Garbage classification intelligent monitoring system

Abstract

The invention discloses a garbage classification intelligent monitoring system, and particularly relates to the field of garbage monitoring systems, the garbage classification intelligent monitoring system comprises a monitoring unit and an analysis unit, the monitoring unit comprises a video recording module used for recording garbage images, and the video recording module is electrically connected with a processing module used for processing and analyzing whether the recorded images have labels. Intelligent monitoring of garbage classification is achieved through the video recording module, the processing module, the detection module, the identification module, the tracing module, the data arrangement module and the analysis unit, the comprehensive design is combined with various technologies such as image recognition, sensor data analysis and big data tracing, the comprehensiveness and accuracy of the system are enhanced, and through the label processing and tracing functions, the system can be widely applied to the field of garbage classification. According to the method, garbage without labels is processed, tracing is carried out through the labels, meanwhile, label identification and big data tracing are integrated, and the garbage can be classified and managed more accurately.

Description

Technical Field

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

[0002] As the world's attention to environmental protection and sustainable development continues to deepen, garbage classification has become an important means to solve the problem of solid waste. Garbage classification can not only reduce the pollution of garbage to the environment, but also improve the efficiency of resource recycling. The existing technologies either use manual classification or static classification. The first type is inefficient and prone to misclassification. The second type of equipment can usually only sort obvious types of garbage, and has limited processing capabilities for complex mixed garbage. It is also difficult to adjust the classification strategy in real time. With the development of intelligence and the application of high-definition cameras, a garbage classification intelligent monitoring 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 deepening of global attention to environmental protection and sustainable development, garbage classification has become an important means to solve the problem of solid waste. Garbage classification can not only reduce the pollution of garbage to the environment, but also improve the efficiency of resource recycling. The existing technology either adopts manual classification or static classification. The first one is inefficient and prone to misclassification. The second one can usually only sort obvious types of garbage, and has limited processing capacity for complex mixed garbage, and it is difficult to adjust the classification strategy in real time. With the development of intelligence and the application of high-definition cameras, an intelligent monitoring system for garbage classification is proposed.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: The intelligent monitoring system for garbage classification includes a monitoring unit and an analysis unit. The monitoring unit includes a recording module for recording garbage images. The recording module is electrically connected to a processing module for processing and analyzing whether the recorded images have labels. The processing module is electrically connected to a detection module for detecting garbage materials and an identification module for identifying whether garbage labels can be traced back to their sources. The identification module is electrically connected to a tracing module for tracing the original characteristics of garbage labels. The tracing module is electrically connected to a product cloud for big data search product traceability. The tracing module is electrically connected to a data sorting module for sorting tracing results. The identification module is electrically connected to a detection module. The detection module is electrically connected to a material cloud for storing material data and an evaluation module for evaluating garbage characteristic attributes. The evaluation module is electrically connected to a data sorting module, and the data sorting module is electrically connected to the analysis unit.

[0005] The present invention is further configured as follows: the recording module uses a high-definition camera as the main image recording means, the high-definition camera records the garbage, and then the processing module processes and analyzes whether the garbage has a label based on the garbage image recorded by the recording module.

[0006] The present invention is further configured as follows: when the processing module processes and analyzes that the garbage image does not have a label, the detection module detects the garbage, the detection module extracts useful features from multiple data such as images, sounds, gas sensors, etc., and matches them according to the data provided by the material cloud. At the same time, the evaluation module evaluates the garbage category according to the garbage material results matched by the detection module. When the processing module processes and analyzes that the garbage image has a label, the identification module identifies whether the garbage image label can be traced back to the source.

[0007] The present invention is further configured as follows: the identification module performs label identification analysis on the garbage with labels, and whether the labels can be traced back depends on the formula table, name, etc. When the identification module identifies that the garbage label cannot be traced back, the detection module detects the garbage; when the identification module identifies that the garbage label can be traced back, the tracing module performs tracing processing; the tracing module analyzes and evaluates the garbage category based on the data support provided by the product cloud; the evaluation results processed by the evaluation module and the tracing module are sorted by the data sorting module and then handed over to the analysis unit for analysis and processing.

[0008] The present invention is further configured as follows: the analysis unit includes an analysis module for analyzing whether the garbage is correctly put into the garbage, the analysis module is electrically connected to a recording module for recording correct and incorrect garbage putting into the garbage, the recording module is electrically connected to a feature extraction module for extracting the characteristics of garbage with an error rate of putting into the garbage exceeding 5%, the feature extraction module is electrically connected to a model building module for building a model according to the garbage extracted by the feature extraction module, the model building module is electrically connected to a feature marking module for marking the category characteristics of the garbage model, and the feature marking module is electrically connected to a display reminder module for displaying the garbage model and the garbage characteristics.

[0009] The present invention is further configured as follows: the analysis module analyzes whether the garbage input is correct according to the garbage attributes provided by the monitoring unit, and records the garbage input correctly and the garbage input incorrectly through the recording module; the feature extraction module performs data analysis based on the data information recorded by the recording module, and performs feature extraction based on the total amount of various garbage input and the garbage input error rate exceeding 5% during a period of 5-7 days.

[0010] The present invention is further configured as follows: the model building module establishes a garbage model based on the garbage products and garbage images provided by the feature extraction module, organizes, analyzes and evaluates, and the feature labeling module labels the feature information of the garbage model, the feature information includes but is not limited to garbage categories, garbage traceability products, and garbage assessment category theory; the display reminder module displays the garbage model and feature information for a period of 5-7 days.

[0011] The present invention is further configured as follows: the specific operations of the monitoring unit and the analysis unit include the following steps: S1. Use monitoring units to monitor the specific categories of garbage disposal; S2. Analyze whether the garbage is placed correctly by the analysis unit and give reminders for garbage placement with a high error rate.

[0012] The present invention is further configured as follows: in step S1: 1) Use a high-definition camera to capture garbage images, and the processing module analyzes the captured garbage images to determine whether the garbage has a label; 2) For garbage with labels, the identification module checks whether the labels can be traced. If the labels can be traced, the traceability module traces the garbage based on the product cloud data and evaluates the garbage attributes. The data sorting module sorts the traceability results and passes them to the analysis unit. If the labels cannot be traced, the detection module detects the garbage, including image, sound, gas sensor and other data, matches the material cloud data, evaluates the garbage attributes, and the data sorting module sorts the detection and evaluation results and passes them to the analysis unit. 3) Processing of unlabeled garbage. The detection module detects the garbage, including image, sound, gas sensor and other data, and matches the material cloud data. The evaluation module evaluates the garbage properties based on the detection results. The data sorting module sorts the evaluation results and passes them to the analysis unit.

[0013] The present invention is further configured as follows: in step S2: 1) The analysis unit uses the analysis module to determine whether the garbage is correctly classified. The recording module records the correctly and incorrectly classified garbage. The feature extraction module extracts the features of garbage with an input error rate of more than 5%. The model building module builds a garbage classification model based on the extracted features. The feature marking module marks the garbage category and other feature information for the model. The display reminder module displays the garbage model and feature information. 2) Among them, feature extraction is performed based on the misclassified data in the recording module; 3) The evaluation period is 5-7 days, during which the model is updated and the classification system is optimized based on the garbage input data during this period.

[0014] The beneficial effects of the present invention are: The present invention: 1. Intelligent monitoring of garbage classification is achieved through recording module, processing module, detection module, identification module, tracing module, data sorting module and analysis unit. The comprehensive design combines multiple technologies such as image recognition, sensor data analysis and big data tracing to enhance the comprehensiveness and accuracy of the system. Through label processing and tracing functions, it not only processes unlabeled garbage, but also traces it through labels. At the same time, it integrates label recognition and big data tracing, which can classify and manage garbage more accurately. Through multiple data sources such as images, sounds and gas sensors to detect garbage materials and match them with material cloud data, the multi-data fusion method helps to improve the accuracy of garbage classification; 2. The analysis unit extracts features based on the misclassified data, builds a classification model, and marks garbage features through the model. The data sorting module sorts the traceability and detection results and passes them to the analysis unit to provide systematic data support. Structured data processing and analysis methods help to generate accurate garbage classification results and reports. The garbage model and its characteristic information are displayed through the display reminder module. The system can provide intuitive feedback and teaching functions, which helps users better understand and improve garbage classification behavior, thereby realizing the feedback of misclassified data and model optimization mechanism, and improving the intelligence and adaptability of the classification system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a system flow diagram of the garbage classification intelligent monitoring system in the present invention; Figure 2 It is a schematic diagram of the system flow of the monitoring unit of the intelligent monitoring system for garbage classification in the present invention; Figure 3 It is a schematic diagram of the system flow of the analysis unit of the intelligent monitoring system for garbage classification 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 1-3As shown, the intelligent monitoring system for garbage classification includes a monitoring unit and an analysis unit. The monitoring unit includes a recording module for recording garbage images. The recording module is electrically connected to a processing module for processing and analyzing whether the recorded images have labels. The processing module is electrically connected to a detection module for detecting garbage materials and an identification module for identifying whether the garbage labels can be traced back to the source. The identification module is electrically connected to a tracing module for tracing the original characteristics of the garbage labels. The tracing module is electrically connected to a product cloud for big data search product tracing. The tracing module is electrically connected to a data sorting module for sorting tracing results. The identification module is electrically connected to the detection module. The detection module is electrically connected to a material cloud for material data storage and an evaluation module for evaluating garbage characteristic attributes. The evaluation module is electrically connected to the data sorting module, and the data sorting module is electrically connected to the analysis unit. The recording module uses a high-definition camera as the main means of image recording. The high-definition camera records the garbage, and then the processing module processes and analyzes whether the garbage has a label based on the garbage image recorded by the recording module; When the processing module processes and analyzes that the garbage image does not have a label, the detection module will detect the garbage. The detection module extracts useful features from multiple data such as images, sounds, and gas sensors, and matches them according to the data provided by the material cloud. At the same time, the evaluation module evaluates the garbage category based on the garbage material results matched by the detection module. When the processing module processes and analyzes that the garbage image has a label, the identification module will identify whether the garbage image label can be traced back to the source; The identification module performs label identification analysis on the labeled garbage. Whether the label can be traced back depends on the formula table, name, etc. When the identification module identifies that the garbage label cannot be traced back, the detection module will detect the garbage. When the identification module identifies that the garbage label can be traced back, the traceability module will perform traceability processing. The traceability module analyzes and evaluates the garbage category based on the data support provided by the product cloud. The evaluation results processed by the evaluation module and the traceability module are sorted by the data sorting module and then handed over to the analysis unit for analysis and processing; Through the above embodiments, intelligent monitoring of garbage classification is achieved through the recording module, processing module, detection module, identification module, tracing module, data sorting module, and analysis unit. The comprehensive design combines multiple technologies such as image recognition, sensor data analysis, and big data tracing, which enhances the comprehensiveness and accuracy of the system. Through the label processing and tracing function, not only unlabeled garbage is processed, but also traced through labels. At the same time, label recognition and big data tracing are integrated, which can classify and manage garbage more accurately. The garbage material is detected through multiple data sources such as images, sounds, and gas sensors, and matched with the material cloud data. The multi-data fusion method helps to improve the accuracy of garbage classification. The analysis unit includes an analysis module for analyzing whether the garbage is correctly put in, the analysis module is electrically connected to a recording module for recording correct and incorrect garbage putting in, the recording module is electrically connected to a feature extraction module for extracting features of garbage with an error rate of putting in exceeding 5%, the feature extraction module is electrically connected to a model building module for building a model according to the garbage extracted by the feature extraction module, the model building module is electrically connected to a feature marking module for marking the category features of the garbage model, and the feature marking module is electrically connected to a display reminder module for displaying the garbage model and garbage features; The analysis module analyzes whether the garbage input is correct according to the garbage attributes provided by the monitoring unit, and records the correct garbage input and the wrong garbage input through the recording module. The feature extraction module performs data analysis based on the data information recorded by the recording module, and extracts features based on the total amount of various garbage input and the garbage input error rate exceeding 5% during the 5-day period; The model building module organizes, analyzes and evaluates the garbage products and garbage images provided by the feature extraction module to establish a garbage model, and the feature marking module marks the feature information of the garbage model, which includes but is not limited to garbage categories, garbage tracing products, and garbage assessment category theory. The display reminder module displays the garbage model and feature information for a period of 5 days; Through the above embodiments, the analysis unit extracts features based on the misclassified data, establishes a classification model, and marks garbage features through the model. The data sorting module sorts the traceability and detection results and passes them to the analysis unit, providing systematic data support. The structured data processing and analysis methods are helpful to generate accurate garbage classification results and reports. The garbage model and its feature information are displayed by the display reminder module. The system can provide intuitive feedback and teaching functions, which helps users better understand and improve garbage classification behavior, thereby realizing the feedback of misclassified data and the model optimization mechanism, and improving the intelligence and adaptability of the classification system. The specific operations of the monitoring unit and the analysis unit include the following steps: S1. Use monitoring units to monitor the specific categories of garbage disposal; S2. Analyze whether the garbage is disposed correctly by using the analysis unit and provide reminders for garbage disposal with a high error rate.

[0018] In step S1: 1) Use a high-definition camera to capture garbage images, and the processing module analyzes the captured garbage images to determine whether the garbage has a label; 2) For garbage with labels, the identification module checks whether the labels can be traced. If the labels can be traced, the traceability module traces the garbage based on the product cloud data and evaluates the garbage attributes. The data sorting module sorts the traceability results and passes them to the analysis unit. If the labels cannot be traced, the detection module detects the garbage, including image, sound, gas sensor and other data, matches the material cloud data, evaluates the garbage attributes, and the data sorting module sorts the detection and evaluation results and passes them to the analysis unit. 3) Processing of unlabeled garbage. The detection module detects the garbage, including image, sound, gas sensor and other data, and matches the material cloud data. The evaluation module evaluates the garbage properties based on the detection results. The data sorting module sorts the evaluation results and passes them to the analysis unit.

[0019] In step S2: 1) The analysis unit uses the analysis module to determine whether the garbage is correctly classified. The recording module records the correctly and incorrectly classified garbage. The feature extraction module extracts the features of garbage with an input error rate of more than 5%. The model building module builds a garbage classification model based on the extracted features. The feature marking module marks the garbage category and other feature information for the model. The display reminder module displays the garbage model and feature information. 2) Among them, feature extraction is performed based on the misclassified data in the recording module; 3) The evaluation period is 5 days, and the model is updated based on the garbage input data during this period to optimize the classification system.

[0020] Working principle: When the present invention is in use, a high-definition camera is used to capture garbage images. The processing module analyzes the captured garbage images to determine whether the garbage is labeled. For labeled garbage, the identification module checks whether the label can be traced. If the label can be traced, the traceability module traces the garbage according to the product cloud data and evaluates the garbage attributes. The data sorting module organizes the traceability results and passes them to the analysis unit. If the label cannot be traced, the detection module detects the garbage, including image, sound, gas sensor and other data, matches the material cloud data, evaluates the garbage attributes, and the data sorting module organizes the detection and evaluation results and passes them to the analysis unit. For the processing of unlabeled garbage, the detection module detects the garbage, including image, sound, gas sensor and other data. , gas sensor and other data, match the material cloud data, the evaluation module evaluates the garbage attributes according to the detection results, the data sorting module sorts the evaluation results and passes them to the analysis unit, the analysis unit uses the analysis module to determine whether the garbage is correctly classified, the recording module records the correct and incorrectly classified garbage, the feature extraction module extracts the garbage features with an input error rate of more than 5%, the model building module establishes a garbage classification model according to the extracted features, the feature marking module marks the garbage category and other feature information for the model, and the display reminder module displays the garbage model and feature information, wherein feature extraction is performed based on the incorrectly classified data in the recording module, wherein the evaluation period is 5 days, and the model is updated according to the garbage input data during the period, and the classification system is optimized.

[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. Intelligent monitoring system for garbage classification, characterized by: The system comprises a monitoring unit and an analysis unit. The monitoring unit comprises a recording module for recording garbage images. The recording module is electrically connected to a processing module for processing and analyzing whether the recorded images have labels. The processing module is electrically connected to a detection module for detecting garbage materials and an identification module for identifying whether garbage labels can be traced back to their sources. The identification module is electrically connected to a tracing module for tracing the original characteristics of garbage labels. The tracing module is electrically connected to a product cloud for big data search product traceability. The tracing module is electrically connected to a data sorting module for sorting tracing results. The identification module is electrically connected to a detection module. The detection module is electrically connected to a material cloud for storing material data and an evaluation module for evaluating garbage characteristic attributes. The evaluation module is electrically connected to a data sorting module, and the data sorting module is electrically connected to the analysis unit.

2. The intelligent monitoring system for garbage classification according to claim 1 is characterized in that: The recording module uses a high-definition camera as the main image recording means to record the garbage. Then, the processing module processes and analyzes whether the garbage has a label based on the garbage image recorded by the recording module.

3. The intelligent monitoring system for garbage classification according to claim 2 is characterized in that: When the processing module processes and analyzes that the garbage image does not have a label, the detection module detects the garbage. The detection module extracts useful features from multiple data such as images, sounds, and gas sensors, and matches them according to the data provided by the material cloud. At the same time, the evaluation module evaluates the garbage category based on the garbage material results matched by the detection module. When the processing module processes and analyzes that the garbage image has a label, the identification module identifies whether the garbage image label can be traced back to the source.

4. The intelligent monitoring system for garbage classification according to claim 3 is characterized in that: The identification module performs label identification analysis on the labeled garbage. Whether the label can be traced back depends on the formula table, name, etc. When the identification module identifies that the garbage label cannot be traced back, the detection module detects the garbage. When the identification module identifies that the garbage label can be traced back, the traceability module performs traceability processing. The traceability module analyzes and evaluates the garbage category based on the data support provided by the product cloud. The evaluation results processed by the evaluation module and the traceability module are sorted by the data sorting module and then handed over to the analysis unit for analysis and processing.

5. The intelligent monitoring system for garbage classification according to claim 1 is characterized in that: The analysis unit includes an analysis module for analyzing whether the garbage is correctly put into the garbage, the analysis module is electrically connected to a recording module for recording correct and incorrect garbage putting into the garbage, the recording module is electrically connected to a feature extraction module for extracting the characteristics of garbage with an error rate of putting into the garbage exceeding 5%, the feature extraction module is electrically connected to a model building module for building a model according to the garbage extracted by the feature extraction module, the model building module is electrically connected to a feature marking module for marking the category characteristics of the garbage model, and the feature marking module is electrically connected to a display reminder module for displaying the garbage model and the garbage characteristics.

6. The intelligent monitoring system for garbage classification according to claim 5 is characterized in that: The analysis module analyzes whether the garbage input is correct according to the garbage attributes provided by the monitoring unit, and records the correct garbage input and the wrong garbage input through the recording module. The feature extraction module performs data analysis based on the data information recorded by the recording module, and performs feature extraction based on the total amount of various garbage input and the garbage input error rate exceeding 5% during a period of 5-7 days.

7. The intelligent monitoring system for garbage classification according to claim 6 is characterized in that: The model building module organizes, analyzes and evaluates the garbage products and garbage images provided by the feature extraction module to establish a garbage model, and the feature marking module marks the feature information of the garbage model, the feature information includes but is not limited to garbage categories, garbage traceability products, and garbage assessment category theory. The display reminder module displays the garbage model and feature information for a period of 5-7 days.

8. The intelligent monitoring system for garbage classification according to claim 1 is characterized in that: The specific operations of the monitoring unit and the analysis unit include the following steps: S1. Use monitoring units to monitor the specific categories of garbage disposal; S2. Analyze whether the garbage is placed correctly by using the analysis unit and give reminders for garbage placement with a high error rate.

9. The intelligent monitoring system for garbage classification according to claim 8 is characterized in that: In step S1: 1) Use a high-definition camera to capture garbage images, and the processing module analyzes the captured garbage images to determine whether the garbage has a label; 2) For garbage with labels, the identification module checks whether the labels can be traced. If the labels can be traced, the traceability module traces the garbage based on the product cloud data and evaluates the garbage attributes. The data sorting module sorts the traceability results and passes them to the analysis unit. If the labels cannot be traced, the detection module detects the garbage, including image, sound, gas sensor and other data, matches the material cloud data, evaluates the garbage attributes, and the data sorting module sorts the detection and evaluation results and passes them to the analysis unit. 3) Processing of unlabeled garbage. The detection module detects the garbage, including image, sound, gas sensor and other data, and matches the material cloud data. The evaluation module evaluates the garbage properties based on the detection results. The data sorting module sorts the evaluation results and passes them to the analysis unit.

10. The intelligent monitoring system for garbage classification according to claim 8 is characterized in that: In step S2: 1) The analysis unit uses the analysis module to determine whether the garbage is correctly classified. The recording module records the correctly and incorrectly classified garbage. The feature extraction module extracts the features of garbage with an input error rate of more than 5%. The model building module builds a garbage classification model based on the extracted features. The feature marking module marks the garbage category and other feature information for the model. The display reminder module displays the garbage model and feature information. 2) Among them, feature extraction is performed based on the misclassified data in the recording module; 3) The evaluation period is 5-7 days, during which the model is updated and the classification system is optimized based on the garbage input data during this period.

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