Smart community garbage classification system and method

Abstract

The invention relates to the technical field of garbage cleaning, in particular to a smart community garbage classification system and method.The system comprises a garbage picking module, a navigation module, a detection module, a collection control module, a garbage analysis module and an early warning module, the garbage picking module picks up and collects garbage, and the navigation module determines the picking area and position and sends the information to the detection module; the detection module analyzes and identifies the characteristics of garbage and classifies and records the garbage, the collection control module controls the pickup module to complete the pickup action, and the garbage analysis module determines the high-frequency garbage category of each area in the current pickup period and adjusts the pickup sub-period in the next period; according to the system, the accuracy and efficiency of garbage classification are improved, the requirement of manual classification is reduced, technical support is provided for sustainable development of a smart community, and through intelligent management and a dynamic adjustment mechanism, the system can adapt to changes generated by garbage, the operation cost is reduced, and reasonable utilization of resources is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage cleaning, and in particular to a smart community garbage classification system and method. Background Art

[0002] The technologies for sorting and picking up garbage in smart communities mainly include smart trash cans, IoT sensors, and data analysis systems. Smart trash cans are equipped with classification recognition technology, which automatically recognizes the type of items placed through sensors and guides users to place them correctly. IoT technology enables trash cans to monitor the filling status in real time, optimize garbage collection routes, and improve collection efficiency. At the same time, the data analysis system can summarize garbage classification data, provide feedback and optimization suggestions, thereby promoting the environmental awareness and participation of community residents and promoting the implementation of garbage classification.

[0003] Chinese Patent Publication No.: CN117141664A discloses a multi-machine cooperative sea surface garbage picking system and its control method, wherein the visual module obtains and accurately analyzes the size, quantity, position and motion state information of garbage, obstacles and waves on the sea surface, and inputs the data information into the central computing module; the sensor module identifies the size and direction of the resistance of waves and sea wind in the sea surface environment in real time, and transmits the corresponding data information into the central computing module; the central computing module processes the data information transmitted by the visual module and the sensor module, and formulates the task allocation strategy, the formation control strategy and the path planning strategy; the execution module executes the commands of the task allocation strategy, the formation control strategy and the path planning strategy, thereby realizing the coordinated cooperation, collection and transportation of the sea surface garbage recovery device; it can be seen that the multi-machine cooperative sea surface garbage picking system and its control method have the following problems: garbage classification is not performed when picking up garbage, and the subsequent garbage picking method is not determined according to the garbage picking situation, resulting in unnecessary waste of system resources during the picking process. Summary of the invention

[0004] To this end, the present invention provides a smart community garbage classification system and method to overcome the problem in the prior art that garbage classification is not performed when garbage is picked up and the subsequent garbage picking method is not determined according to the garbage picking situation, resulting in unnecessary waste of system resources during the picking process.

[0005] To achieve the above objectives, on the one hand, the present invention provides a smart community garbage classification system, comprising:

[0006] The garbage collection module includes a robotic arm group and a number of garbage collection units to complete the garbage collection and collection actions, as well as periodically pick up garbage in the community area;

[0007] A navigation module, which is connected to the garbage picking module and is used to determine the picking area and picking position of the garbage picking module;

[0008] A detection module, which is connected to the garbage picking module and the navigation module respectively, for obtaining characteristic information of each garbage, analyzing the characteristic information to determine the garbage collection category of the current garbage, and recording the total amount of each category of garbage in each picking area respectively;

[0009] A collection control module, which is connected to the garbage picking module and the detection module respectively, and is used to control the mechanical arm group to place the garbage into the corresponding garbage collection unit according to the garbage collection category of the current garbage determined by the detection module;

[0010] A garbage analysis module, which is connected to the garbage picking module, the detection module and the navigation module respectively, and is used to determine the high-frequency garbage category of each picking area according to the total amount of each category of garbage in a single picking cycle of each picking area; determine whether to adjust the next picking sub-cycle according to the high-frequency garbage category and the total amount of garbage picked in a single picking cycle, and determine the adjustment method of the picking sub-cycle;

[0011] An early warning module, which is connected to the garbage picking module and the navigation module respectively, and is used to control the garbage picking module to stop picking and control the navigation module to return;

[0012] The area of ​​the community area is the sum of the areas of each picking area, and the picking area is determined according to the type of each building in the community.

[0013] Further, the garbage picking module includes a mechanical arm group and a number of garbage collection units;

[0014] The mechanical arm group includes a gripping mechanical arm, a flat shovel mechanical arm and a push-sweep mechanical arm;

[0015] Each of the garbage collection units is connected to the robotic arm group, and the types of garbage collection units include recyclable garbage collection units, hazardous garbage collection units and other garbage collection units.

[0016] Furthermore, the detection module includes:

[0017] A detector group, which includes several types of sensors for collecting the characteristic information of the garbage;

[0018] an information processing unit connected to the detector group, for acquiring the characteristic information and determining the garbage collection category of the current garbage according to the characteristic information;

[0019] A collection and recording unit connected to the information processing unit, for determining the total amount of garbage of the corresponding category according to the volume of each category of garbage in each of the pickup areas;

[0020] The characteristic information includes image information, edge information, quality information and vibration sound information.

[0021] Further, the information processing unit in the detection module determines the garbage collection category of the current garbage according to the characteristic information of the garbage;

[0022] If the characteristic information of the garbage meets the direct identification condition, the information processing unit directly determines the garbage collection category of the current garbage;

[0023] If the characteristic information of the garbage does not meet the direct identification condition, the information processing unit analyzes the edge information, the quality information and the vibration sound information, and determines the garbage collection category of the current garbage according to the analysis result;

[0024] The garbage collection categories include recyclable garbage, hazardous garbage and other garbage; the direct identification condition is that the information processing unit directly determines the garbage collection category of the current garbage only through the image information of the current garbage.

[0025] Furthermore, the garbage analysis module determines the high-frequency garbage category in the current picking area according to the garbage categories and their total amount in a single picking cycle;

[0026] If the total amount of hazardous waste exceeds the corresponding high-frequency ratio, the waste analysis module determines that the high-frequency waste category in the current pickup area is a hazardous high-frequency category;

[0027] If the total amount of hazardous waste does not exceed the corresponding high-frequency ratio and the total amount of recyclable waste exceeds the corresponding high-frequency ratio, the waste analysis module determines that the high-frequency waste category in the current pickup area is a recyclable high-frequency category;

[0028] Among them, the high-frequency garbage categories include harmful high-frequency categories, recyclable high-frequency categories and other high-frequency categories.

[0029] Further, the garbage analysis module determines whether to adjust the picking sub-cycle according to the high-frequency garbage category in the current picking area;

[0030] If the high-frequency garbage category of the current picking area is different from the high-frequency garbage category of the previous picking cycle, the garbage analysis module determines that the picking sub-cycle of the current picking area needs to be adjusted.

[0031] Furthermore, the garbage analysis module determines the adjustment method of the picking sub-cycle according to the total amount of garbage picked up in a single picking cycle;

[0032] If the absolute value of the difference between the total amount of garbage picked up and the total amount of garbage picked up in the previous picking cycle is greater than the preset change difference and the difference is positive, then the number of picking sub-cycles is increased;

[0033] If the absolute value of the difference between the total amount of garbage picked up and the total amount of garbage picked up in the previous picking cycle is greater than the preset change difference and the difference is a negative number, the number of picking sub-cycles is reduced.

[0034] Furthermore, the garbage analysis module determines an adjustment amount of a final picking sub-cycle according to the total amount difference and changes in the high-frequency garbage categories.

[0035] Furthermore, the early warning module determines whether to terminate the garbage picking module from picking up garbage according to the collection amount of each garbage collection unit;

[0036] The early warning module determines whether to control the navigation module to return according to the termination result.

[0037] On the other hand, the present invention also provides a method for garbage classification in a smart community, comprising:

[0038] Determining the pickup community area and each of the pickup areas;

[0039] Periodically picking up garbage in each of the picking areas, determining the garbage collection category of each garbage, and collecting each garbage into a corresponding garbage collection unit;

[0040] Record the total amount of each type of garbage in each pickup area;

[0041] Determine the high-frequency garbage category of each pickup area based on the total amount of each category of garbage in a single pickup cycle of each pickup area;

[0042] According to the high-frequency garbage categories in the pickup area and the total amount of garbage picked up in a single pickup cycle, determine whether to adjust the next pickup sub-cycle in the current pickup area, and determine the adjustment method of the pickup sub-cycle.

[0043] Compared with the prior art, the beneficial effect of the present invention is that the present invention realizes the automatic picking, classification and intelligent management of garbage in the community by integrating garbage picking module, navigation module, detection module, collection control module, garbage analysis module and early warning module. The system can intelligently identify and classify garbage according to the characteristic information of garbage, such as image, edge, quality and vibration sound, to ensure that the garbage is correctly placed in the corresponding collection unit. In addition, the garbage analysis module dynamically adjusts the picking sub-cycle by analyzing the total amount of garbage and high-frequency garbage categories in each picking area, optimizes the picking route and frequency, and improves the operation efficiency. The early warning module is triggered when the garbage collection unit is full, instructing the robot to return to dump and record the location for continued picking. The present invention improves the accuracy and efficiency of garbage classification, reduces the need for manual classification, and reduces environmental pollution, providing technical support for the sustainable development of smart communities. Through intelligent management and dynamic adjustment mechanisms, the system can adapt to changes in garbage generation, reduce operating costs, and promote the rational use of resources.

[0044] Furthermore, through the information processing unit in the integrated detection module, the collection category of garbage can be intelligently determined based on the characteristic information of the garbage. When classifying garbage that does not meet the direct identification conditions, the category of the garbage is determined by comprehensively analyzing the edge information, quality information and oscillation sound information of the garbage. For hazardous garbage, due to its clear characteristic information, its category can be directly determined by the garbage recognition model. As for recyclable garbage and other garbage, when the image information is not sufficient to directly characterize the type of garbage, the pitch of the sound generated by the collision of liquids is analyzed to determine whether it is recyclable garbage, the regular edge ratio is used to determine whether the garbage is recyclable garbage, and the density of the garbage is calculated to distinguish other garbage from recyclable garbage, which improves the accuracy and efficiency of garbage classification, reduces the need for manual classification, promotes the recycling of resources, and reduces environmental pollution, providing technical support for the sustainable development of smart communities.

[0045] Furthermore, in the present invention, the garbage analysis module analyzes the total amount and proportion of different garbage categories in a single picking cycle and compares it with the preset high-frequency proportion threshold to determine the main garbage type in the current picking area. The garbage analysis module can monitor and optimize the garbage collection route in real time, improve the efficiency and accuracy of garbage classification, reduce resource waste, and promote the rational treatment of garbage. Through this intelligent garbage classification management, garbage collection work can be more effectively guided, resource allocation can be optimized, and operating costs can be reduced.

[0046] Furthermore, in the present invention, the garbage analysis module determines whether it is necessary to adjust the picking sub-cycle by comparing the high-frequency garbage category of the current picking cycle with that of the previous picking cycle. This dynamic adjustment mechanism makes the garbage sorting work more flexible and efficient. If the high-frequency garbage category changes, such as from a harmful high-frequency category to a recyclable high-frequency category, the number of picking sub-cycles will be reduced accordingly to optimize resource allocation and improve picking efficiency. On the contrary, the number of picking sub-cycles will be increased accordingly to ensure that all garbage can be processed in a timely manner. This method helps to reduce unnecessary picking actions, improve the working efficiency of the system, and ensure the accuracy and timeliness of garbage sorting. Through this intelligent adjustment mechanism, it is possible to better adapt to changes in garbage generation, improve the efficiency and effectiveness of garbage sorting, reduce operating costs, and promote the rational use of resources.

[0047] Furthermore, in the present invention, the garbage analysis module analyzes the change trend of garbage generation by comparing the total amount of garbage picked up in the current picking cycle with the total amount of garbage picked up in the previous cycle. When the increase in the total amount of garbage exceeds the preset change threshold, indicating that the amount of garbage generated has increased significantly, the module will increase the number of picking sub-cycles to ensure that the garbage is cleaned up in time. On the contrary, if the decrease in the total amount of garbage exceeds the preset threshold, indicating that the amount of garbage generated has decreased significantly, the module will reduce the number of picking sub-cycles to avoid excessive picking and waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a schematic diagram of the structure of a smart community garbage classification system according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the structure of a garbage picking module according to an embodiment of the present invention;

[0050] Figure 3 It is a structural schematic diagram of a detection module according to an embodiment of the present invention;

[0051] Figure 4 This is a flow chart of the application method of the smart community garbage classification system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0054] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0055] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] See also Figure 1 As shown, it is a structural schematic diagram of a smart community garbage classification method according to an embodiment of the present invention; the present invention provides a smart community garbage classification system, including:

[0057] The garbage collection module includes a robotic arm group and a number of garbage collection units to complete the garbage collection and collection actions, as well as periodically pick up garbage in the community area;

[0058] A navigation module, which is connected to the garbage picking module and is used to determine the picking area and picking position of the garbage picking module;

[0059] A detection module, which is connected to the garbage picking module and the navigation module respectively, for obtaining characteristic information of each garbage, analyzing the characteristic information to determine the garbage collection category of the current garbage, and recording the total amount of each category of garbage in each picking area respectively;

[0060] A collection control module, which is connected to the garbage picking module and the detection module respectively, and is used to control the mechanical arm group to place the garbage into the corresponding garbage collection unit according to the garbage collection category of the current garbage determined by the detection module;

[0061] A garbage analysis module, which is connected to the garbage picking module, the detection module and the navigation module respectively, and is used to determine the high-frequency garbage category of each picking area according to the total amount of each category of garbage in a single picking cycle of each picking area; determine whether to adjust the next picking sub-cycle according to the high-frequency garbage category and the total amount of garbage picked in a single picking cycle, and determine the adjustment method of the picking sub-cycle;

[0062] An early warning module, which is connected to the garbage picking module and the navigation module respectively, and is used to control the garbage picking module to stop picking and control the navigation module to return;

[0063] The area of ​​the community area is the sum of the areas of each picking area, and the picking area is determined according to the type of each building in the community.

[0064] In practice, the types of buildings in the community include residential buildings, commercial buildings, leisure facilities, and parking facilities, ground parking lots or ground garages. Each picking area only contains one type of building. The area and shape of each picking area are not exactly the same, and there is no intersection between the picking areas. A single building does not span two picking areas. Generally, the area of ​​a single picking area is 400 to 600m2.

[0065] The present invention does not limit the specific structures of the detection module, collection control module, garbage analysis module and early warning module, which can be composed of logic components, including a field programmable processor, a computer and a microprocessor in the computer.

[0066] The initial number of picking sub-cycles in each picking area is 3, and a single picking cycle is 1 day. If the community is too large, garbage sorting and picking can be completed through multiple smart community garbage sorting systems.

[0067] The number of picking sub-cycles indicates the number of times garbage is picked up in a single picking cycle. After the system completes picking up garbage in each picking sub-cycle, it will go to the next picking area or return to the starting point to wait, instead of continuously picking in one area. Generally, the actual picking time for a picking area is 1 hour.

[0068] The movement of the system can be achieved by any kind of moving mechanism, which is the prior art and is not specifically limited.

[0069] The present invention realizes the automatic picking, classification and intelligent management of garbage in the community by integrating garbage picking module, navigation module, detection module, collection control module, garbage analysis module and early warning module. The system can intelligently identify and classify garbage according to the characteristic information of garbage, such as image, edge, quality and vibration sound, to ensure that the garbage is correctly placed in the corresponding collection unit. In addition, the garbage analysis module dynamically adjusts the picking sub-cycle by analyzing the total amount of garbage and high-frequency garbage categories in each picking area, optimizes the picking route and frequency, and improves the operation efficiency. The early warning module is triggered when the garbage collection unit is full, instructing the robot to return to dump and record the location for continued picking. The present invention improves the accuracy and efficiency of garbage classification, reduces the need for manual classification, and reduces environmental pollution, providing technical support for the sustainable development of smart communities. Through intelligent management and dynamic adjustment mechanisms, the system can adapt to changes in garbage generation, reduce operating costs, and promote the rational use of resources.

[0070] See also Figure 2 As shown, it is a schematic diagram of the structure of a garbage picking module according to an embodiment of the present invention, wherein the garbage picking module includes a mechanical arm group and a plurality of garbage collecting units;

[0071] The mechanical arm group includes a gripping mechanical arm, a flat shovel mechanical arm and a push-sweep mechanical arm;

[0072] Each of the garbage collection units is connected to the robotic arm group, and the types of garbage collection units include recyclable garbage collection units, hazardous garbage collection units and other garbage collection units.

[0073] In implementation, each robot arm in the robot arm group can work individually or in combination. For example, if the current garbage is a beverage bottle, it can be picked up individually by a gripping robot arm. If the current garbage is a scattered garbage bag, a push-sweep robot arm and a flat shovel robot arm are required to complete the collection action at the same time. The robot arm group is a prior art and is not specifically limited thereto.

[0074] The volume of the hazardous waste collection unit is smaller than that of the recyclable waste collection unit, and the volume of the recyclable waste collection unit is smaller than that of other waste collection units.

[0075] The recyclable waste collection unit is used to store recyclables, including waste paper, plastic, glass, metal and cloth; the hazardous waste collection unit is used to store other waste (dry waste) and kitchen waste (wet waste); the hazardous waste collection unit is used to store hazardous waste, including batteries, fluorescent tubes, light bulbs, mercury thermometers, paint buckets, some household appliances, expired medicines and their containers, and expired cosmetics;

[0076] Among them, other garbage includes rubble, toilet waste paper, paper towels and other difficult-to-recycle waste and dust, food bags (boxes), and kitchen waste includes leftovers, bones, vegetable roots and leaves, fruit peels and other food waste.

[0077] See also Figure 3 As shown, it is a schematic diagram of the structure of the detection module of an embodiment of the present invention, and the detection module includes:

[0078] A detector group, which includes several types of sensors for collecting the characteristic information of the garbage;

[0079] an information processing unit connected to the detector group, for acquiring the characteristic information and determining the garbage collection category of the current garbage according to the characteristic information;

[0080] A collection and recording unit connected to the information processing unit, for determining the total amount of garbage of the corresponding category according to the volume of each category of garbage in each of the pickup areas;

[0081] The characteristic information includes image information, edge information, quality information and vibration sound information.

[0082] In implementation, the detector group also includes a camera for taking garbage images. The types of sensors include weight sensors and audio sensors. The information processing unit is also equipped with a microprocessor. The microprocessor can process the images collected by the camera, including cropping, scaling, normalization, etc., to improve the generalization ability and recognition accuracy of the model. This is a prior art and is not specifically limited.

[0083] The method of directly determining the collection category through image information can directly use the existing recognition model or establish a garbage recognition model.

[0084] The process of building the model includes: data collection, collecting a large amount of image data of different types of garbage, labeling their categories, and using them as training data sets. Processing the images and extracting key features, such as using edge detection, color histogram, etc.

[0085] Machine learning and deep learning, using deep learning models such as convolutional neural networks (CNNs) to effectively process and classify image data, train models using labeled datasets, verify model accuracy, and make adjustments to improve performance;

[0086] Real-time garbage identification and classification: When the system is running, the images captured by the camera are input into the trained model for analysis in real time. The model classifies the garbage into corresponding categories based on the identified features (such as shape, color, material, etc.);

[0087] Establishing a garbage identification model is a conventional existing technology and will not be described in detail.

[0088] Specifically, the information processing unit in the detection module determines the garbage collection category of the current garbage according to the characteristic information of the garbage;

[0089] If the characteristic information of the garbage meets the direct identification condition, the information processing unit directly determines the garbage collection category of the current garbage;

[0090] If the characteristic information of the garbage does not meet the direct identification condition, the information processing unit analyzes the edge information, the quality information and the vibration sound information, and determines the garbage collection category of the current garbage according to the analysis result;

[0091] The garbage collection categories include recyclable garbage, hazardous garbage and other garbage; the direct identification condition is that the information processing unit directly determines the garbage collection category of the current garbage only through the image information of the current garbage.

[0092] In implementation, each type of garbage in the garbage collection category corresponds to a type of garbage collection unit one by one.

[0093] The characteristic information of hazardous waste is relatively clear, and its collection category can be directly determined as hazardous waste through the waste identification model. On the contrary, the image information in the characteristic information of recyclables, kitchen waste and other waste is less direct in representing the current type of waste, so it is necessary to determine the collection category of the current waste by analyzing the quality information, edge information and vibration sound information;

[0094] In this implementation, if the oscillating sound information changes, and according to the pitch, it is determined that the current sound is generated by liquid collision, then the garbage collection category of the current garbage is determined to be recyclable garbage. The sound of liquid usually has a higher pitch because the vibration frequency of liquid is usually higher. For example, when liquid is poured into a container or bottle, the interaction between the liquid and the container will produce a higher frequency sound. The pitch is the inverse of the vocal cord vibration period, which can be determined by measuring the fundamental frequency of the sound signal;

[0095] If the garbage collection category cannot be determined based on the vibration sound information, the garbage collection category of the current garbage is determined based on the edge information. If the rule edge ratio of the current garbage is greater than the preset rule ratio, the garbage collection category of the current garbage is determined to be recyclable garbage, where the preset rule ratio is 0.7 and the rule edge is a straight line or a perfect circle arc;

[0096] If the regular edge ratio of the current garbage is less than or equal to the preset regular ratio, the density of the current garbage is simply determined based on the mass information and volume. If the density is within 0.2-0.95g / cm3, the current garbage collection category is other garbage. If it is not within the range, it is recyclable garbage.

[0097] In the present invention, the information processing unit in the integrated detection module can intelligently determine the collection category of garbage based on the characteristic information of the garbage. When classifying garbage that does not meet the direct identification conditions, the category of the garbage is determined by comprehensively analyzing the edge information, quality information and oscillation sound information of the garbage. For hazardous garbage, due to its clear characteristic information, its category can be directly determined by the garbage recognition model. As for recyclable garbage and other garbage, when the image information is not sufficient to directly characterize the type of garbage, the pitch of the sound generated by the collision of liquids is analyzed to determine whether it is recyclable garbage, the regular edge ratio is used to determine whether the garbage is recyclable garbage, and the density of the garbage is calculated to distinguish other garbage from recyclable garbage, thereby improving the accuracy and efficiency of garbage classification, reducing the need for manual classification, promoting the recycling of resources, and reducing environmental pollution, providing technical support for the sustainable development of smart communities.

[0098] Specifically, the garbage analysis module determines the high-frequency garbage category of the current pickup area based on the garbage categories and their total amount in a single pickup cycle;

[0099] If the total amount of hazardous waste exceeds the corresponding high-frequency ratio, the waste analysis module determines that the high-frequency waste category in the current pickup area is a hazardous high-frequency category;

[0100] If the total amount of hazardous waste does not exceed the corresponding high-frequency ratio and the total amount of recyclable waste exceeds the corresponding high-frequency ratio, the waste analysis module determines that the high-frequency waste category in the current pickup area is a recyclable high-frequency category;

[0101] Among them, the high-frequency garbage categories include harmful high-frequency categories, recyclable high-frequency categories and other high-frequency categories.

[0102] In implementation, the total amount accounts for the mass proportion, and the sum of the mass proportions of each type of garbage is 1; the high-frequency proportion of hazardous waste is 10% to 15%, and the high-frequency proportion of recyclable waste is 20% to 30%.

[0103] If the high-frequency waste category in the current pickup area is not the hazardous high-frequency category or the recyclable high-frequency category, it is other high-frequency category.

[0104] In the present invention, the garbage analysis module analyzes the total amount and proportion of different garbage categories in a single picking cycle and compares it with the preset high-frequency proportion threshold to determine the main garbage type in the current picking area. The garbage analysis module can monitor and optimize the garbage collection route in real time, improve the efficiency and accuracy of garbage classification, reduce resource waste, and promote the rational treatment of garbage. Through this intelligent garbage classification management, garbage collection work can be more effectively guided, resource allocation can be optimized, and operating costs can be reduced.

[0105] Specifically, the garbage analysis module determines whether to adjust the picking sub-cycle according to the high-frequency garbage category of the current picking area;

[0106] If the high-frequency garbage category of the current picking area is different from the high-frequency garbage category of the previous picking cycle, the garbage analysis module determines that the picking sub-cycle of the current picking area needs to be adjusted.

[0107] During implementation, if the high-frequency garbage category changes from a harmful high-frequency category to a recyclable high-frequency category, the number of picking sub-cycles will be reduced by one; if it changes from a harmful high-frequency category to other high-frequency categories, the number of picking sub-cycles will be reduced by two; if it changes from a recyclable high-frequency category to other high-frequency categories, the number of picking sub-cycles will be reduced by one; otherwise, the corresponding number of picking sub-cycles will be increased.

[0108] In the present invention, the garbage analysis module determines whether it is necessary to adjust the picking sub-cycle by comparing the high-frequency garbage categories of the current picking cycle with those of the previous picking cycle. This dynamic adjustment mechanism makes garbage sorting more flexible and efficient. If the high-frequency garbage category changes, such as from a harmful high-frequency category to a recyclable high-frequency category, the number of picking sub-cycles will be reduced accordingly to optimize resource allocation and improve picking efficiency. On the contrary, the number of picking sub-cycles will be increased accordingly to ensure that all garbage can be processed in a timely manner. This method helps to reduce unnecessary picking actions, improve the working efficiency of the system, and ensure the accuracy and timeliness of garbage sorting. Through this intelligent adjustment mechanism, it is possible to better adapt to changes in garbage generation, improve the efficiency and effectiveness of garbage sorting, reduce operating costs, and promote the rational use of resources.

[0109] Specifically, the garbage analysis module determines the adjustment method of the picking sub-cycle according to the total amount of garbage picked up in a single picking cycle;

[0110] If the absolute value of the difference between the total amount of garbage picked up and the total amount of garbage picked up in the previous picking cycle is greater than the preset change difference and the difference is positive, then the number of picking sub-cycles is increased;

[0111] If the absolute value of the difference between the total amount of garbage picked up and the total amount of garbage picked up in the previous picking cycle is greater than the preset change difference and the difference is a negative number, the number of picking sub-cycles is reduced.

[0112] In practice, the total amount of garbage is represented by the mass of the garbage. The preset change difference is 0.2 times the total amount of garbage picked up in one picking cycle.

[0113] The number of changes in the picking sub-period is determined by rounding down the ratio of the total difference to the preset change difference;

[0114] For example, the total difference is -0.5, and its absolute value is 0.5, which is greater than the preset change difference and the difference is a negative number, so the number of picking sub-cycles is reduced by 2.

[0115] If the number of picking sub-cycles after the reduction is less than 0, there will be only one picking sub-cycle for the corresponding picking area in the next picking cycle, and the maximum value of the picking sub-cycle does not exceed 6.

[0116] In the present invention, the garbage analysis module analyzes the change trend of garbage generation by comparing the total amount of garbage picked up in the current picking cycle with the previous cycle. When the increase in the total amount of garbage exceeds the preset change threshold, it indicates that the garbage generation has increased significantly. At this time, the module will increase the number of picking sub-cycles to ensure that the garbage is cleaned up in time. On the contrary, if the decrease in the total amount of garbage exceeds the preset threshold, it indicates that the garbage generation has decreased significantly. The module will reduce the number of picking sub-cycles to avoid excessive picking and waste of resources.

[0117] Specifically, the garbage analysis module determines the adjustment amount of the final picking sub-cycle according to the total amount difference and the change of the high-frequency garbage category.

[0118] Specifically, the early warning module determines whether to terminate the garbage picking module from picking up garbage according to the collection amount of each garbage collection unit;

[0119] The early warning module determines whether to control the navigation module to return according to the termination result.

[0120] During implementation, if the collection amount of any garbage collection unit exceeds the preset collection amount, the early warning module controls the garbage picking module to terminate picking, and controls the navigation module to return, while recording the location where the picking was terminated. After returning to dump the garbage, it returns to the location where the picking was terminated and continues picking in the current area.

[0121] See also Figure 4 As shown, it is a flow chart of the application method of the smart community garbage classification system according to an embodiment of the present invention. The present invention also provides a method for garbage classification in a smart community, including:

[0122] Step S1, determining the picking community area and each picking area;

[0123] Step S2, periodically picking up garbage in each of the picking areas, determining the garbage collection category of each garbage, and collecting each garbage into a corresponding garbage collection unit;

[0124] Step S3, recording the total amount of each type of garbage in each pickup area;

[0125] Step S4, determining the high-frequency garbage category of each picking area according to the total amount of each category of garbage in a single picking cycle of each picking area;

[0126] Step S5, determining whether to adjust the next picking sub-cycle of the current picking area according to the high-frequency garbage category of the picking area and the total amount of garbage picked up in a single picking cycle, and determining the adjustment method of the picking sub-cycle.

[0127] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0128] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart community garbage classification system, characterized in that: include: The garbage collection module includes a robotic arm group and a number of garbage collection units to complete the garbage collection and collection actions, as well as periodically pick up garbage in the community area; A navigation module, which is connected to the garbage picking module and is used to determine the picking area and picking position of the garbage picking module; A detection module, which is connected to the garbage picking module and the navigation module respectively, for obtaining characteristic information of each garbage, analyzing the characteristic information to determine the garbage collection category of the current garbage, and recording the total amount of each category of garbage in each picking area respectively; A collection control module, which is connected to the garbage picking module and the detection module respectively, and is used to control the mechanical arm group to place the garbage into the corresponding garbage collection unit according to the garbage collection category of the current garbage determined by the detection module; A garbage analysis module, which is connected to the garbage picking module, the detection module and the navigation module respectively, and is used to determine the high-frequency garbage category of each picking area according to the total amount of each category of garbage in a single picking cycle of each picking area; determine whether to adjust the next picking sub-cycle according to the high-frequency garbage category and the total amount of garbage picked in a single picking cycle, and determine the adjustment method of the picking sub-cycle; An early warning module, which is connected to the garbage picking module and the navigation module respectively, and is used to control the garbage picking module to stop picking and control the navigation module to return; The area of ​​the community area is the sum of the areas of each picking area, and the picking area is determined according to the type of each building in the community.

2. The smart community garbage classification system according to claim 1 is characterized in that: The garbage picking module includes a mechanical arm group and a number of garbage collection units; The mechanical arm group includes a gripping mechanical arm, a flat shovel mechanical arm and a push-sweep mechanical arm; Each of the garbage collection units is connected to the robotic arm group, and the types of garbage collection units include recyclable garbage collection units, hazardous garbage collection units, and other garbage collection units.

3. The smart community garbage classification system according to claim 2 is characterized in that: The detection module comprises: A detector group, which includes several types of sensors for collecting the characteristic information of the garbage; an information processing unit connected to the detector group, for acquiring the characteristic information and determining the garbage collection category of the current garbage according to the characteristic information; A collection and recording unit connected to the information processing unit, for determining the total amount of garbage of the corresponding category according to the volume of each category of garbage in each of the picking areas; The characteristic information includes image information, edge information, quality information and vibration sound information.

4. The smart community garbage classification system according to claim 3 is characterized in that: The information processing unit in the detection module determines the garbage collection category of the current garbage according to the characteristic information of the garbage; If the characteristic information of the garbage meets the direct identification condition, the information processing unit directly determines the garbage collection category of the current garbage; If the characteristic information of the garbage does not meet the direct identification condition, the information processing unit analyzes the edge information, the quality information and the vibration sound information, and determines the garbage collection category of the current garbage according to the analysis result; The garbage collection categories include recyclable garbage, hazardous garbage and other garbage; the direct identification condition is that the information processing unit directly determines the garbage collection category of the current garbage only through the image information of the current garbage.

5. The smart community garbage classification system according to claim 4 is characterized in that: The garbage analysis module determines the high-frequency garbage category in the current picking area based on the garbage categories and their total amount in a single picking cycle; If the total amount of hazardous waste exceeds the corresponding high-frequency ratio, the waste analysis module determines that the high-frequency waste category in the current pickup area is a hazardous high-frequency category; If the total amount of hazardous waste does not exceed the corresponding high-frequency ratio and the total amount of recyclable waste exceeds the corresponding high-frequency ratio, the waste analysis module determines that the high-frequency waste category in the current pickup area is a recyclable high-frequency category; Among them, the high-frequency garbage categories include harmful high-frequency categories, recyclable high-frequency categories and other high-frequency categories.

6. The smart community garbage classification system according to claim 5 is characterized in that: The garbage analysis module determines whether to adjust the picking sub-cycle according to the high-frequency garbage category in the current picking area; If the high-frequency garbage category of the current picking area is different from the high-frequency garbage category of the previous picking cycle, the garbage analysis module determines that the picking sub-cycle of the current picking area needs to be adjusted.

7. The smart community garbage classification system according to claim 6 is characterized in that: The garbage analysis module determines the adjustment method of the picking sub-cycle according to the total amount of garbage picked up in a single picking cycle; If the absolute value of the difference between the total amount of garbage picked up and the total amount of garbage picked up in the previous picking cycle is greater than the preset change difference and the difference is positive, then the number of picking sub-cycles is increased; If the absolute value of the total difference between the total amount of garbage picked up and the total amount of garbage picked up in the previous picking cycle is greater than the preset change difference and the difference is a negative number, the number of picking sub-cycles is reduced.

8. The smart community garbage classification system according to claim 7 is characterized in that: The garbage analysis module determines the adjustment amount of the final picking sub-cycle according to the total amount difference and the change of the high-frequency garbage category.

9. The smart community garbage classification system according to claim 8, characterized in that: The early warning module determines whether to stop the garbage picking module from picking up garbage according to the collection amount of each garbage collection unit; The early warning module determines whether to control the navigation module to return according to the termination result.

10. A method for applying the smart community garbage classification system according to any one of claims 1 to 9, characterized in that: include: Determining the pickup community area and each of the pickup areas; Periodically picking up garbage in each of the picking areas, determining the garbage collection category of each garbage, and collecting each garbage into a corresponding garbage collection unit; Record the total amount of each type of garbage in each pickup area; Determine the high-frequency garbage category of each pickup area based on the total amount of each category of garbage in a single pickup cycle of each pickup area; According to the high-frequency garbage categories in the pickup area and the total amount of garbage picked up in a single pickup cycle, determine whether to adjust the next pickup sub-cycle in the current pickup area, and determine the adjustment method of the pickup sub-cycle.

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