Intelligent garbage recycling and garbage can cleaning, disinfecting and drying system

Abstract

The invention relates to the technical field of garbage treatment, and discloses an intelligent garbage recycling and garbage can cleaning, disinfecting and drying system which comprises a vehicle body for garbage transportation, a mechanical overturning module for controlling the position of a garbage can is arranged on the vehicle body, and a cleaning bin and a garbage bin are arranged on the vehicle body. The mechanical overturning module controls garbage in the garbage can to be poured into the garbage bin, the mechanical overturning module controls the garbage can to be located in the cleaning bin to be cleaned, and a filtering module used for filtering and recycling sewage is arranged in the cleaning bin. The cleaning and disinfection strategy is adjusted, the working efficiency and quality are improved, intelligent management, efficient cleaning and disinfection, environmental protection, energy conservation and data monitoring are achieved, convenience is brought to urban environmental sanitation work, urban environmental sanitation management is promoted to advance towards the intelligent, green and sustainable development direction, and the intelligent, green and sustainable development of urban environmental sanitation management is promoted through path planning. The vehicle can complete the cleaning task of the designated garbage can in the shortest route and the least time, and unnecessary round trip and empty driving are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage disposal, and in particular to an intelligent garbage recycling, garbage bin cleaning, disinfecting and drying system. Background Art

[0002] Intelligent garbage recycling helps to improve the level of intelligent and automated urban management and promote the high-quality development of the sanitation industry. The driving control system of the unmanned vehicle adopts a pure visual solution to realize the perception of the world, and constructs a three-dimensional vector space of the real world through a neural network based on the original video data. In the vector space, a hybrid planning system combining traditional regulatory control methods with neural networks is used to realize the behavior and path planning of the car, and a control signal is generated and transmitted to the actuator. At the same time, the continuous iteration of autonomous driving capabilities is realized through a complete data closed-loop system and simulation platform. As an important node for garbage collection, the cleanliness and sanitation of garbage cans directly affect the urban environment and the health of residents. At present, there are generally problems in the cleaning of urban garbage cans, such as low cleaning frequency, incomplete cleaning, and incomplete cleaning records. These problems not only affect the beauty of the urban environment, but may also cause garbage cans to become a medium for disease transmission, threatening public health and safety. In addition, the degree of cleaning of garbage cans is not easy to guarantee. At the same time, there is a lack of information management and monitoring systems, poor real-time performance, and opaque information. Data statistics and analysis make it difficult to achieve intelligent management and optimized scheduling. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent garbage recycling, garbage bin cleaning, disinfection and drying system. Through autonomous navigation and route planning of the vehicle, garbage management and operation can be carried out according to the frequency of garbage cleaning in urban areas, and the cleaning and disinfection tasks of garbage bins can be automatically completed, thereby improving work efficiency and accuracy, bringing convenience to urban sanitation work, and promoting urban sanitation management towards intelligent, green and sustainable development, which can effectively solve the problems in the background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An intelligent garbage recycling, garbage bin cleaning, disinfection and drying system comprises a vehicle body for garbage transportation, wherein the vehicle body is provided with a mechanical flipping module for controlling the position of the garbage bin, the vehicle body is provided with a cleaning bin and a garbage bin, the mechanical flipping module controls the garbage in the garbage bin to be dumped into the garbage bin, the mechanical flipping module controls the garbage bin to be located in the cleaning bin for cleaning, the cleaning bin is provided with a filtering module for sewage filtering and recycling, the vehicle body identifies lanes and objects through a visual environment recognition module, and determines the feasibility of walking, the appropriate time and route of passing through through a vehicle drive module, thereby realizing motion path planning, environmental information processing, positioning and navigation, and image feature processing of the vehicle body.

[0006] As a further solution of the present invention: the mechanical flipping module includes a propulsion module for controlling the horizontal movement of the trash can, a flipping frame for flipping the trash can, and a positioning frame for clamping and positioning the trash can. By identifying and locating the trash can and transmitting signals to the propulsion module of the flipping frame, the trash can is positioned to realize the clamping and flipping function.

[0007] As a further solution of the present invention: four rotating roller brushes are provided in the cleaning chamber, spray devices are provided on the top and sides of the cleaning chamber, and a disinfection and drying module and a high-speed rotating nozzle for spraying the surface of the trash can are provided in the cleaning chamber.

[0008] As a further solution of the present invention: the filtering module includes a box for storing detergent, and a driving module for controlling the start and stop of the spraying device, the amount of water sprayed, and the cleaning operation.

[0009] As a further solution of the present invention: the visual environment recognition module captures images through a visual data transmission camera, and the visual environment recognition module includes the following steps to implement path and motion planning:

[0010] S1, using sparse extraction methods to extract potential features to identify traffic participants such as lanes, obstacles, and moving objects;

[0011] S2, select target candidates, determine lane lines, and form traversable spatial information in unstructured areas;

[0012] S3, generate possible trajectories, and generate motion trajectories through trajectory optimization and neuron planning;

[0013] S4, generate a movement plan and determine whether to forcibly cut into traffic or wait for pedestrians to pass;

[0014] S5. When determining the trajectory, time, speed and motion plan, analyze the traffic conditions of other vehicles under the motion trajectory and output the motion plan after considering various factors.

[0015] As a further solution of the present invention: the vehicle driving module is implemented by a particle swarm algorithm, and the code implemented by the particle swarm algorithm is

[0016] for:

[0017]

[0018]

[0019]

[0020] As a further solution of the present invention: the specific operation steps for implementing the particle swarm algorithm are as follows:

[0021] S1. Import the NumPy library and name it np;

[0022] S2, start the iterative process, update each particle, calculate the fitness score of the particle, update the optimal position of the particle and the global optimal position, update the speed and position of the particle according to the PSO algorithm, perform collision detection, and avoid collision with obstacles;

[0023] S3. Output the final path planning result, that is, the global best position global_best_position.

[0024] As a further solution of the present invention: it also includes a data processing warehouse, and the data processing warehouse is used to store the vehicle's intelligent algorithm and real-time monitoring data.

[0025] As a further solution of the present invention: it also includes processing and analyzing the acquired environmental information through an environmental perception algorithm, and constructing features by analyzing the directional gradients of pixels in the local area of ​​the image, so as to achieve accurate recognition of the trash can.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] With autonomous navigation and route planning of vehicles, garbage management and operation can be carried out according to the frequency of garbage cleaning in urban areas, and the cleaning and disinfection tasks of garbage bins can be automatically completed, which improves work efficiency and accuracy. The garbage bins can be fully cleaned and disinfected to ensure that the surface of the garbage bins is clean and hygienic. Through data analysis and feedback, the cleaning and disinfection strategies can be adjusted in time to improve work efficiency and quality. Intelligent management, efficient cleaning and disinfection, environmental protection and energy saving, and data-based monitoring will bring convenience to urban sanitation work and promote urban sanitation management towards intelligent, green and sustainable development. Through route planning, vehicles can complete the cleaning tasks of designated garbage bins in the shortest route and the least time, reducing unnecessary round trips and empty driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0029] Figure 1 This is a schematic diagram of the structure of the intelligent garbage recycling, garbage bin cleaning, disinfection and drying system;

[0030] Figure 2This is a schematic diagram of the vehicle drive module in the intelligent garbage collection, garbage bin cleaning, disinfection and drying system;

[0031] Figure 3 This is a schematic diagram of the mechanical flipping module in the intelligent garbage recycling, garbage bin cleaning, disinfection and drying system;

[0032] Figure 4 This is a schematic diagram of the structure of the cleaning bin and garbage bin in the intelligent garbage recycling, garbage bin cleaning, disinfection and drying system;

[0033] Figure 5 This is a schematic diagram of the structure of the filter module in the intelligent garbage recycling, garbage bin cleaning, disinfection and drying system;

[0034] In the figure: 1. Vehicle body; 2. Cleaning chamber; 3. Garbage chamber; 4. Mechanical turning module; 5. Filter module. DETAILED DESCRIPTION

[0035] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0036] See also Figure 1 to Figure 5 In an embodiment of the present invention, an intelligent garbage recycling, garbage bin cleaning, disinfection and drying system includes a vehicle body 1 for garbage transportation, a mechanical flipping module 4 for controlling the position of the garbage bin is provided on the vehicle body 1, a cleaning bin 2 and a garbage bin 3 are provided on the vehicle body 1, the mechanical flipping module 4 controls the garbage in the garbage bin to be dumped into the garbage bin 3, the mechanical flipping module 4 controls the garbage bin to be located in the cleaning bin 2 for cleaning, and a filtering module 5 for sewage filtering and recycling is provided in the cleaning bin 2. The vehicle body 1 recognizes lanes and objects through a visual environment recognition module, and judges the feasibility of walking, the appropriate time and route of passing through through a vehicle driving module, thereby realizing motion path planning, environmental information processing, positioning and navigation, and image feature processing of the vehicle body 1.

[0037] In this embodiment: a mechanical flipping module 4; a mechanical flipping mechanism combined with a visual transmission system is used to automatically identify and locate the trash can, and the trash in the trash can is dumped through the flipping frame, and then the trash is put into the cleaning bin for cleaning, and finally the trash can is returned to its original position. The trash can is clamped by the flipping and clamping function of the flipping frame, and then the remaining trash in the trash can is dumped into the trash bin of the intelligent unmanned vehicle through mechanical flipping, and then the trash can is put into the cleaning bin for cleaning, and finally the trash can is returned to its original position. The mechanical flipping frame can automatically dump the remaining trash in the trash can through the mechanical flipping frame, and at the same time, it can rotate, tilt and telescope to adapt to the cleaning and disinfection needs of trash cans of different sizes and shapes. The propulsion mechanism of the flipping frame can move the flipping frame according to the positioning data transmitted by the wide-angle camera equipped on the front of the vehicle according to the position of the trash can, so that the flipping frame can It can accurately move to the designated position and clamp the trash can, effectively compress the garbage, reduce the space occupied by the garbage during transportation, thereby reducing the number of transportation times, improving loading efficiency, saving fuel costs, and effectively increasing the loading capacity of the garbage bin. There are three cameras and a rangefinder when identifying the trash can. The trash can can be identified by the cameras on both sides, and then positioned by the rangefinder and the linear push mechanism of the flip frame can be transmitted to position the trash can to realize the clamping and flipping function. The flip frame is a mechanism for the intelligent garbage recycling, garbage can cleaning, disinfection and drying multifunctional integrated vehicle to clamp and flip the trash can. It plays an important role in the cleaning of the trash can. The flip frame should have a solid structure and stable support, which can bear the weight of the trash can and ensure safe flipping. It can be made of materials such as steel or aluminum alloy, and the structural design should take into account the load-bearing capacity and stability.

[0038] Cleaning chamber 2: Put the trash can into the cleaning chamber for cleaning, disinfection and drying, and finally put the trash can back in place. It is equipped with four rotating roller brushes to ensure that the outside of the trash can is cleaned without dead angles. The spray device on the top and the two pipe-mounted spray devices on the side can supply water for external cleaning. The cleaning chamber 2 is equipped with an ultraviolet disinfection and drying lamp, which can emit ultraviolet radiation to disinfect the air and surface in the sanitation vehicle and improve the sanitary quality in the vehicle. At the same time, ultraviolet disinfection is a chemical-free disinfection method with low energy consumption and no chemical residue, which is beneficial to energy conservation and environmental protection. After the ultraviolet lamp disinfects, it will switch to the drying mode to dry the trash can to prevent bacteria from growing again. The cleaning chamber 2 is equipped with an automatically operated high-pressure rotating nozzle, which can not only generate a strong water flow rotation jet to effectively clean the inside of the trash can, but also the ejected high-pressure water flow can deeply clean the angles and gaps that are difficult to reach, ensuring the quality and efficiency of cleaning. The control system adjusts the angle, intensity and range of the water spray as needed to achieve automatic cleaning and improve work efficiency.

[0039] Filter module 5: Combine the filter device with the cleaning liquid spray device. The cleaning liquid uses quick-drying disinfectant as a cleaning agent. The cleaning liquid spray device sprays the cleaning liquid evenly onto the surface to be cleaned. The filter device is used to filter the sewage and pollutants discharged during the cleaning process, reduce the damage of impurities in the cleaning liquid to the cleaning equipment, and filter out clean water for the next cleaning. The combination of the two can not only improve the cleaning quality, but also extend the service life of the equipment and reduce maintenance costs. The vehicle can be equipped with various cleaning tools and equipment, such as high-pressure water guns, vacuum cleaners, etc., which can be used to clean different environments such as urban roads and public places, improving work efficiency and flexibility.

[0040] Vehicle body 1; has a sturdy and durable chassis, supporting the structure of the entire cleaning and disinfection vehicle, efficient path planning, in a complex urban environment, selects an optimal path from many possible paths to achieve fast and efficient cleaning operations, the PSO algorithm quickly converges to the optimal path by simulating the flight and search behavior of particles in the solution space, which not only greatly shortens the path planning time, but also improves the accuracy of path selection, dynamic environmental adaptability, the urban environment is constantly changing, such as the emergence of new construction sites, road construction or temporary closures, etc. The PSO algorithm can adjust the search strategy in real time according to environmental changes, and provide the latest optimal path planning for vehicle body 1 to ensure the continuity and efficiency of the cleaning task. In the complex urban environment and changing task requirements, vehicle body 1 generates an effective decision tree to respond to various In emergencies, the PSO algorithm can optimize the generation process of the decision tree, quickly find the optimal or approximately optimal decision-making plan through group intelligence, and improve the vehicle's autonomous decision-making ability. In the scenario of multi-vehicle collaborative operation, the PSO algorithm can optimize resource allocation and scheduling strategies to ensure that each vehicle can perform cleaning tasks at the most appropriate time and place to maximize overall operation efficiency. By introducing the PSO algorithm, the intelligent garbage collection, garbage bin cleaning, disinfection and drying multifunctional integrated vehicle can more intelligently plan paths and make decisions, effectively respond to complex and changeable urban environments and operation requirements, and significantly improve the efficiency and quality of cleaning and disinfection. Environmental information is obtained through sensors, cameras and other equipment, and algorithms are used to process and analyze this information, thereby realizing the perception and understanding of the environment. The Occupancy Network and convolutional neural network CNN algorithms are used to predict the behavior of traffic participants (vehicles, pedestrians, etc.). The main steps are:

[0041] The first step is image input: the 8 cameras are directly input into the algorithm after calibration and correction. Since the original data of the camera is 4-bit color, it can provide 16 times the dynamic range to the algorithm, so the input delay can be reduced.

[0042] The second step is image feature extraction: use the pre-trained deep neural network model to characterize the image, and use the RegNet and BiFPNs algorithms to characterize the image. Characterization can be understood as identifying objects.

[0043] The third step is image processing: it is divided into two parts. One part is to align the trajectory of one's own vehicle, and the other part is to align various identification features to initially form the spatiotemporal features.

[0044] The fourth step is deconvolution: deconvolution is to reverse the concentrated features into objects. The intelligent garbage recycling, garbage bin cleaning, disinfection and drying multifunctional integrated vehicle indicates that its algorithm can recognize the geographical features of the road, such as up and down slopes.

[0045] The fifth step is queryable output: some of the reverse objects after deconvolution may not fully represent the size of the real objects, so the intelligent garbage recycling and garbage bin cleaning, disinfection and drying multifunctional integrated vehicle algorithm uses the query method to query the real-world results in the database, correct the position and size, and then place them in space.

[0046] In the environmental perception algorithm of vehicle body 1, the introduction of CNN algorithm can be used for the step of image feature extraction to further optimize the processing and analysis of environmental images. The functions of CNN algorithm are as follows:

[0047] Image feature extraction: CNN can automatically learn features in images, such as edges, textures, shapes, etc., through multi-layer convolution and pooling operations. These learned features can better represent the content of the image and help with subsequent environmental understanding and behavior prediction; Feature abstraction: CNN will gradually abstract higher-level feature representations during the feature extraction process, from simple edges and textures to more complex objects and scene features, thereby improving the accuracy and robustness of environmental perception algorithms; Target detection and recognition: CNN can also be used for target detection and recognition, helping smart garbage recycling and garbage bin cleaning, disinfection and drying multifunctional integrated vehicles to identify vehicles, pedestrians and other traffic participants on the road. Vehicles can more accurately perceive various targets in the surrounding environment and make corresponding decisions and actions.

[0048] The environment perception algorithm of the intelligent garbage collection, garbage bin cleaning, disinfection and drying multifunctional integrated vehicle can use convolutional neural network (CNN) for image recognition, which can help the vehicle identify and classify different types of garbage bins, as well as identify obstacles in the surrounding environment. The specific code is implemented as follows:

[0049]

[0050]

[0051]

[0052]

[0053] The code provides a basic CNN structure for daily image recognition tasks of vehicle body 1.

[0054] The code implements a simple convolutional neural network model to identify the vehicle body 1 dataset.

[0055] The specific operations include:

[0056] Step 1: Import the TensorFlow library and import the required layers and models from the Keras module of TensorFlow.

[0057] Step 2: Define a function `build_model` to build a CNN model, including convolutional layer, pooling layer, fully connected layer and output layer.

[0058] Step 3: Define a function `compile_model` to compile the model, set the loss function, optimizer and evaluation indicators.

[0059] Step 4: Define a function `train_model` for training the model, accept training data and test data, and specify the training rounds.

[0060] Step 5: In the main function, the shape of the input image and the number of bin categories are set, the model is built, compiled, and the training and test datasets are prepared.

[0061] Step 6: Finally, the model is trained by calling the `train_model` function. Since no specific loading logic is provided for the training and test datasets, they are temporarily set to None in the code.

[0062] Using the SLAM algorithm, in the application of intelligent garbage collection, garbage bin cleaning, disinfection and drying multifunctional integrated vehicle, the following steps are mainly implemented:

[0063] Motion estimation: Through data from sensors such as lidar and cameras, the SLAM algorithm can help the vehicle estimate its own position and posture in real time, thereby accurately locating the vehicle's position relative to the trash can.

[0064] Map construction: SLAM algorithms can help vehicles build environmental maps, including information such as the location of trash cans, obstacle locations, cleaning and disinfection paths, etc., so that vehicles can plan paths and avoid obstacles.

[0065] Autonomous navigation: Based on the map and positioning information constructed by the SLAM algorithm, the vehicle can achieve autonomous navigation, move along the predetermined path, locate and clean and disinfect the trash cans.

[0066] Closed-loop detection: SLAM algorithms can help vehicles detect whether they have passed through known locations to correct positioning errors and ensure the accuracy and stability of the vehicle during the cleaning and disinfection process.

[0067] Optimization: Optimize the vehicle's trajectory and map through optimization algorithms (such as least squares method, nonlinear optimization, etc.) to improve the accuracy of positioning and mapping.

[0068] Improve the positioning accuracy, navigation capabilities and efficiency of vehicles, enable vehicles to operate autonomously in complex environments, and realize intelligent cleaning and disinfection operations.

[0069] The HOG algorithm is a feature extraction method widely used in the field of computer vision and image processing. It constructs features by analyzing the directional gradients of pixels in the local area of ​​the image. These features can be used to support various image recognition tasks. In the application of car body 1, it can realize accurate recognition of trash cans. The following are its main steps:

[0070] Training model: First, the HOG algorithm is trained using a labeled image dataset to learn the appearance features and shape of the trash can.

[0071] Feature extraction: In real-time operation, the trained HOG model is used to extract the feature vector of the trash can in the image captured by the camera.

[0072] Object detection: The extracted feature vector is input into the trained model to perform object detection and recognition to determine the location and status of the trash can.

[0073] Autonomous operation: Based on the detected location and status of the trash can, the intelligent garbage collection, trash can cleaning, disinfection and drying multifunctional integrated vehicle can autonomously plan the cleaning and disinfection route, locate and clean and disinfect the trash can.

[0074] Effect evaluation: After cleaning and disinfection, the HOG algorithm can also be used to evaluate the effect of cleaning and disinfection. By analyzing the images of the trash can before and after processing, the algorithm can help determine whether the trash can has been thoroughly cleaned and disinfected.

[0075] By setting up a remote monitoring system: the Internet connection monitors the vehicle's location, cleaning and disinfection process, equipment status and other information in real time; the data management system: used to record data during the cleaning and disinfection process, such as cleaning time, disinfectant usage, cleaning effect and other information; the path optimization system: used to optimize the driving path of the vehicle 1, improve work efficiency and save costs. The path planning system can automatically plan the best driving route based on real-time traffic conditions and cleaning task allocation. The goal of the path optimization system is to plan the best working path for the vehicle under specific constraints, such as time, distance, energy consumption, etc. The following are the implementation steps of the path optimization system:

[0076] Step 1: Data collection and processing: collect data such as trash bin locations, urban road networks, and traffic conditions, and pre-process the data, such as cleaning and standardization.

[0077] Step 2: Preliminary path planning, using graph theory algorithms such as Dijkstra and A*, calculate the shortest path from the current location to each trash can on the urban road network, and determine the priority of cleaning and disinfection based on the overflow prediction information of the trash can.

[0078] Step 3: Multi-objective optimization, using heuristic search algorithms such as genetic algorithms, comprehensively consider multiple optimization goals such as time, distance, energy consumption, etc., perform path optimization, and dynamically adjust path planning based on real-time traffic information.

[0079] Step 4: Path execution and dynamic adjustment. The vehicle performs cleaning and disinfection tasks according to the optimized path, monitors the vehicle status and external environment in real time, and uses a dynamic programming algorithm to adjust the path in case of traffic congestion.

[0080] Step 5: Feedback and iterative optimization, collect data during the execution process, such as actual driving time, energy consumption, etc., use machine learning algorithms to analyze the data, and continuously optimize the path planning model.

[0081] According to wireless communication, GPS positioning, remote control cleaning tasks, the camera senses the surrounding environment, and analyzes and processes the images and videos captured by the camera. It can realize functions such as target detection, object recognition, and path planning, helping vehicles to achieve intelligent driving and cleaning and disinfection operations. Through path planning and optimization algorithms, intelligent vehicles can complete the cleaning tasks of designated trash cans in the shortest route and the least time, reducing unnecessary round trips and empty driving.

[0082] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent garbage collection, garbage bin cleaning, disinfection and drying system, comprising a vehicle body (1) for garbage transportation, characterized in that: The vehicle body (1) is provided with a mechanical flip module (4) for controlling the position of a trash can. The vehicle body (1) is provided with a cleaning bin (2) and a trash bin (3). The mechanical flip module (4) controls the trash in the trash can to be dumped into the trash bin (3). The mechanical flip module (4) controls the trash can to be located in the cleaning bin (2) for cleaning. The cleaning bin (2) is provided with a filter module (5) for filtering and recycling sewage. The vehicle body (1) recognizes lanes and objects through a visual environment recognition module, and judges the feasibility of walking, the appropriate time and route of passing through through a vehicle drive module, thereby realizing movement path planning, environmental information processing, positioning and navigation, and image feature processing of the vehicle body (1).

2. The intelligent garbage collection, garbage bin cleaning, disinfection and drying system according to claim 1 is characterized in that: The mechanical turning module (4) comprises a propulsion module for controlling the horizontal movement of the trash can, a turning frame for turning the trash can, and a positioning frame for clamping and positioning the trash can. The trash can is identified and positioned, and a signal is transmitted to the propulsion module of the turning frame to position the trash can and realize the clamping and turning function.

3. The intelligent garbage collection, garbage bin cleaning, disinfection and drying system according to claim 1 is characterized in that: The cleaning chamber (2) is provided with four rotating roller brushes, the top and sides of the cleaning chamber (2) are provided with spraying devices, and the cleaning chamber (2) is provided with a disinfection and drying module and a high-speed rotating nozzle for spraying the surface of the garbage can.

4. The intelligent garbage collection, garbage bin cleaning, disinfection and drying system according to claim 1 is characterized in that: The filtering module (5) comprises a box for storing detergent, and a driving module for controlling the start and stop of the spraying device, the amount of water sprayed, and the cleaning operation.

5. The intelligent garbage collection, garbage bin cleaning, disinfection and drying system according to claim 1 is characterized in that: The visual environment recognition module captures images via a visual data transmission camera, and the visual environment recognition module includes the following steps to implement path and motion planning: S1, using sparse extraction methods to extract potential features to identify traffic participants such as lanes, obstacles, and moving objects; S2, select target candidates, determine lane lines, and form traversable spatial information in unstructured areas; S3, generate possible trajectories, and generate motion trajectories through trajectory optimization and neuron planning; S4, generate a movement plan and determine whether to forcibly cut into traffic or wait for pedestrians to pass; S5. When determining the trajectory, time, speed and motion plan, analyze the traffic conditions of other vehicles under the motion trajectory and output the motion plan after considering various factors.

6. The intelligent garbage collection, garbage bin cleaning, disinfection and drying system according to claim 1 is characterized in that: The vehicle driving module is implemented by the particle swarm algorithm, and the implementation code is as follows: import import numpy as np / / Import the NumPy library and name it np 7. The intelligent garbage collection, garbage bin cleaning, disinfection and drying system according to claim 6 is characterized in that: The specific steps used to implement the particle swarm algorithm are as follows: S1. Import the NumPy library and name it np; S2, start the iterative process, update each particle, calculate the fitness score of the particle, update the optimal position of the particle and the global optimal position, update the speed and position of the particle according to the PSO algorithm, perform collision detection, and avoid collision with obstacles; S3. Output the final path planning result, that is, the global best position global_best_position.

8. The intelligent garbage collection, garbage bin cleaning, disinfection and drying system according to claim 1 is characterized in that: It also includes a data processing warehouse, which is used to store the vehicle's intelligent algorithms and real-time monitoring data.

9. The intelligent garbage collection, garbage bin cleaning, disinfection and drying system according to claim 1 is characterized in that: It also includes processing and analyzing the acquired environmental information through an environmental perception algorithm, and constructing features by analyzing the directional gradients of pixels in the local area of ​​the image to achieve accurate identification of the trash can.

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