with a complete set of additional functions required in the recycling process. These functions may include guidance on the classification of recycled items, real-time tracking of recycling progress, accumulation and redemption of points, revenue records, and publicity and education on environmental protection knowledge. The service unit is designed to enhance the user experience, allowing users to enjoy more value-added services and convenience while participating in recycling activities.

[0060] The secure logout unit ensures that users can log out of the system safely when they finish the recycling service. This unit protects users' privacy and account security and prevents unauthorized access and data leakage through a series of security measures, such as automatic logout and clearing temporary data.

[0061] Furthermore, the renewable resource identification module includes an image acquisition unit, an image processing unit and an image recognition unit.

[0062] The image acquisition unit collects images of objects from the cameras configured in the partitioned storage device. These images are the raw data for the recognition process. The camera captures every detail of the object through high-definition shooting, providing high-quality visual information for subsequent image processing and recognition.

[0063] Subsequently, the image processing unit performs a series of preprocessing operations on the collected object images. This step includes adjusting the brightness and contrast of the image, cutting out irrelevant backgrounds, converting the image format, applying filters to remove noise, etc., in order to improve the image quality and create conditions for the accurate operation of the recognition algorithm.

[0064] The image recognition unit uses advanced image recognition technology to analyze and classify pre-processed images. By applying machine learning and deep learning algorithms, the unit is able to identify the characteristics of objects, such as shape, color, texture, etc., and match them with known renewable resource categories in the database, thereby achieving automatic classification. This process not only greatly improves the efficiency of recycling processing, but also provides accurate data support for the subsequent recycling of resources.

[0065] In addition, the renewable resource identification module also has the ability to self-learn and optimize. With the increase of usage time and the accumulation of data, the system can continuously optimize the identification algorithm to improve the accuracy and adaptability of identification. The module can also adapt to different types of items and recycling environments, and has strong versatility and flexibility.

[0066] In terms of the user interface, the recognition module also provides intuitive feedback information, allowing users to understand the results and status of object recognition. For example, when an object is successfully recognized and classified, the system will display the corresponding information and proceed to the next step; when recognition encounters difficulties or errors, the system will prompt the user to make corresponding adjustments or re-deliver.

[0067] As a preferred solution of this embodiment, the specific implementation process includes: this embodiment selects all renewable resource intelligent recycling devices in a certain area as the topological model of the recycling device. The model includes 45 recycling device nodes, 5 secondary center nodes, 2 primary center nodes and 57 connection edges, among which, firstly, a capacity balance equation is established for each node to calculate the recycling efficiency distribution of the recycling device network, and a recycling device network model is established based on the recycling efficiency distribution of the recycling device network.

[0068] C capacity =C+diag(L)

[0069] In the formula, C capacity represents the recycling device network model, C represents the capacity conversion rate between the central node and the node recycling device, diag(L) represents the element L in the vector L i is a diagonal matrix with diagonal elements.

[0070] Then, the information network model is constructed. Initial state: At the beginning, the network consists of a fully connected three-node network. Growth process: New nodes are gradually added to the network, one at a time. Each new node must establish two edges with any two nodes in the original network to maintain the connectivity of the network. Selection of connection edges: The probability that a new node is connected to a node i in the original network is p i By repeating this process, a scale-free information network with nodes can be generated.

[0071] Finally, the dependency model between the recycling device network model and the information network model is established based on the structural entropy of the connection edges.

[0072] After the user successfully logs in to the mobile terminal and selects "I want to recycle", the interface will automatically jump to the "I want to recycle" interface. The user can enter the quantity, estimated size and other information of the items to be recycled into the corresponding information box. The mobile terminal will provide the user with the best recycling device and alternative recycling devices for the user to choose based on the constructed dependency model, and plan the transportation route.

[0073] After the user successfully places the recyclable items, the partition storage device is started, and the image acquisition unit of the intelligent recycling device for renewable resources obtains the original image of the recyclable items from the camera in the partition storage device. Subsequently, the image processing unit pre-processes the original image.

[0074] The specific implementation process includes: (1) graying the original image based on the weighted average method; (2) performing two-dimensional median filtering: first, a filtering window must be determined, and the grayscale values ​​of all pixels in the window are sorted by size, and the middle value of the ordered sequence is taken as the grayscale value of the center point of the window to filter the noisy image; (3) the image processed by the two-dimensional median filtering is binarized to obtain the preprocessed image.

[0075] The image recognition unit recognizes and classifies the preprocessed image. After obtaining reasonable binary image data, an image edge detection operator is used to extract boundary data. This embodiment uses the Canny operator to detect edge data.

[0076] In the process of image edge extraction using the Canny operator, although the main edges in the image can be identified, some non-target edges are often extracted. These edge data not only distract the algorithm's attention, but may also interfere with subsequent image analysis. At the same time, even the extracted edges are often not smooth enough visually, affecting the quality of the edges and the accuracy of subsequent processing. In order to solve these problems, mathematical morphology methods in the field of image processing provide effective solutions.

[0077] Mathematical morphology is an image analysis method based on geometric and topological concepts. It processes images through operations such as erosion and dilation, which can effectively shield unnecessary edge data and smooth edge contours. In practical applications, by performing the same number of opening and closing operations, noise and small edge breaks in the image can be effectively removed, while filling the gaps on the edges, making the edge contours clearer and smoother.

[0078] The opening operation first erodes the image through the structural element to remove smaller objects and noise, and then performs a dilation operation to restore the original shape of the image, but in this process, the edges will become smoother. The closing operation first performs a dilation operation and then an erosion operation. This operation can connect adjacent edges and fill small gaps. By properly selecting the structural element and the number of operations, the edges can be effectively refined and smoothed, thereby improving the accuracy and reliability of image recognition.

[0079] Finally, contour matching is performed. Although the automated transmission process of the area to be recovered may cause positional offsets, such offsets are usually limited to the horizontal direction. To cope with this situation, the algorithm adopts a unique processing method: First, each contour is subdivided into 1024 discrete points, which can improve the accuracy and reliability of matching. Next, the algorithm continuously searches to find the maximum point of the position difference between the two contours to be compared. The difference between the two maximum points is the horizontal offset between the standard contour and the actual extracted contour. The calculation of this offset is a key step in the contour matching process because it determines how the contours are aligned. By adjusting the maximum point by adding or subtracting the offset, the algorithm aligns the two contours at the rightmost end, laying the foundation for accurate matching. Based on this alignment, the contour matching algorithm begins to execute, and by comparing the corresponding points of the two contours, the similarity or difference between them is evaluated, and finally an accurate match is achieved.

[0080] Embodiment 2

[0081] This embodiment provides a renewable resource intelligent recycling device, including: a human-computer interaction device, a partition storage device, a power supply device, a monitoring device and a lighting device.

[0082] As a preferred solution of this embodiment, the specific implementation process is: the user sends the items to be recycled to the recycling device based on the human-computer interaction device, and the partition storage device in the recycling device classifies and recycles the items to be recycled, and the power supply device provides power for the normal operation of the recycling device. The monitoring device and the lighting device are installed on the top of the recycling device. In an environment with poor light, the lighting device automatically turns on or off the fill light according to the current environment. In addition, the present invention also includes a monitoring device for real-time monitoring of changes in the surrounding environment of the recycling device to avoid malicious damage to the device.

[0083] Furthermore, the human-computer interaction device integrates a touch screen display, a non-touch screen display, a voice device, and two delivery tables of different sizes: a first delivery table and a second delivery table.

[0084] The touch screen display is the main interface for users to interact directly with the machine on the recycling device. It not only displays a clear and intuitive operating interface, but also accurately captures and responds to every touch instruction of the user. This intelligent design greatly simplifies the operating process and enables users to easily complete various operations.

[0085] At the same time, the non-touch screen display plays a supporting role in information display. It displays the operating status of the recycling device in real time, ensuring that users have a clear understanding of the current working mode of the equipment. In addition, the display also undertakes the responsibility of education and publicity, by playing promotional videos on the recycling of renewable resources, to improve the public's environmental awareness and participation.

[0086] As the audible part of the interactive system, voice devices provide auditory feedback to users by playing various prompts or warning sounds. This voice prompt function is very useful in many operating scenarios, such as warning when the equipment needs maintenance or an abnormality occurs, or giving confirmation prompts when the user successfully completes an operation.

[0087] In order to accommodate renewable resources of different sizes, the intelligent recycling device is specially designed with two delivery stations: the first delivery station and the second delivery station. The first delivery station is designed for small recyclable items with a diameter of less than 30 cm, while the second delivery station is used to receive large recyclable items with a diameter greater than or equal to 30 cm and less than 60 cm. This distinction ensures that recyclable items of various sizes can be properly handled. In order to ensure safety and prevent misoperation, when the recycling device has not detected the user's touch command, the two delivery stations will remain closed. Only after the user issues the corresponding command through the touch screen display, the corresponding delivery station will be opened and ready to receive the recyclable items.

[0088] As a preferred solution of this embodiment, the specific implementation process is: the user uses a mobile terminal to scan the recycling device QR code on the touch screen display. After scanning, the user clicks to confirm on the mobile terminal and the touch screen display automatically jumps to the service interface. According to the size of the items to be recycled, the user clicks the button to select the first delivery station or the second delivery station on the service interface, and the voice device sends a prompt message. When there are both small items to be recycled that are less than one meter and large items to be recycled that are greater than or equal to one meter and less than two meters among the items to be recycled, the user classifies them by himself and cannot put them in at the same time. After the recycling of items of one size is completed, the recycling of items of another size can be started.

[0089] Further, the compartmentalized storage device includes a lifting structure, a robotic arm, a transmission structure, a camera, and a plurality of recovery bins equipped with weight sensors;

[0090] The lifting structure is a key part of the system. It is connected to the transmission structure and is responsible for controlling the vertical movement of the transmission structure to ensure that the items can be accurately delivered to the recycling bin at the specified height. This design enables the system to adapt to storage requirements at different heights and improve space utilization.

[0091] The robotic arm device is installed on the side of the transmission structure. It uses flexible mechanical movements to place the recyclable items on the delivery platform onto the transmission structure in an orderly manner. When the robotic arm transfers all the recyclable items on the delivery platform to the transmission structure and places them neatly, the robotic arm is placed horizontally along the side of the transmission structure. The precise operation of the robotic arm not only improves the efficiency of item placement, but also ensures the stability and safety of the items during the transmission process.

[0092] The transmission structure is the intermediate link from the delivery to the storage of items. It is responsible for carrying and transporting items. By working in coordination with the lifting structure, the transmission structure can smoothly deliver items to each recycling bin, realizing the automatic circulation of items.

[0093] The cameras are responsible for collecting image information of the objects to be collected on the transport structure. They can be used for a variety of applications such as object identification, quantity counting and anomaly detection.

[0094] The recycling bin is the core part of the storage device. Each recycling bin is equipped with a high-precision weight sensor, which is not only used to store items, but also to weigh items in real time. This design enables the system to accurately measure the weight of each item, providing reliable data support for subsequent data analysis and revenue billing. In addition, the design of the recycling bin also takes into account the classification and storage needs of items. Each recycling bin can be used specifically to store a certain type of renewable resources, thereby achieving orderly management and efficient use of items. In this way, the system can automatically assign different types of items to the corresponding recycling bins, greatly improving storage efficiency.

[0095] As a preferred solution of this embodiment, the specific implementation process includes: when the user clicks on the touch screen display to select the button of the corresponding size delivery platform, the corresponding delivery platform window opens and the delivery platform is automatically lowered, the delivery platform is 90 degrees with the outer surface of the recycling device, and then the user places the items to be recycled on the delivery platform according to the prompt information of the voice device, wherein the number of items to be delivered at one time on the first delivery platform does not exceed 5, and the number of items to be delivered at one time on the second delivery platform does not exceed 3; after the placement is completed, the user clicks on the touch screen display to close the window, and then the transmission structure is positioned to the side of the delivery platform where the delivered items are placed under the movement of the lifting structure, and then the mechanical arm starts to run, and the items to be recycled on the delivery platform are placed one by one on the transmission structure in an orderly manner, and at the same time the camera captures the image of the items to be recycled and transmits the image to the renewable resource identification module, when the items in the image do not belong to renewable resources, the mechanical arm moves the non-renewable resources to the delivery platform, and finally, the remaining renewable resources are identified and delivered to the corresponding recycling bin, and the weight of the renewable resources is calculated, and the user is given a profit reward according to the weight of the recycled renewable resources. If the user does not take the non-renewable resource items away, the corresponding income will be deducted. When the number of items placed is greater than the fixed number, the placement table will send the excess items out of the device, and the recycling device will start to operate when the user removes the excess items. The design of the entire partition storage device embodies the concept of automation, intelligence and precision. Through the close cooperation of various components, it realizes the full process automation management from item placement, transmission, storage to weighing, greatly improving the efficiency and accuracy of renewable resource recycling.

[0096] Further, the power supply equipment includes a solar charging panel, a battery, a voltage stabilizer, a power conversion chip and a backup power supply;

[0097] Solar charging panels are clean energy collectors in the power supply system. They use sunlight to efficiently convert solar energy into DC power, providing a steady supply of power for the entire system. This power is then stored in batteries, which are designed to accumulate and preserve power, ensuring that the recovery device can work properly even at night or in poor lighting conditions.

[0098] The voltage regulator in the power supply system plays a vital role. It protects the circuit from voltage fluctuations by maintaining a stable output voltage, thereby ensuring the safety and stability of the electronic components inside the device. In addition, the power conversion chip is responsible for converting the collected high-voltage electrical energy into a low voltage suitable for the internal components of the device. This conversion process not only improves the efficiency of electrical energy use, but also ensures the reliability of device operation.

[0099] In the design of the power supply system, special consideration is given to possible emergencies, so a backup power supply is equipped. The backup power supply can quickly intervene when the main power supply system fails or there is a power shortage, providing temporary power to ensure the continuous operation of the recovery device and the integrity of the data.

[0100] The renewable resource intelligent recycling device and system of the present invention realizes efficient and automated recycling of renewable resources through carefully designed human-computer interaction equipment, partition storage equipment, power supply equipment, monitoring equipment and lighting equipment. The diversified design of human-computer interaction equipment, including touch screen display, non-touch screen display, voice equipment, first delivery platform and second delivery platform, not only provides a rich user interaction mode, but also has a special delivery platform design for recyclable items of different sizes, which improves the convenience and accuracy of operation.

[0101] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for intelligent recycling of renewable resources, characterized in that: include: Constructing a dependency model of recycling device information based on several renewable resource intelligent recycling devices; Providing an optimal recycling device to the user based on the dependency model; Acquiring an original image of the object to be recycled based on the recycling device; Preprocessing the original image to obtain a preprocessed image; Contour matching is performed on the preprocessed image to obtain a final classification result.

2. The method for intelligent recycling of renewable resources according to claim 1, characterized in that: The process of building a dependency model for recycling device information includes: Establishing a recycling device topology model including a plurality of recycling device nodes and a central node; Constructing a recycling device network model based on the recycling device topology model; Constructing information network model based on scale-free information network; The recycling device network model and the information network model are fused based on structural entropy to obtain a dependency model.

3. The method for intelligent recycling of renewable resources according to claim 2, characterized in that: The process of preprocessing the original image to obtain a preprocessed image includes: The original images are grayed by using a weighted average method to obtain grayed images; Performing two-dimensional median filtering on the grayscale image to obtain a filtered image; The filtered image is binarized to obtain a preprocessed image.

4. The method for intelligent recycling of renewable resources according to claim 1, characterized in that: Before performing contour matching based on the preprocessed image, a contour extraction process is also included, wherein the contour extraction process includes: Using the Canny operator to extract edges of the preprocessed image to obtain an initial contour; The initial contour is shielded from unnecessary edge data based on a mathematical morphology method to obtain a final contour.

5. A renewable resource intelligent recycling system, characterized in that: include: Device management module, user module, renewable resource identification module and safety and maintenance module; Wherein, the device management module is used to collect device information and transmit real-time information to each device; The user module is used to display operation options for users and obtain feedback information; The renewable resource identification module is used to identify and classify a number of renewable resources; The security and maintenance module is used to maintain user information security and normal operation of the device.

6. The renewable resource intelligent recycling system according to claim 5, characterized in that: The device management module includes a communication unit, a database unit and a recycling device management unit; Wherein, the communication unit is used to obtain the real-time status information of each recycling device based on the Internet of Things technology; The database unit is used to collect and store real-time status information of each recovery device; The recycling device management unit is used to construct a recycling device information dependency model.

7. The renewable resource intelligent recycling system according to claim 5, characterized in that: The user module includes a login unit, a service unit and a safe logout unit; Wherein, the login unit is used for the user to log in to the renewable resource intelligent recycling client; The service unit is used to provide users with additional functions in the recycling process of renewable resources; The safety exit unit is used to safely exit the renewable resource intelligent recycling device when the user ends the recycling service.

8. The renewable resource intelligent recycling system according to claim 5, characterized in that: The renewable resource identification module includes an image acquisition unit, an image processing unit and an image recognition unit; Wherein, the image acquisition unit is used to collect images of the items to be recycled acquired by the camera in the partition storage device; The image processing unit is used to process the object image to obtain a pre-processed image; The image recognition unit is used to recognize and classify the pre-processed image.

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