Garbage classification system

Abstract

The invention discloses a garbage classification system, and particularly relates to the field of garbage classification systems.The garbage classification system comprises a modeling unit used for establishing an environment model, the modeling unit is electrically connected with a data end used for big data engine data supply, and the modeling unit is electrically connected with a selection unit used for mode selection; the selection unit is electrically connected with a display unit used for displaying and controlling the operation sum. According to the method, time-limited scoring and infinite-time scoring are divided through a complex scoring and feedback mechanism, feedback of different levels is given according to correct rate analysis, the mechanism provides detailed feedback and exaggeration through hierarchical evaluation, interactivity and the learning effect are improved, and the learning efficiency is improved. The user is allowed to drag the junk pictures to the classification module through the dragging module, real-time scoring is performed through the classification and scoring module, the actual classification process is simulated, the classification result can be fed back in time, and the classification accuracy of the user can be improved.

Description

Technical Field

[0001] The present invention relates to the field of garbage classification systems, and in particular to a garbage classification system. Background Art

[0002] With the increasing awareness of environmental protection, garbage classification has become an important environmental management measure in many countries and regions around the world. Correct garbage classification not only helps to recycle and reuse resources, but also reduces the burden of garbage on the environment. However, the implementation and management of garbage classification face many challenges in actual operation, including the complexity of classification standards, the improvement of classification accuracy, and user participation. The existing technical garbage classification mostly relies on the teaching of teachers in school and the teaching of family elders. Usually, the traditional way of teaching garbage classification is to manually classify garbage. Although this method is simple and direct, it is inefficient and prone to errors, especially when the amount of garbage is large or the classification standards are complex. With the development of science and technology, the generation of intelligent pictures can make learners more intuitively recognize how to classify garbage, but it still faces problems such as recognition accuracy, model training and real-time feedback. Therefore, a garbage classification system is needed to solve the above problems. Summary of the invention

[0003] The purpose of the present invention is to solve the shortcomings existing in the prior art. With the enhancement of environmental protection awareness, garbage classification has become an important environmental management measure in many countries and regions around the world. Correct garbage classification not only helps to recycle and reuse resources, but also reduces the burden of garbage on the environment. However, the implementation and management of garbage classification face many challenges in actual operation, including the complexity of classification standards, the improvement of classification accuracy, the participation of users, etc., and the existing technical garbage classification mostly relies on the teaching of teachers in school, the teaching of family elders, etc., and the traditional way of garbage classification is usually used for garbage classification teaching. In the traditional garbage classification method, garbage is manually classified. Although this method is simple and direct, it is inefficient and prone to errors, especially in the case of large garbage volume or complex classification standards. With the development of science and technology, the generation of intelligent pictures can make learners more intuitively recognize how to classify garbage, but still faces problems such as recognition accuracy, model training and real-time feedback, and a garbage classification system is proposed.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A garbage classification system includes a modeling unit for establishing an environmental model, the modeling unit is electrically connected to a data terminal for supplying data to a big data engine, the modeling unit is electrically connected to a selection unit for mode selection, the selection unit is electrically connected to a display unit for displaying and controlling operations, the display unit is electrically connected to a scoring unit for learning scoring, the scoring unit includes a dragging module for dragging garbage to move, the dragging module is electrically connected to a classification module for garbage placement points, the classification module is electrically connected to a scoring module for calculating scores, the scoring module is electrically connected to a suggestion feedback module for suggestion feedback and an evaluation module for evaluating scores, the evaluation module is electrically connected to the suggestion feedback module, and the evaluation module is electrically connected to a praise feedback module for praising score winners.

[0005] The present invention is further configured as follows: the scoring unit acts on the display unit, the scoring module is based on time-limited scoring and unlimited time scoring, the classification module includes but is not limited to categories such as recyclables, non-recyclables, hazardous waste, etc., the dragging module drags the garbage picture to the classification module, and the recyclables, non-recyclables, hazardous waste, etc. categories set in the classification module receive and eliminate the picture, and the scoring module calculates the accuracy rate and analyzes whether the accuracy rate exceeds 80% at the end.

[0006] The present invention is further configured as follows: when the analysis accuracy of the scoring module exceeds 80%, the evaluation module evaluates the actual accuracy to evaluate whether the actual accuracy exceeds 98%; when the evaluation module evaluates the actual accuracy to exceed 98%, the praise feedback module displays praise statements, and the suggestion feedback module provides error classification details; when the analysis accuracy of the scoring module is lower than 80%, the suggestion feedback module provides error classification details; when the evaluation module evaluates the actual accuracy to be lower than 98%, the suggestion feedback module provides error classification details.

[0007] The present invention is further configured as follows: the modeling unit includes an environment construction module for constructing a model according to the environment, the environment construction module is electrically connected to a garbage construction module for constructing garbage shapes and pictures according to environmental information, and the garbage construction module is electrically connected to a storage module for storing models and garbage shapes and pictures.

[0008] The present invention is further configured as follows: the data end provides a large amount of data to supply an environment construction module and a garbage construction module, the environment construction module obtains environmental data information to construct a corresponding environment model, the environment model includes but is not limited to a kitchen, an office, a factory, etc., the garbage construction module constructs corresponding garbage shapes and pictures according to the environment model, and stores them together with the environment model in the storage module.

[0009] The present invention is further configured as follows: the selection module includes a random module for randomly selecting an environmental model and a self-selection module for autonomously selecting an environmental model; the random module and the self-selection module are electrically connected to a scene extraction module for extracting an environmental model; the scene extraction module is electrically connected to a garbage generation module for generating garbage according to the environmental model extracted by the scene extraction module; the scene extraction module is electrically connected to a storage module; and the self-selection module and the random module are electrically connected to the storage module.

[0010] The present invention is further configured as follows: the selection unit is attached to the display unit, the random module randomly selects from all the constructed environment models in the storage module, the self-selection module can self-select an environment model from all the constructed environment models in the storage module, and after the self-selection module and the random module determine the scene, the scene extraction module extracts the selected environment model, and the garbage generation module generates a garbage picture that conforms to the environment model at any position in the environment model.

[0011] The present invention is further configured as follows: the specific operations of the modeling unit, the selection unit, the display unit, the scoring unit and the data terminal include the following steps: S1, providing the environment name and environment data to the modeling unit to establish a model, and obtaining the shape and image of garbage in the environment through the data terminal according to the environment model; S2. Use the display unit as the control and display function, select the required scene through the selection unit, and use the scoring unit for learning and evaluation.

[0012] The present invention is further configured as follows: in step S1: 1) Provide the environment name and related environment data to the modeling unit, and the environment building module builds the related environment model; 2) Based on the environmental model, the garbage output related to the environmental model provided by the data end is used to generate garbage images that meet the environmental characteristics; 3) Store the environment model and the generated garbage images in the storage module for subsequent selection and evaluation.

[0013] The present invention is further configured as follows: in step S2: 1) The random module or self-selection module of the selection unit selects the required environment scene, and the scene extraction module extracts the relevant environment model from the storage module. According to the environment model, the garbage generation module generates the corresponding garbage picture at any position in the specified environment model; 2) Drag the junk images in the drag module and place them in the classification module for elimination. When the junk images in the environment model are eliminated by more than 50% of the set value, some junk images will be regenerated to supplement the junk images in the environment model; 3) At the end, when the scoring module analyzes that the accuracy rate exceeds 80%, the evaluation module will evaluate whether the actual accuracy rate exceeds 98%. If the actual accuracy rate exceeds 98%, the praise feedback module will display praise statements, and the suggestion feedback module will provide error classification details. When the actual accuracy rate is lower than 98%, the suggestion feedback module will provide error classification details. When the scoring module analyzes that the accuracy rate is lower than 80%, the suggestion feedback module will provide error classification details.

[0014] The present invention 1. Through a complex scoring and feedback mechanism, it is divided into time-limited scoring and unlimited scoring, and gives different levels of feedback based on the accuracy analysis. This mechanism provides detailed feedback and praise through hierarchical evaluation, which increases interactivity and learning effect. The drag module allows users to drag junk images to the classification module, and the classification and scoring modules are used for real-time scoring. It not only simulates the actual classification process, but also provides instant feedback on the classification results, which helps to improve the classification accuracy of users. The detailed feedback and suggestion mechanism not only provides scoring, but also includes detailed feedback on incorrect classification information, which can help users understand their mistakes and make improvements. The detailed feedback mechanism enhances the learning effect; 2. Generate garbage pictures according to the environmental model through the environmental modeling unit and the garbage construction module. The dynamic generation method can better simulate the actual garbage classification scene, making the training closer to the real environment. The data end provided by the big data engine can establish the environmental model and garbage shape based on a large amount of data. This data-driven method makes the system's environmental model more accurate and effective. The selection module supports random selection and autonomous selection of environmental models, allowing users to experience different garbage classification scenes and conduct targeted training or learning. The flexible selection mechanism increases the adaptability and diversity of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a system flow diagram of a garbage classification system in the present invention; Figure 2 A schematic diagram of the system flow of a modeling unit and a selection unit of a garbage classification system in the present invention; Figure 3 It is a system flow diagram of a scoring unit of a garbage classification system in the present invention. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0017] like Figure 1-3 As shown, a garbage classification system includes a modeling unit for establishing an environmental model, the modeling unit is electrically connected to a data terminal for supplying data to a big data engine, the modeling unit is electrically connected to a selection unit for mode selection, the selection unit is electrically connected to a display unit for displaying and controlling operations, the display unit is electrically connected to a scoring unit for learning scoring, the scoring unit includes a dragging module for dragging garbage to move, the dragging module is electrically connected to a classification module for garbage placement points, the classification module is electrically connected to a scoring module for calculating scores, the scoring module is electrically connected to a suggestion feedback module for suggestion feedback and an evaluation module for evaluating scores, the evaluation module is electrically connected to the suggestion feedback module, and the evaluation module is electrically connected to a praise feedback module for praising score winners; The scoring unit acts on the display unit. The scoring module is based on time-limited scoring and unlimited scoring. The classification module includes but is not limited to categories such as recyclables, non-recyclables, and hazardous waste. The drag module drags the garbage picture to the classification module, and the recyclables, non-recyclables, and hazardous waste categories set in the classification module receive and eliminate the picture. At the same time, the scoring module calculates the accuracy rate and analyzes whether the accuracy rate exceeds 80% at the end. When the scoring module analyzes that the accuracy rate exceeds 80%, the evaluation module evaluates the actual accuracy rate to evaluate whether the actual accuracy rate exceeds 98%. When the evaluation module evaluates that the actual accuracy rate exceeds 98%, the praise feedback module displays the praise sentence, and the suggestion feedback module provides the error classification details. When the scoring module analyzes that the accuracy rate is lower than 80%, the suggestion feedback module provides the error classification details. When the evaluation module evaluates that the actual accuracy rate is lower than 98%, the suggestion feedback module provides the error classification details. Through the above embodiment, a complex scoring and feedback mechanism is used, which is divided into time-limited scoring and unlimited scoring, and different levels of feedback are given according to the accuracy analysis. This mechanism provides detailed feedback and praise through hierarchical evaluation, increases interactivity and learning effect, allows users to drag junk pictures to the classification module through the drag module, and performs real-time scoring through the classification and scoring module, which not only simulates the actual classification process, but also provides instant feedback on the classification results, which helps to improve the classification accuracy of users. The detailed feedback and suggestion mechanism not only provides scoring, but also includes detailed feedback on error classification information, which can help users understand their mistakes and make improvements, and enhances the learning effect through the detailed feedback mechanism; The modeling unit includes an environment construction module for constructing a model according to the environment, the environment construction module is electrically connected to a garbage construction module for constructing garbage shapes and pictures according to environmental information, and the garbage construction module is electrically connected to a storage module for storing the model and the garbage shapes and pictures; The data end provides a large amount of data to the environment construction module and the garbage construction module. The environment construction module obtains environmental data information to build a corresponding environment model. The environment model includes but is not limited to kitchens, offices, factories, etc. The garbage construction module builds the corresponding garbage shape and picture according to the environment model, and stores it in the storage module together with the environment model; Through the above embodiments, garbage images are generated according to the environmental model through the environmental modeling unit and the garbage construction module, and the actual garbage classification scene can be better simulated in a dynamic generation manner, so that the training is closer to the real environment. Through the data end provided by the big data engine, the environmental model and garbage shape can be established according to a large amount of data. This data-driven method makes the system's environmental model more accurate and effective; The selection module includes a random module for randomly selecting an environmental model and a self-selection module for autonomously selecting an environmental model, the random module and the self-selection module are electrically connected to a scene extraction module for extracting an environmental model, the scene extraction module is electrically connected to a garbage generation module for generating garbage according to the environmental model extracted by the scene extraction module, the scene extraction module is electrically connected to a storage module, and the self-selection module and the random module are electrically connected to the storage module; The selection unit is attached to the display unit, the random module randomly selects from all the constructed environment models in the storage module, the self-selection module can select the environment model from all the constructed environment models in the storage module, after the self-selection module and the random module determine the scene, the scene extraction module extracts the selected environment model, and the garbage generation module generates a garbage picture that conforms to the environment model at any position in the environment model; Through the above embodiments, the selection module supports random selection and autonomous selection of environmental models, so that users can experience different garbage classification scenarios and conduct targeted training or learning, and the adaptability and diversity of the system are increased through a flexible selection mechanism; The specific operations of the modeling unit, selection unit, display unit, scoring unit and data end include the following steps: S1, providing the environment name and environment data to the modeling unit to establish a model, and obtaining the shape and image of garbage in the environment through the data terminal according to the environment model; S2. Use the display unit as the control and display function, select the required scene through the selection unit, and use the scoring unit for learning and evaluation.

[0018] In step S1: 1) Provide the environment name and related environment data to the modeling unit, and the environment building module builds the related environment model; 2) Based on the environmental model, the garbage output related to the environmental model provided by the data end is used to generate garbage images that meet the environmental characteristics; 3) Store the environment model and the generated garbage images in the storage module for subsequent selection and evaluation.

[0019] In step S2: 1) The random module or self-selection module of the selection unit selects the required environment scene, and the scene extraction module extracts the relevant environment model from the storage module. According to the environment model, the garbage generation module generates the corresponding garbage picture at any position in the specified environment model; 2) Drag the junk images in the drag module and place them in the classification module for elimination. When the junk images in the environment model are eliminated by more than 50% of the set value, some junk images will be regenerated to supplement the junk images in the environment model; 3) At the end, when the scoring module analyzes that the accuracy rate exceeds 80%, the evaluation module will evaluate whether the actual accuracy rate exceeds 98%. If the actual accuracy rate exceeds 98%, the praise feedback module will display praise statements, and the suggestion feedback module will provide detailed information on error classification. When the actual accuracy rate is lower than 98%, the suggestion feedback module will provide detailed information on error classification. When the scoring module analyzes that the accuracy rate is lower than 80%, the suggestion feedback module will provide detailed information on error classification.

[0020] Working principle: When the present invention is in use, the environment name and related environment data are provided to the modeling unit, and the environment construction module constructs the related environment model. Based on the environment model, the garbage output related to the environment model provided by the data end is used to generate garbage pictures that meet the environmental characteristics. The environment model and the generated garbage pictures are stored in the storage module for subsequent selection and evaluation. The required environment scene is selected by the random module or the self-selected module of the selection unit. The scene extraction module extracts the related environment model from the storage module. According to the environment model, the garbage generation module generates the corresponding garbage picture at any position in the specified environment model. The drag module drags the garbage The pictures are placed in the classification module for elimination. When the elimination of junk pictures in the environment model exceeds 50% of the set value, some junk pictures will be regenerated to supplement the junk pictures in the environment model. At the end, when the scoring module analyzes that the accuracy rate exceeds 80%, the evaluation module will evaluate whether the actual accuracy rate exceeds 98%. When the actual accuracy rate is evaluated to be more than 98%, the praise feedback module will display praise statements, and the suggestion feedback module will provide detailed information on the wrong classification. When the actual accuracy rate is evaluated to be lower than 98%, the suggestion feedback module will provide detailed information on the wrong classification. When the scoring module analyzes that the accuracy rate is lower than 80%, the suggestion feedback module will provide detailed information on the wrong classification.

[0021] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A garbage classification system, characterized in that: The system comprises a modeling unit for establishing an environmental model, the modeling unit being electrically connected to a data terminal for supplying data to a big data engine, the modeling unit being electrically connected to a selection unit for mode selection, the selection unit being electrically connected to a display unit for displaying and controlling operations, the display unit being electrically connected to a scoring unit for learning scoring, the scoring unit comprising a dragging module for dragging garbage to move, the dragging module being electrically connected to a classification module for garbage placement points, the classification module being electrically connected to a scoring module for calculating scores, the scoring module being electrically connected to a suggestion feedback module for suggestion feedback and an evaluation module for evaluating scores, the evaluation module being electrically connected to the suggestion feedback module, and the evaluation module being electrically connected to a praise feedback module for praising score winners.

2. A garbage classification system according to claim 1, characterized in that: The scoring unit acts on the display unit, the scoring module is based on time-limited scoring and unlimited time scoring, the classification module includes but is not limited to categories such as recyclables, non-recyclables, hazardous waste, etc., the drag module drags the garbage picture to the classification module, and the recyclables, non-recyclables, hazardous waste, etc. categories set in the classification module receive and eliminate the picture, and the scoring module calculates the accuracy rate and analyzes whether the accuracy rate exceeds 80% at the end.

3. A garbage classification system according to claim 2, characterized in that: When the scoring module analyzes that the accuracy rate exceeds 80%, the evaluation module will evaluate the actual accuracy rate to evaluate whether the actual accuracy rate exceeds 98%; when the evaluation module evaluates that the actual accuracy rate exceeds 98%, the praise feedback module will display the praise and compliments, and the suggestion feedback module will provide error classification details; when the scoring module analyzes that the accuracy rate is lower than 80%, the suggestion feedback module will provide error classification details; when the evaluation module evaluates that the actual accuracy rate is lower than 98%, the suggestion feedback module will provide error classification details.

4. A garbage classification system according to claim 1, characterized in that: The modeling unit includes an environment construction module for constructing a model according to the environment, the environment construction module is electrically connected to a garbage construction module for constructing garbage shapes and pictures according to environmental information, and the garbage construction module is electrically connected to a storage module for storing models and garbage shapes and pictures.

5. A garbage classification system according to claim 4, characterized in that: The data end provides a large amount of data to the environment construction module and the garbage construction module. The environment construction module obtains environmental data information to construct a corresponding environment model. The environment model includes but is not limited to kitchens, offices, factories, etc. The garbage construction module constructs corresponding garbage shapes and pictures according to the environment model, and stores them in the storage module together with the environment model.

6. A garbage classification system according to claim 1, characterized in that: The selection module includes a random module for randomly selecting an environmental model and a self-selection module for autonomously selecting an environmental model. The random module and the self-selection module are electrically connected to a scene extraction module for extracting an environmental model. The scene extraction module is electrically connected to a garbage generation module for generating garbage according to the environmental model extracted by the scene extraction module. The scene extraction module is electrically connected to a storage module, and the self-selection module and the random module are electrically connected to the storage module.

7. A garbage classification system according to claim 6, characterized in that: The selection unit is attached to the display unit, the random module will randomly select from all the constructed environment models in the storage module, the self-selection module can self-select an environment model from all the constructed environment models in the storage module, after the self-selection module and the random module determine the scene, the scene extraction module will extract the selected environment model, and the garbage generation module will generate a garbage picture that conforms to the environment model at any position in the environment model.

8. A garbage classification system according to claim 1, characterized in that: The specific operations of the modeling unit, the selection unit, the display unit, the scoring unit and the data terminal include the following steps: S1, providing the environment name and environment data to the modeling unit to establish a model, and obtaining the shape and image of garbage in the environment through the data terminal according to the environment model; S2. Use the display unit as the control and display function, select the required scene through the selection unit, and use the scoring unit for learning and evaluation.

9. A garbage classification system according to claim 8, characterized in that: In step S1: 1) Provide the environment name and related environment data to the modeling unit, and the environment building module builds the related environment model; 2) Based on the environmental model, the garbage output related to the environmental model provided by the data end is used to generate garbage images that meet the environmental characteristics; 3) Store the environment model and the generated garbage images in the storage module for subsequent selection and evaluation.

10. A garbage classification system according to claim 8, characterized in that: In step S2: 1) The random module or self-selection module of the selection unit selects the required environment scene, and the scene extraction module extracts the relevant environment model from the storage module. According to the environment model, the garbage generation module generates the corresponding garbage picture at any position in the specified environment model; 2) Drag the junk images in the drag module and place them in the classification module for elimination. When the junk images in the environment model are eliminated by more than 50% of the set value, some junk images will be regenerated to supplement the junk images in the environment model; 3) At the end, when the scoring module analyzes that the accuracy rate exceeds 80%, the evaluation module will evaluate whether the actual accuracy rate exceeds 98%. If the actual accuracy rate exceeds 98%, the praise feedback module will display praise statements, and the suggestion feedback module will provide error classification details. When the actual accuracy rate is lower than 98%, the suggestion feedback module will provide error classification details. When the scoring module analyzes that the accuracy rate is lower than 80%, the suggestion feedback module will provide error classification details.

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