Garbage collection robot scheduling method and scheduling system

Abstract

The invention relates to the technical field of garbage collection, and particularly provides a garbage collection robot scheduling method, which is used for solving the technical problems in the prior art. The method comprises the following steps: acquiring and updating current state information by using a real-time monitoring module, and sending the current state information to a cloud service platform; receiving a task instruction of the cloud service platform; an optimal path is planned, and autonomous navigation is carried out; after arriving at the doorway of the user, user identity verification is carried out, and a recycling bin cover is opened; receiving garbage thrown by the user, closing the recycling bin cover after completion, sending completion information, uploading the completion information to the cloud service platform, and sending the completion information to the user side; and returning to a designated place to execute the next task. The invention also provides a garbage collection robot scheduling system. By means of the method and system, intelligent and automatic management of the garbage recycling process is achieved, the garbage recycling efficiency and the resource utilization rate are improved, meanwhile, the user experience is improved, and popularization and promotion of garbage classification and recycling are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage collection, and in particular to a garbage collection robot scheduling method and system based on intelligent Internet of Things. Background Art

[0002] With the acceleration of urbanization, garbage disposal has become an important issue in urban management. Traditional garbage collection methods have problems such as low efficiency, waste of resources, and poor user experience, and cannot meet the needs of modern cities for efficient, intelligent, and environmentally friendly garbage disposal. In order to solve these problems, various intelligent garbage recycling equipment have appeared on the market, but most of these devices can only achieve simple garbage delivery functions and lack systematic and intelligent scheduling management.

[0003] Although the existing garbage collection robots have improved the automation of garbage collection to a certain extent, they still have problems such as inaccurate scheduling, low operating efficiency, and poor user experience. For example, some garbage collection robots lack real-time status monitoring and intelligent scheduling systems, which makes it impossible for the robots to perform efficient operations according to actual needs; at the same time, when users use these robots, they also lack convenient and intuitive operation interfaces and reservation services, which greatly reduces the user experience.

[0004] Therefore, there is an urgent need for an innovative intelligent IoT garbage recycling robot scheduling system and its operation method to solve the problems existing in the existing technology, realize the intelligent and automated management of the garbage recycling process, improve garbage recycling efficiency and resource utilization, and enhance user experience. Summary of the invention

[0005] The embodiments of the present invention provide a garbage collection robot scheduling method and scheduling system to solve the following technical problems: In the prior art, garbage collection has technical problems such as inaccurate scheduling, low operating efficiency and poor user experience.

[0006] The present invention provides a garbage collection robot scheduling method, the method comprising: S1. Use the real-time monitoring module to obtain and update the current status information, including power, fault conditions and current location, and send the current status information to the cloud service platform through the communication module; S2. The communication module receives a task instruction from the cloud service platform, wherein the task instruction includes the garbage collection information reserved by the user, including but not limited to the user's location, reservation time, garbage type, and estimated volume or weight; S3, according to the received task instruction, using the built-in path planning algorithm of the driving determination module, planning the optimal path from the current location to the user's location, and autonomously navigating to the location; S4. After arriving at the user's door, the user's reservation code generated by the user terminal is identified through the identity recognition module or the user's identity is authenticated, including but not limited to face recognition and QR code scanning. After confirming the user's identity, the recycling box cover is opened; S5. After receiving the garbage dropped by the user, the space adjustment module automatically adjusts the space allocation in the recycling bin according to the type of garbage and the estimated volume or weight to ensure classified storage of the garbage; S6. After the user completes the garbage delivery, the recycling bin cover is automatically closed, and a recycling completion notification is displayed through the notification display module. At the same time, the completion information of the recycling task is uploaded to the cloud service platform, and the user terminal receives the recycling completion instruction sent by the cloud service platform; S7. According to the subsequent instructions of the cloud service platform, return to the designated location for garbage disposal or continue to perform the next recycling task.

[0007] As an improvement, the method further comprises: S8, using the information collection module to continuously collect information related to the user's delivery habits in real time, The collected information is analyzed and processed through the deep learning module.

[0008] As a further improvement, the deep learning module includes a UBP user behavior prediction algorithm module and a DOA dynamic optimization algorithm module; The UBP user behavior prediction algorithm module adopts a multi-objective genetic algorithm.

[0009] As a further improvement, the deep learning module uses deep learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN) to identify and predict garbage types and volumes. These models can extract useful features and information from a large amount of images and data through training and learning, thereby achieving more accurate identification and prediction.

[0010] In addition, we can also introduce some optimization algorithms to solve the problem of internal space allocation in recycling bins. For example, we can use global optimization algorithms such as genetic algorithms or particle swarm optimization algorithms to search for the optimal space allocation solution. These algorithms can maximize the use of internal space in recycling bins while ensuring garbage classification and storage, so as to optimize subsequent scheduling and path planning strategies and improve recycling efficiency and service quality.

[0011] The present invention also provides a garbage collection robot scheduling system, comprising: Real-time monitoring module, used to obtain and update current status information, including power, fault conditions and current location, and send the current status information to the cloud service platform through the communication module; A communication module, used to receive a task instruction from a cloud service platform, wherein the task instruction includes garbage collection information reserved by a user, including but not limited to the user's location, reservation time, garbage type, and estimated volume or weight; The driving determination module uses the built-in path planning algorithm to plan the optimal path from the current location to the user's location based on the received task instructions, and autonomously navigates to the location; Identity recognition module: after the robot arrives at the user's door, it will identify the reservation code generated by the user or perform user identity verification through the identity recognition module, including but not limited to face recognition and QR code scanning, and open the recycling box cover after confirming the user's identity; The space adjustment module is used to receive the garbage put in by the user, automatically adjust the space allocation in the recycling bin according to the type of garbage and the estimated volume or weight, ensure the classified storage of garbage, and automatically close the recycling bin cover after the user finishes putting in the garbage; The notification display module is used to display the recycling completion notification and upload the completion information of the recycling task to the cloud service platform. The user end receives the recycling completion instruction sent by the cloud service platform.

[0012] As a further improvement, the system also includes an information collection module and a deep learning module. The information collection module continuously collects relevant information about the user's delivery habits in real time, and the collected information is analyzed and processed by the deep learning module.

[0013] As a further improvement, the deep learning module includes a UBP user behavior prediction algorithm module and a DOA dynamic optimization algorithm module; The UBP user behavior prediction algorithm module adopts a multi-objective genetic algorithm.

[0014] As a further improvement, the deep learning module uses convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to identify and predict garbage type and volume.

[0015] Due to the adoption of the above technical solution, the present invention has beneficial technical effects: Through the above method and system, the present invention realizes intelligent and automated management of the garbage recycling process, improves garbage recycling efficiency and resource utilization, while enhancing user experience and promoting the popularization and promotion of garbage classification and recycling. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2 is a system block diagram of an embodiment of the present invention; Figure 3 Schematic diagram of the system architecture of an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] The technical solution proposed in the embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0019] like Figures 1 to 3 As shown, a garbage collection robot scheduling method includes: S1. Use the real-time monitoring module to obtain and update the current status information, including power, fault conditions and current location, and send the current status information to the cloud service platform through the communication module; S2. The communication module receives a task instruction from the cloud service platform. The task instruction includes the garbage collection information reserved by the user, including but not limited to the user's location, reservation time, garbage type, and estimated volume or weight; S3, according to the received task instruction, using the built-in path planning algorithm of the driving determination module, planning the optimal path from the current location to the user's location, and autonomously navigating to the location; S4. After arriving at the user's door, the user's reservation code generated by the user terminal is identified through the identity recognition module or the user's identity is authenticated, including but not limited to face recognition and QR code scanning. After confirming the user's identity, the recycling box cover is opened; S5. After receiving the garbage dropped by the user, the space adjustment module automatically adjusts the space allocation in the recycling bin according to the type of garbage and the estimated volume or weight to ensure classified storage of the garbage; S6. After the user completes the garbage delivery, the recycling bin cover is automatically closed, and a recycling completion notification is displayed through the notification display module. At the same time, the completion information of the recycling task is uploaded to the cloud service platform, and the user terminal receives the recycling completion instruction sent by the cloud service platform; S7. According to the subsequent instructions of the cloud service platform, return to the designated location for garbage disposal or continue to perform the next recycling task.

[0020] In this embodiment, the garbage collection robot scheduling method further includes: S8, using the information collection module to continuously collect information related to the user's delivery habits in real time, The collected information is analyzed and processed through the deep learning module.

[0021] In this embodiment, the deep learning module includes a UBP user behavior prediction algorithm module and a DOA dynamic optimization algorithm module; The UBP user behavior prediction algorithm module adopts a multi-objective genetic algorithm.

[0022] In this embodiment, the deep learning module uses deep learning models such as convolutional neural network (CNN) and recurrent neural network (RNN) to identify and predict garbage type and volume. These models can extract useful features and information from a large amount of images and data through training and learning, thereby achieving more accurate identification and prediction.

[0023] In addition, we can also introduce some optimization algorithms to solve the problem of internal space allocation in recycling bins. For example, we can use global optimization algorithms such as genetic algorithms or particle swarm optimization algorithms to search for the optimal space allocation solution. These algorithms can maximize the use of internal space in recycling bins while ensuring garbage classification and storage, so as to optimize subsequent scheduling and path planning strategies and improve recycling efficiency and service quality.

[0024] The present invention also provides a garbage collection robot scheduling system, comprising: Real-time monitoring module, used to obtain and update current status information, including power, fault conditions and current location, and send the current status information to the cloud service platform through the communication module; A communication module, used to receive task instructions from the cloud service platform, where the task instructions include the garbage collection information reserved by the user, including but not limited to the user's location, reservation time, garbage type, and estimated volume or weight; The driving determination module uses the built-in path planning algorithm to plan the optimal path from the current location to the user's location based on the received task instructions, and autonomously navigates to the location; Identity recognition module: after the robot arrives at the user's door, it will identify the reservation code generated by the user or perform user identity verification through the identity recognition module, including but not limited to face recognition and QR code scanning, and open the recycling box cover after confirming the user's identity; The space adjustment module is used to receive the garbage put in by the user, automatically adjust the space allocation in the recycling bin according to the type of garbage and the estimated volume or weight, ensure the classified storage of garbage, and automatically close the recycling bin cover after the user finishes putting in the garbage; The notification display module is used to display the recycling completion notification and upload the completion information of the recycling task to the cloud service platform. The user end receives the recycling completion instruction sent by the cloud service platform.

[0025] In this embodiment, the system also includes an information collection module and a deep learning module. The information collection module continuously collects relevant information about the user's delivery habits in real time, and the deep learning module analyzes and processes the collected information.

[0026] In this embodiment, the deep learning module includes a UBP user behavior prediction algorithm module and a DOA dynamic optimization algorithm module; The UBP user behavior prediction algorithm module adopts a multi-objective genetic algorithm.

[0027] In this embodiment, the deep learning module uses convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to identify and predict garbage type and volume.

[0028] In the specific use of this embodiment: a) Users register and log in to the mini program, enter or select their residential address, and the system automatically locates and displays nearby available smart IoT garbage collection robots and their status information; b) The user selects a suitable recycling robot according to their needs to make an appointment and fill in the appointment information; c) The cloud service platform receives and processes the reservation request and uses intelligent algorithms to allocate tasks based on factors such as the robot’s real-time status, user location, and reservation time; d) After receiving the command from the cloud service platform, the recycling robot autonomously goes to the user's location according to the planned path, confirms the user's identity by identifying the reservation code or user identity verification information, and opens the recycling box cover; e) After the user puts the garbage into the recycling bin, the robot automatically closes the lid and sends a recycling completion notification to the user through the mini program.

[0029] Through the above method and system, the present invention realizes intelligent and automated management of the garbage recycling process, improves garbage recycling efficiency and resource utilization, while enhancing user experience and promoting the popularization and promotion of garbage classification and recycling.

[0030] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope of the present disclosure is indicated by the following claims.

Claims

1. A garbage collection robot scheduling method, characterized in that: The method comprises: S1. Use the real-time monitoring module to obtain and update the current status information, including power, fault conditions and current location, and send the current status information to the cloud service platform through the communication module; S2. The communication module receives a task instruction from the cloud service platform, wherein the task instruction includes the garbage collection information reserved by the user, including but not limited to the user's location, reservation time, garbage type, and estimated volume or weight; S3, according to the received task instruction, using the built-in path planning algorithm of the driving determination module, planning the optimal path from the current location to the user's location, and autonomously navigating to the location; S4. After arriving at the user's door, the user's reservation code generated by the user terminal is identified through the identity recognition module or the user's identity is authenticated, including but not limited to face recognition and QR code scanning. After confirming the user's identity, the recycling box cover is opened; S5. After receiving the garbage dropped by the user, the space adjustment module automatically adjusts the space allocation in the recycling bin according to the type of garbage and the estimated volume or weight to ensure classified storage of the garbage; S6. After the user completes the garbage delivery, the recycling bin cover is automatically closed, and a recycling completion notification is displayed through the notification display module. At the same time, the completion information of the recycling task is uploaded to the cloud service platform, and the user terminal receives the recycling completion instruction sent by the cloud service platform; S7. According to the subsequent instructions of the cloud service platform, return to the designated location for garbage disposal or continue to perform the next recycling task.

2. A garbage collection robot scheduling method according to claim 1, characterized in that: The method further comprises: S8, using the information collection module to continuously collect information related to the user's delivery habits in real time, The collected information is analyzed and processed through the deep learning module.

3. A garbage collection robot scheduling method according to claim 2, characterized in that: The deep learning module includes a UBP user behavior prediction algorithm module and a DOA dynamic optimization algorithm module; The UBP user behavior prediction algorithm module adopts a multi-objective genetic algorithm.

4. A garbage collection robot scheduling method according to claim 2, characterized in that: The deep learning module uses convolutional neural network and recurrent neural network deep learning models to identify and predict garbage type and volume.

5. A garbage collection robot scheduling system, characterized in that: include: Real-time monitoring module, used to obtain and update current status information, including power, fault conditions and current location, and send the current status information to the cloud service platform through the communication module; A communication module, used to receive a task instruction from a cloud service platform, wherein the task instruction includes garbage collection information reserved by a user, including but not limited to the user's location, reservation time, garbage type, and estimated volume or weight; The driving determination module uses the built-in path planning algorithm to plan the optimal path from the current location to the user's location based on the received task instructions, and autonomously navigates to the location; Identity recognition module: after the robot arrives at the user's door, it will identify the reservation code generated by the user or perform user identity verification through the identity recognition module, including but not limited to face recognition and QR code scanning, and open the recycling box cover after confirming the user's identity; The space adjustment module is used to receive the garbage put in by the user, automatically adjust the space allocation in the recycling bin according to the type of garbage and the estimated volume or weight, ensure the classified storage of garbage, and automatically close the recycling bin cover after the user finishes putting in the garbage; The notification display module is used to display the recycling completion notification and upload the completion information of the recycling task to the cloud service platform. The user end receives the recycling completion instruction sent by the cloud service platform.

6. A garbage collection robot scheduling system according to claim 5, characterized in that: The system also includes an information collection module and a deep learning module. The information collection module continuously collects relevant information about the user's delivery habits in real time, and the deep learning module analyzes and processes the collected information.

7. A garbage collection robot scheduling system according to claim 6, characterized in that: The deep learning module includes a UBP user behavior prediction algorithm module and a DOA dynamic optimization algorithm module; The UBP user behavior prediction algorithm module adopts a multi-objective genetic algorithm.

8. A garbage collection robot scheduling system according to claim 6, characterized in that: The deep learning module uses convolutional neural network and recurrent neural network deep learning models to identify and predict garbage type and volume.

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