Intelligent environmental sanitation garbage classification monitoring and feedback system

Abstract

The invention relates to the technical field of environmental engineering, in particular to an intelligent environmental sanitation garbage classification monitoring and feedback system, a garbage can is provided with a controllable electric can cover and is composed of different classification garbage cans, and each can is provided with an independent electric switch. The garbage can comprises a garbage bag storage box, an information acquisition device, a softness acquisition device, an in-can garbage detection module, a data analysis module, a garbage judgment module and a control module. The information acquisition device acquires garbage weight, image information and user input information. The softness acquisition device detects garbage softness. The in-can garbage detection module determines classification accuracy. The data analysis module determines garbage types. The garbage determination module verifies analysis results. The control module controls the box cover to be opened according to analysis data. And whether split charging or weight adjustment can be judged. According to the invention, the garbage thrown in the community is precisely classified, so that the subsequent garbage sorting difficulty is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage disposal, and in particular to an intelligent sanitation garbage classification monitoring and feedback system. Background Art

[0002] Waste sorting is fundamental to the operation of terminal waste treatment facilities. Implementing household waste sorting can effectively improve the urban and rural environment and promote resource recycling. Based on the scientific and rational classification of household waste, a corresponding supporting system for household waste sorting should be developed, and terminal treatment facilities that are connected to waste sorting should be improved to ensure the interconnection of classified collection, recycling, utilization, and treatment facilities.

[0003] Chinese Patent Publication No. CN105772403A discloses a garbage sorting and detection system, comprising a belt conveyor, a garbage claw, a camera, and a computer processing system. The belt conveyor is used to transport garbage bags containing garbage, each of which is provided with information about the type of garbage and an identification code containing information about the garbage owner. The garbage claw is controlled by a drive mechanism to place the garbage bags onto the belt conveyor. The camera is used to capture the type information and identification codes of the garbage bags on the belt conveyor and feed them back to the computer processing system. The computer processing system calculates the garbage disposal fee based on the type, quantity, and price of the garbage in the garbage bags and feeds it back to the garbage owner. The garbage sorting and detection system of the present invention automates garbage sorting, detection, and cost management, significantly improving the efficiency of garbage disposal, thereby bringing the social benefit of a cleaner and more comfortable living environment.

[0004] It can be seen that the existing technology has the following problems. At this stage, some community residents do not sort the garbage in advance when disposing of garbage, or put the sorted garbage into other types of garbage bins, which increases the difficulty of subsequent garbage sorting, wastes a lot of manpower and material resources, and causes environmental pollution. Summary of the Invention

[0005] To this end, the present invention provides an intelligent sanitation garbage classification monitoring and feedback system to overcome the problem in the existing technology that some community residents do not classify the garbage in advance when disposing of the garbage, or put the classified garbage into other types of garbage bins, which increases the difficulty of subsequent garbage sorting and wastes a lot of manpower and material resources.

[0006] To achieve the above objectives, the present invention provides an intelligent sanitation waste classification monitoring and feedback system, comprising: Trash bins, used for holding trash, are equipped with a controllable electric lid; including a trash bin set, which is composed of several trash bins of different categories placed side by side, wherein each trash bin is provided with an electric switch that can be individually controlled to open or close; Garbage bag storage box, used for placing garbage bags when sorting garbage; An information collection device for collecting spam information and user input information; A softness collecting device, which is arranged at the lower part of the information collecting device and is used to collect the softness of the garbage bag; The garbage detection module is used to detect the proportion of corresponding garbage types in each classification of garbage bins to determine the accuracy of garbage classification; a data analysis module, connected to the information collection device and the garbage bin detection module, respectively, to determine the type of garbage based on the garbage weight information and the garbage image information to obtain a first determination result; compare the first determination result with the input information to obtain a garbage classification comparison result; and determine whether garbage separation is required based on the garbage classification comparison result, or adjust the weight of the garbage weight information and the garbage image information on the first determination result; A garbage determination module is used to determine whether the analysis result of the data analysis module is correct based on the data of the garbage detection module in the bucket; a control module connected to the trash bin group and controlling the opening of the electric lid of the trash bin group according to the analysis data of the data analysis module; The junk information includes junk weight information and junk image information; and the input information is the type of junk to be delivered actively selected by the user.

[0007] Furthermore, the information collection device includes: A carrying platform for placing garbage to be inspected; a camera, disposed on top of the carrying platform, for capturing images of the garbage placed on the carrying platform; The interactive device is set in the middle of the test platform and is used to complete the interaction between the system and the user; The interactive device includes an input keyboard and a display screen. The input keyboard is used to collect information input by the user, and the display screen is used to display feedback information.

[0008] Furthermore, the information collection device identifies the junk image formed by the junk image information, extracts characteristic elements of the junk to obtain actual characteristic elements, the data analysis module compares the actual characteristic elements with the standard characteristic elements to obtain a degree of consistency between the characteristic elements, and determines the type of junk corresponding to the junk image based on the degree of consistency between the characteristic elements; Among them, the characteristic elements include the number of prominent points and the softness of the garbage.

[0009] Furthermore, the data analysis module obtains the actual garbage classification matching degree based on the overall garbage type information and the input information, compares the actual garbage classification matching degree with the standard garbage classification matching degree to obtain a garbage classification matching degree comparison result, and determines whether the garbage needs to be repackaged, or whether to control the electric box cover to open, based on the garbage classification matching degree comparison result.

[0010] Furthermore, the data analysis module includes: A first data analysis unit is used to make an initial determination of the garbage material based on the number of prominent points in the garbage image; The second data analysis unit is used to modify the initial determination result according to the softness of the garbage; The third data analysis unit is used to determine whether to perform a secondary determination on the type of garbage based on a comparison result between the actual garbage weight and the garbage weight within the standard garbage weight range.

[0011] Furthermore, the garbage determination module determines whether to place the garbage as a whole, or whether to adjust the standard softness index range, based on the secondary determination result of the third data analysis unit.

[0012] Furthermore, the control module controls the electric box cover. If the actual garbage weight is less than the minimum standard garbage weight, the corresponding standard softness index range will be expanded according to the difference between the minimum standard garbage weight and the actual garbage weight; If the actual garbage weight is within the standard garbage weight range, the garbage is determined to be Class A garbage, and the control module controls the electric box cover to open the electric box cover to put the garbage in; If the actual garbage weight is greater than the maximum standard garbage weight, the corresponding standard softness index range will be narrowed according to the difference between the actual garbage weight and the maximum standard garbage weight.

[0013] Furthermore, the garbage detection module collects secondary garbage data on the garbage in the trash bin to generate garbage data in the bin, and determines the type of garbage in the bin based on the garbage data in the bin.

[0014] Furthermore, the determination module compares the type of garbage in the bin with the secondary determination result to obtain the accuracy of garbage classification in the bin, and determines whether to adjust the weight of the garbage weight information to the overall weight of the garbage based on the comparison result of the garbage classification accuracy in the bin and the garbage classification accuracy in the standard garbage classification accuracy range, or to increase consumption points for the user.

[0015] Furthermore, the determination module makes a determination based on the garbage classification accuracy comparison result. If the accuracy of garbage classification in the bin is less than the minimum value of the standard garbage classification accuracy interval, the weight will be increased according to the difference between the minimum value of the standard garbage classification accuracy interval and the accuracy of garbage classification in the bin; If the accuracy of garbage classification in the bin is within the standard garbage classification accuracy range, the original weight will remain unchanged; If the accuracy of garbage classification in the bin is greater than the maximum value of the standard garbage classification accuracy range, the user will be given additional consumption points.

[0016] Compared with existing technologies, accurate garbage sorting and delivery can be achieved by setting up a group of garbage bins with electric lids, avoiding the mixing of different types of garbage. Each garbage bin has an independent switch, which is turned on only when the corresponding type of garbage is detected, thereby improving the spatial resolution of garbage softness detection and adapting to the detection needs of garbage of different shapes. The standardized detection contact surface ensures the consistency of pressure testing of garbage of different volumes, monitors the composition of garbage in the bin in real time, and provides data support for classification accuracy evaluation and system optimization. Through the fusion of image recognition and weight data, the proportion of each type of garbage is calculated, and the corresponding standard interval is compared to generate an accuracy score, which is used to adjust the model weight or user incentives. By comparing the user input information with the actual detection data, the weight ratio of weight and image information is automatically adjusted.

[0017] Furthermore, a fixed detection position is provided for garbage to ensure the consistency of the camera shooting angle and softness detection pressure, avoid image distortion or pressure detection errors caused by the offset of garbage placement, and improve image recognition accuracy. The size of the carrier platform is adapted to common garbage bags and can meet the specific needs of the scene. Combined with the image recognition algorithm, it effectively improves the accuracy of material recognition of recyclables, especially for garbage with regular shapes (such as cardboard boxes and cans). The comparison of user input information and actual detection results can generate classification error data for adjusting the model weight. The cooperation between the carrier platform and the camera realizes the standardization of the position and angle of garbage detection, providing a high-quality data foundation for AI recognition.

[0018] Furthermore, by applying dual thresholds based on the length and angle of prominent points, the system transforms traditional image recognition "shape analysis" into quantifiable geometric features, solving the challenge of distinguishing soft from hard materials. By deeply integrating visual features (prominent points) with physical features (softness), a dual "visual-tactile" detection approach is formed, improving coverage of common household waste sorting scenarios. Automatically adjusting feature parameters based on historical data enables the system to self-learn and quickly adapt to changing waste characteristics across regions and seasons. This feature extraction and analysis method, through improved hardware precision and innovative algorithms, significantly enhances the intelligence of the waste sorting system, providing key technical support for achieving a complete closed-loop system of "accurate identification - automatic classification - efficient processing."

[0019] Furthermore, matching assessments combine real-time data with historical experience to avoid misjudgments or overly strict "one-size-fits-all" standards. From user declaration, system detection, lid control, to packaging feedback, a complete behavioral guidance chain is formed, achieving continuous improvement in classification accuracy. By reducing mixed waste and the need for manual sorting, back-end processing costs are significantly reduced, while also extending equipment life (due to reduced damage to lids caused by hard foreign objects).

[0020] Furthermore, a three-dimensional classification system based on "prominent points (visual) - softness (tactile) - weight (density)" has been constructed, breaking through the bottleneck of a single dimension and achieving three-dimensional recognition of material, shape, and density. Weight-driven adjustment of softness ranges enables the system to intelligently accommodate "lightweight / high-density" variations in waste. Data accumulation throughout the entire process, from initial assessment to correction to secondary assessment, supports continuous iteration of the AI ​​model, and classification capabilities increase exponentially with time. Through precise inter-module collaboration and data-driven design, this system not only significantly improves waste classification accuracy but also establishes a self-evolving intelligent classification system, providing a practical technical paradigm for intelligent urban sanitation.

[0021] Furthermore, by combining weight deviation and softness compliance rate, misjudgment caused by a single weight abnormality can be avoided. The weight ratios of weight, softness, and image can be adjusted in real time based on the secondary detection results, giving the system the ability to self-evolve.

[0022] Furthermore, for mixed garbage with abnormal weight, by increasing the weight factor, the system's sensitivity to identifying "weighed but abnormal composition" will be improved, reducing the misjudgment rate. Points can be exchanged for garbage bags or community services, forming a positive cycle of "accurate placement - getting rewards." Through the nonlinear mapping formula of accuracy and weight, accurate correction of classification errors can be achieved, avoiding system shocks caused by "one-size-fits-all" adjustments. The points mechanism not only improves participation, but also feeds back model optimization through user behavior data, forming a closed loop of "manual classification - system learning - more accurate identification." The standard accuracy range and weight adjustment strategy evolve dynamically with historical data and processing needs, so that the system maintains high robustness in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the intelligent sanitation waste classification monitoring and feedback system described in this embodiment; Figure 2 This is a flow chart of the determination process of the data analysis module of the intelligent sanitation waste classification monitoring and feedback system described in this embodiment; Figure 3 This is a flowchart of garbage placement determination for the intelligent sanitation garbage classification monitoring and feedback system described in this embodiment; Figure 4 This is a flow chart of weight changes in the intelligent sanitation waste classification monitoring and feedback system described in this embodiment. DETAILED DESCRIPTION

[0024] 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.

[0025] 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 scope of protection of the present invention.

[0026] 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 accompanying drawings. This is only 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.

[0027] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0028] See also Figure 1-4 As shown, Figure 1 This is a flow chart of the intelligent sanitation waste classification monitoring and feedback system described in this embodiment; Figure 2 This is a flow chart of the determination process of the data analysis module of the intelligent sanitation waste classification monitoring and feedback system described in this embodiment; Figure 3 This is a flowchart of garbage placement determination for the intelligent sanitation garbage classification monitoring and feedback system described in this embodiment; Figure 4 This is a flow chart of weight changes in the intelligent sanitation waste classification monitoring and feedback system described in this embodiment.

[0029] This embodiment provides an intelligent sanitation waste classification monitoring and feedback system, including: Trash bins, used for holding trash, are equipped with a controllable electric lid; including a trash bin set, which is composed of several trash bins of different categories placed side by side, wherein each trash bin is provided with an electric switch that can be individually controlled to open or close; Garbage bag storage box, used for placing garbage bags when sorting garbage; An information collection device for collecting spam information and user input information; A softness collection device, disposed below the information collection device, for collecting the softness of the garbage bag, comprises two sets of opposed vertical merging uprights, wherein each merging upright is provided with a displacement sensor and a plurality of pressure sensors. The displacement sensor is used to collect the distance the garbage bag retracts when pressure is applied to the garbage bag, and the pressure sensor is used to collect the pressure value of the garbage bag rebounding when pressure is applied to the garbage bag; The garbage detection module in the bin determines the accuracy of garbage classification by detecting the proportion of corresponding garbage types in each classified garbage bin; a data analysis module, connected to the information collection device and the garbage bin detection module, respectively, to determine the type of garbage based on the garbage weight information and the garbage image information to obtain a first determination result; compare the first determination result with the input information to obtain a garbage classification comparison result; and determine whether garbage separation is required based on the garbage classification comparison result, or adjust the weight of the garbage weight information and the garbage image information on the first determination result; A garbage determination module is used to determine whether the analysis result of the data analysis module is correct based on the data of the garbage detection module in the bucket; a control module connected to the trash bin group and controlling the opening of the electric lid of the trash bin group according to the analysis data of the data analysis module; The junk information includes junk weight information and junk image information; and the input information is the type of junk to be delivered actively selected by the user.

[0030] The combined vertical plate is set to a 20cm×30cm rectangular plane, and the pressure sensors are arranged in a 6×8 array (48 in total), with a distribution density of 1 per 12.5cm. 2 .

[0031] By setting up a group of trash cans with electric lids, accurate garbage sorting can be achieved to avoid mixing different types of garbage. Each trash can has an independent switch and is only turned on when the corresponding type of garbage is detected. This improves the spatial resolution of garbage softness detection and adapts to the detection needs of garbage of different shapes. Standardized detection contact surface ensures the consistency of pressure testing of garbage of different volumes, and monitors the composition of garbage in the bin in real time to provide data support for classification accuracy evaluation and system optimization. Through the fusion of image recognition and weight data, the proportion of each type of garbage is calculated, and the corresponding standard interval is compared to generate an accuracy score, which is used to adjust the model weight or user incentives. By comparing the user input information with the actual detection data, the weight ratio of weight and image information is automatically adjusted.

[0032] Specifically, the information collection device includes: A carrying platform for placing garbage to be inspected; A camera is provided on the top of the carrying platform and is used to collect image information of garbage placed on the carrying platform; The interactive device is set in the middle of the test platform and is used to complete the interaction between the system and the user; The interactive device includes an input keyboard and a display screen. The input keyboard is used to collect information input by the user, and the display screen is used to display feedback information.

[0033] Providing a fixed detection position for garbage ensures consistency in camera shooting angles and softness detection pressure, avoids image distortion or pressure detection errors caused by offset garbage placement, and improves image recognition accuracy. The carrier platform size is adapted to common garbage bags and can meet the specific needs of the scene. Combined with image recognition algorithms, it effectively improves the accuracy of material recognition of recyclables, especially for regularly shaped garbage (such as cardboard boxes and cans). Comparing user input information with actual detection results can generate classification error data for adjusting model weights. The cooperation between the carrier platform and the camera realizes the standardization of the position and angle of garbage detection, providing a high-quality data foundation for AI recognition.

[0034] Specifically, the information collection device identifies the junk image formed by the junk image information, extracts the characteristic elements of the junk to obtain the actual characteristic elements, the data analysis module compares the actual characteristic elements with the standard characteristic elements to obtain the degree of consistency of the characteristic elements, and determines the type of junk corresponding to the junk image based on the degree of consistency of the characteristic elements; Among them, the characteristic elements include the number of prominent points and the softness of the garbage.

[0035] In this embodiment, the user places a garbage bag on the carrying platform. At this time, the camera takes a picture of the garbage bag to obtain a garbage image. The camera uses a high-resolution camera. The data analysis module analyzes the number of prominent points on the garbage image, wherein the prominent point is determined according to the prominent point length and the prominent point angle. A prominent point is a point whose length is greater than a preset value of the maximum length of the garbage image and whose prominent point angle is less than a preset angle. The prominent point is calculated. For example, in this embodiment, the maximum length of the detected garbage image is 50 cm, the preset length value is set to 0.2, and the preset angle is set to 120°. The preset length value is determined according to historical relevant data, and the preset angle is determined according to historical relevant data. Prominent points with a prominent point length greater than 10 cm are marked, and the prominent point angle of the marked prominent point is detected. If the prominent point angle of the marked prominent point is less than the preset angle, then this point is determined to be a prominent point and recorded as a prominent point.

[0036] By using dual thresholds for both the length and angle of prominent points, this system transforms traditional image recognition "shape analysis" into quantifiable geometric features, solving the challenge of distinguishing soft from hard materials. By deeply integrating visual features (prominent points) with physical features (softness), it forms a dual "visual-tactile" detection system, improving coverage of common household waste sorting scenarios. Automatically adjusting feature parameters based on historical data enables the system to self-learn and quickly adapt to changing waste characteristics across regions and seasons. This feature extraction and analysis method, through improved hardware precision and innovative algorithms, significantly enhances the intelligence of the waste sorting system, providing key technical support for achieving a complete closed-loop system of "accurate identification - automatic classification - efficient processing."

[0037] Specifically, the data analysis module obtains the actual garbage classification matching degree based on the overall garbage type information and input information, compares the actual garbage classification matching degree with the standard garbage classification matching degree to obtain a garbage classification matching degree comparison result, and determines whether the garbage needs to be repackaged, or whether to control the electric box cover to open, based on the garbage classification matching degree comparison result.

[0038] Matching accuracy is determined by combining real-time data with historical experience, avoiding misjudgments or overly strict "one-size-fits-all" standards. From user declaration, system detection, lid control, to packaging feedback, a complete behavioral guidance chain is formed, achieving continuous improvement in classification accuracy. By reducing mixed waste and the need for manual sorting, back-end processing costs are significantly reduced, while also extending equipment life (due to reduced damage to lids caused by hard foreign objects).

[0039] Specifically, the data analysis module includes: A first data analysis unit is used to make an initial determination of the garbage material based on the number of prominent points in the garbage image; The second data analysis unit is used to modify the initial determination result according to the softness of the garbage; The third data analysis unit is used to determine whether to perform a secondary determination on the type of garbage based on a comparison result between the actual garbage weight and the garbage weight within the standard garbage weight range.

[0040] Specifically, the garbage determination module determines whether to place the garbage as a whole, or whether to adjust the standard softness index range, based on the secondary determination result of the third data analysis unit.

[0041] Specifically, the control module controls the electric box cover. If the actual garbage weight is less than the minimum standard garbage weight, the corresponding standard softness index range will be expanded according to the difference between the minimum standard garbage weight and the actual garbage weight; If the actual garbage weight is within the standard garbage weight range, the garbage is determined to be Class A garbage, and the control module controls the electric box cover to open the electric box cover to put the garbage in; If the actual garbage weight is greater than the maximum standard garbage weight, the corresponding standard softness index range will be narrowed according to the difference between the actual garbage weight and the maximum standard garbage weight.

[0042] The first data analysis unit is used to make an initial judgment on the garbage material based on the number of prominent points in the garbage image. For example, if the number of prominent points is greater than 10, the garbage bag is initially determined to contain Class I garbage, where Class I garbage includes metal and glass. Setting the threshold for the number of prominent points for Class I garbage to 10 can effectively distinguish metal and glass from other materials. In daily life in a residential area, most garbage bags contain household garbage, so garbage types can be better distinguished. If the number of prominent points is between 7 and 10, the garbage bag is initially determined to contain Class II garbage, where Class II garbage includes plastic and fabric. In daily life, residents have a high demand for bottled liquids, so the threshold for the number of prominent points for plastic and fabric is set between 7 and 10. If the number of protruding points is between 4 and 6, the garbage bag is initially determined to contain Category 3 garbage, which includes paper and wood. For Category 3 garbage, the items inside do not significantly affect the number of protruding points, so the threshold for the number of protruding points is set between 4 and 6. If the number of protruding points is between 0 and 3, it is initially determined that the garbage bag contains four types of garbage, wherein the four types of garbage include kitchen waste and domestic waste. For the four types of garbage, since most of the items are relatively soft and cannot support the garbage bag, resulting in too many protruding points, the number of protruding points is set between 0 and 3; The second data analysis unit is used to modify the initial determination result according to the softness of the garbage; If the initial determination result is any type of garbage, the control module controls the softness acquisition device to squeeze the garbage bag, wherein the pressure sensors on the softness acquisition device are evenly distributed on the two combined vertical plates. At this time, the softness acquisition device determines the squeezing pressure value based on the initial determination result, and squeezes the garbage bag for a preset distance. The squeezing contact area is a constant value. Then, the actual softness index corresponding to any type of garbage is the displacement distance of the garbage bag squeezed divided by the pressure value. The actual softness index is compared with the standard softness index of a type of garbage to obtain a softness index comparison result. Based on the softness index comparison result, it is determined whether to correct the initial determination result. For example, this embodiment uses a type of garbage as an example. The standard softness index of Class I garbage is set to 0.5 mm / N, the displacement distance of Class I garbage is 1.8 mm, and the pressure value is 10 N. The actual softness index of Class I garbage is 0.8. The actual softness index of Class I garbage is not within the standard softness index range [0.4, 0.6], so it is determined not to be Class I garbage. The standard softness index interval of the second type of garbage is set to [0.7, 0.9], the standard softness index interval of the third type of garbage is set to [1.0, 1.5], and the standard softness index interval of the fourth type of garbage is set to [1.6, 2.0]. The third data analysis unit analyzes the actual garbage weight of a type of garbage and compares the actual garbage weight with a standard garbage weight range, wherein the standard garbage weight range is determined based on historical data of the actual proportions of various types of garbage. If the actual garbage weight is less than the minimum standard garbage weight, the standard softness index range of a type of garbage will be expanded according to the difference between the minimum standard garbage weight and the actual garbage weight; If the actual garbage weight is within the standard garbage weight range, the garbage is determined to be Class A garbage and will be disposed of; If the actual garbage weight is greater than the maximum standard garbage weight, the standard softness index range of a type of garbage will be narrowed according to the difference between the actual garbage weight and the maximum standard garbage weight; In this embodiment, the standard garbage weight interval of a type of garbage is set to [5kg, 10kg], and the standard softness index interval adjustment value corresponding to the garbage weight difference is set to 0.1. For example, if the actual weight of Class I garbage is 3 kg and the actual softness is 0.7, then the corresponding adjustment amount of the standard softness index interval of Class I garbage is (5-3)×0.1=0.2. The adjusted standard softness index interval of Class I garbage is [0.4+0.2, 0.6+0.2], that is, [0.6, 0.8]. 0.7 falls into the adjusted standard softness index interval and is determined to be Class I garbage. The actual weight of the Class I garbage was 7kg, and the actual softness was 0.5. Since the actual weight was within the standard garbage weight range, the garbage was determined to be Class I garbage and was directly disposed of. The actual weight of the first-class garbage is detected to be 13kg, and the actual softness is 0.3. The corresponding adjustment amount of the standard softness index range of the first-class garbage is (13-10)×0.1=0.3. The adjusted standard softness index range of the first-class garbage is [0.4-0.3, 0.6-0.3], that is, [0.1, 0.3]. It is determined that the box lid should not be opened and repackaging is required.

[0043] A three-dimensional classification system based on "prominent points (visual) - softness (tactile) - weight (density)" was established, breaking through the bottleneck of a single dimension and achieving three-dimensional recognition of material, shape, and density. Weight-driven adjustment of softness ranges enables the system to intelligently accommodate "lightweight / high-density" variations in waste. Data accumulation throughout the entire process, from initial assessment to correction to secondary assessment, supports continuous iteration of the AI ​​model, and classification capabilities increase exponentially with time. Through precise inter-module collaboration and data-driven design, this system not only significantly improves waste classification accuracy but also establishes a self-evolving intelligent classification system, providing a practical technical paradigm for intelligent urban sanitation.

[0044] Specifically, the garbage detection module collects secondary garbage data on the garbage in the trash bin, generates garbage data in the bin, and determines the type of garbage in the bin based on the garbage data in the bin.

[0045] The garbage detection module in the barrel performs a secondary inspection based on the visual judgment results. Based on the secondary judgment result, the garbage in the bin is detected. If the secondary judgment result is Class I garbage, the garbage detection module in the bin determines the classification accuracy based on the physical matching degree and image matching degree. Among them, the physical matching degree is determined based on the actual weight and softness of the garbage. For example, the actual weight of the garbage is 12 kg, which is outside the standard garbage weight range [5, 10 kg] corresponding to Class I garbage (-20%), and the softness is 0.5, which is within the range [0.4, 0.6] (+100%). The comprehensive physical matching degree is 80%; the image matching degree: Class I garbage accounts for 70%, non-Class I garbage accounts for 30%, and the image matching degree is 70%. It identifies Class I garbage based on the garbage storage unit in the garbage detection module in the bin.

[0046] Combining weight deviation and softness compliance rate to avoid misjudgment caused by single weight abnormality, the weight ratio of weight, softness and image is adjusted in real time according to the secondary detection results, giving the system the ability to self-evolve.

[0047] Specifically, the determination module compares the types of garbage in the bin with the secondary determination results to obtain the accuracy of garbage classification in the bin, and determines whether to adjust the weight of the garbage weight information to the overall weight of the garbage based on the comparison results of the garbage classification accuracy in the bin and the garbage classification accuracy in the standard garbage classification accuracy range, or to increase consumption points for the user.

[0048] Specifically, the determination module makes a determination based on the garbage classification accuracy comparison result. If the accuracy of garbage classification in the bin is less than the minimum value of the standard garbage classification accuracy interval, the weight will be increased according to the difference between the minimum value of the standard garbage classification accuracy interval and the accuracy of garbage classification in the bin; If the accuracy of garbage classification in the bin is within the standard garbage classification accuracy range, the original weight will remain unchanged; If the accuracy of garbage classification in the bin is greater than the maximum value of the standard garbage classification accuracy range, the user will be given additional consumption points.

[0049] In this embodiment, a standard garbage classification accuracy range of [85%, 90%] is set, wherein the standard garbage classification accuracy range is determined based on historical data and garbage disposal technology requirements. If the accuracy of garbage classification in the bin is less than the minimum value of the standard garbage classification accuracy interval, the increased weight is determined by multiplying the original weight by 1 plus the product of the difference between the minimum value of the standard garbage classification accuracy interval and the garbage classification accuracy in the bin divided by the minimum value of the standard garbage classification accuracy interval; If the accuracy of garbage classification in the bin is within the standard garbage classification accuracy range, the original weight will remain unchanged; If the accuracy of garbage classification in the bin is less than the minimum value of the standard garbage classification accuracy range, the reduced weight is determined by multiplying the original weight by 1 minus the product of the accuracy of garbage classification in the bin minus the maximum value of the standard garbage classification accuracy range divided by the maximum value of the standard garbage classification accuracy range.

[0050] For mixed garbage with abnormal weight, increasing the weight factor increases the system's sensitivity to identifying "weighed but abnormal composition" garbage, reducing the misjudgment rate. Points can be exchanged for garbage bags or community services, forming a positive cycle of "accurate placement - reward." Through a nonlinear mapping formula between accuracy and weight, accurate correction of classification errors is achieved, avoiding system shocks caused by "one-size-fits-all" adjustments. The points mechanism not only increases participation but also feeds back model optimization through user behavior data, forming a closed loop of "manual classification - system learning - more accurate identification." The standard accuracy range and weight adjustment strategy evolve dynamically based on historical data and processing needs, ensuring that the system maintains high robustness in different scenarios.

[0051] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0052] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An intelligent sanitation waste classification monitoring and feedback system, characterized in that: include A garbage bin, used for containing garbage, provided with a controllable electric lid; Including, a trash can group, which consists of several trash cans of different categories placed side by side, wherein each trash can is provided with an electric switch that can be individually controlled on and off; Garbage bag storage box, used for placing garbage bags when sorting garbage; An information collection device for collecting spam information and user input information; A softness collecting device, which is arranged at the lower part of the information collecting device and is used to collect the softness of the garbage bag; The garbage detection module in the bin determines the accuracy of garbage classification by detecting the proportion of corresponding garbage types in each classified garbage bin; a data analysis module, connected to the information collection device and the garbage detection module in the bin, respectively, to determine the type of garbage based on the garbage weight information and the garbage image information, and obtain a first determination result; Comparing the first determination result with the input information to obtain a garbage classification comparison result, and determining whether garbage separation is required based on the garbage classification comparison result, or adjusting the weight of the garbage weight information and the garbage image information relative to the first determination result; A garbage determination module is used to determine whether the analysis result of the data analysis module is correct based on the data of the garbage detection module in the bucket; a control module connected to the trash bin group and controlling the opening of the electric lid of the trash bin group according to the analysis data of the data analysis module; The junk information includes junk weight information and junk image information; and the input information is the type of junk to be delivered actively selected by the user.

2. The intelligent sanitation waste classification monitoring and feedback system according to claim 1 is characterized in that: The information collection device, include, A carrying platform for placing garbage to be inspected; a camera, disposed on top of the carrying platform, for collecting images of garbage placed on the carrying platform; The interactive device is set in the middle of the testing platform and is used to complete the interaction between the system and the user; The interactive device includes an input keyboard and a display screen. The input keyboard is used to collect information input by the user, and the display screen is used to display feedback information.

3. The intelligent sanitation waste classification monitoring and feedback system according to claim 1 is characterized in that: The information collection device identifies a junk image formed by the junk image information, extracts characteristic elements of the junk to obtain actual characteristic elements, the data analysis module compares the actual characteristic elements with standard characteristic elements to obtain a degree of consistency between the characteristic elements, and determines the type of junk corresponding to the junk image based on the degree of consistency between the characteristic elements; Among them, the characteristic elements include the number of prominent points and the softness of the garbage.

4. The intelligent sanitation waste classification monitoring and feedback system according to claim 3 is characterized in that: The data analysis module obtains the actual garbage classification matching degree based on the overall garbage type information and input information, compares the actual garbage classification matching degree with the standard garbage classification matching degree to obtain a garbage classification matching degree comparison result, and determines whether the garbage needs to be repackaged or whether to control the electric box cover to open according to the garbage classification matching degree comparison result.

5. The intelligent sanitation waste classification monitoring and feedback system according to claim 4 is characterized in that: The data analysis module includes: A first data analysis unit is used to make an initial determination of the garbage material based on the number of prominent points in the garbage image; The second data analysis unit is used to modify the initial determination result according to the softness of the garbage; The third data analysis unit is used to determine whether to perform a secondary determination on the type of garbage based on a comparison result between the actual garbage weight and the garbage weight within the standard garbage weight range.

6. The intelligent sanitation waste classification monitoring and feedback system according to claim 5 is characterized in that: The garbage determination module determines whether to put the garbage as a whole, or whether to adjust the standard softness index range, based on the secondary determination result of the third data analysis unit.

7. The intelligent sanitation waste classification monitoring and feedback system according to claim 6 is characterized in that: The control module controls the electric box cover. If the actual garbage weight is less than the minimum standard garbage weight, the corresponding standard softness index range will be expanded according to the difference between the minimum standard garbage weight and the actual garbage weight; If the actual garbage weight is within the standard garbage weight range, the garbage is determined to be Class A garbage, and the control module controls the electric box cover to open the electric box cover to put the garbage in; If the actual garbage weight is greater than the maximum standard garbage weight, the corresponding standard softness index range will be narrowed according to the difference between the actual garbage weight and the maximum standard garbage weight.

8. The intelligent sanitation waste classification monitoring and feedback system according to claim 7 is characterized in that: The garbage detection module collects secondary garbage data on the garbage in the garbage bin, generates garbage data in the bin, and determines the type of garbage in the bin based on the garbage data in the bin.

9. The intelligent sanitation waste classification monitoring and feedback system according to claim 8 is characterized in that: The determination module compares the type of garbage in the bin with the secondary determination result to obtain the accuracy of garbage classification in the bin, and determines whether to adjust the weight of the garbage weight information to the overall weight of the garbage based on the comparison result of the garbage classification accuracy in the bin and the garbage classification accuracy in the standard garbage classification accuracy range, or to increase consumption points for the user.

10. The intelligent sanitation waste classification monitoring and feedback system according to claim 9 is characterized in that: The determination module makes a determination based on the garbage classification accuracy comparison result. If the accuracy of garbage classification in the bin is less than the minimum value of the standard garbage classification accuracy interval, the weight will be increased according to the difference between the minimum value of the standard garbage classification accuracy interval and the accuracy of garbage classification in the bin; If the accuracy of garbage classification in the bin is within the standard garbage classification accuracy range, the original weight will remain unchanged; If the accuracy of garbage classification in the bin is greater than the maximum value of the standard garbage classification accuracy range, the user will be given additional consumption points.

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