Garbage room integrated management method and system

Abstract

The invention discloses a garbage room comprehensive management method and system. The method comprises the following steps: establishing a recyclable molecular spectrum knowledge base; collecting image information of the garbage; calling interface identification; classifying for the first time; packaging the non-resourceful wastes; collecting near infrared spectrum data of the recyclable wastes; screening key characteristic wavelength points; similarity calculation is conducted, recyclable molecules of the recyclable waste are obtained, and mixed spectrum similarity measurement is adopted for similarity calculation; and performing secondary classification. The system comprises an information acquisition module, an intelligent robot classification and packaging module, a data module and a central control module. According to the method, the garbage is automatically identified, and the recyclable wastes in the garbage are divided out for independent treatment, so that the utilization rate of the wastes is increased; and meanwhile, a recyclable molecular spectrum knowledge base is established, the characteristic spectrum of the recyclable waste and the recyclable molecular spectrum are subjected to mixing similarity calculation, the classification of the recyclable waste is judged, and the subsequent recycling efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of garbage room management, and in particular relates to a garbage room comprehensive management method and system. Background Art

[0002] With the acceleration of urbanization, the amount of urban garbage has continued to increase, causing great impacts on the environment and public health. Many cities do not thoroughly sort garbage, and residents generally lack awareness and knowledge of garbage sorting, resulting in the mixing of different types of garbage, which increases the complexity and difficulty of subsequent garbage disposal; however, traditional garbage disposal methods such as landfill and incineration are prone to waste of resources and fail to make full use of materials.

[0003] At the same time, in the process of waste resource treatment, there are still problems such as cumbersome processing steps and low efficiency, which not only increases the processing cost, but also slows down the speed of resource recovery. Therefore, it is urgent to design a new method and system to solve the above problems. Summary of the invention

[0004] Purpose of the invention: In order to overcome the above shortcomings, the purpose of the present invention is to provide a comprehensive management method and system for garbage rooms, which can automatically identify garbage and separate the recyclable waste from the garbage for separate treatment, thereby improving the waste utilization rate and protecting the environment; at the same time, a knowledge base of recyclable molecular spectra is established, and the characteristic spectra of recyclable waste and the spectra of recyclable molecules are mixed for similarity calculation to determine their classification, thereby improving the efficiency of subsequent resource utilization.

[0005] Technical solution: In order to achieve the above purpose, the present invention provides a comprehensive management method for garbage rooms, comprising the following steps:

[0006] S1): Establish a resourceable molecular spectroscopy knowledge base through web crawler technology;

[0007] S2): Collect image information of garbage through IoT devices;

[0008] S3): Calling the interface to identify whether the garbage is recyclable waste;

[0009] S4): The intelligent robot performs the first classification based on the recognition results, dividing the garbage into recyclable waste and non-recyclable waste;

[0010] S5): The intelligent robot packs the non-recyclable waste and adds a non-recyclable label for subsequent landfill or incineration;

[0011] S6): Collect near infrared spectral data of recyclable waste;

[0012] S7): Using the variance threshold method to screen out key characteristic wavelength points from the near-infrared spectrum data;

[0013] S8): Calculate the similarity between the characteristic wavelength point and the recyclable molecule spectrum in the recyclable molecule spectrum knowledge base to obtain the recyclable molecules of the recyclable waste;

[0014] S801): Similarity calculation is achieved by using a hybrid spectral similarity measure, combining spectral angle matching and spectral information divergence;

[0015] S9): The intelligent robot performs a second classification based on the recognition results, and classifies and packages the recyclable waste according to the recyclable molecules for subsequent resource processing.

[0016] Furthermore, the web crawler technology in S1) can automatically perform tasks without human intervention, and can quickly extract data from a large number of web pages, facilitating the establishment of a resource-based molecular spectroscopy knowledge base; S3) calls on existing mature image recognition interfaces to identify garbage, thereby improving classification efficiency; S5) and S9) introduce intelligent robots to complete garbage classification and packaging operations, reducing human intervention and improving work efficiency.

[0017] Further, the S801) includes:

[0018] S80101): Calculate the SAM value of the spectral angle matching between the characteristic wavelength point and each resourceable molecular spectrum, the formula is as follows:

[0019]

[0020] Where A and B are two spectral vectors, A·B represents the dot product of the vectors, and ||A|| and ||B|| represent the modulus of the vectors respectively. Spectral angle matching is a similarity measurement method based on vector angles, which is used to evaluate the similarity between two spectra.

[0021] S80102): Calculate the spectral information divergence SID value between the characteristic wavelength point and each resourceable molecular spectrum, the formula is as follows:

[0022]

[0023] Where p(i) and q(i) are the normalized probability distributions of the characteristic wavelength point and the resourceable molecule at the i-th wavelength point, respectively, and n is the total number of wavelength points; spectral information divergence is a similarity measurement method based on information theory, which is used to evaluate the information difference between two spectra;

[0024] S80103): Calculate the hybrid spectral similarity Hybrid-SS, the formula is as follows:

[0025] Hybrid-SS=SID×tan(SAM)

[0026] Among them, SAM is the spectral angle matching value, and SID is the spectral information divergence value. By combining spectral angle matching and spectral information divergence, the similarity between spectra can be evaluated more comprehensively, which improves the accuracy of molecular classification judgment of resource waste.

[0027] Furthermore, the variance threshold method in S7) screens the key characteristic wavelength points from the near infrared spectrum data, including:

[0028] S701): For each characteristic wavelength point, calculate its variance;

[0029] S702): Select the characteristic wavelength point with the largest variance as the key characteristic wavelength point.

[0030] Furthermore, the characteristic wavelength point with the largest variance is selected as the key characteristic wavelength point, which reduces the dimension of the data, simplifies the data calculation, reduces the use of computing resources, and reduces the cost of operation and maintenance.

[0031] Furthermore, in S6), near-infrared optical fiber spectrometer is used to collect near-infrared spectrum data. The near-infrared optical fiber spectrometer performs spectrum analysis based on reflection measurement and is suitable for garbage sorting.

[0032] Furthermore, the interface called in S3) is a YOLOv8 pre-trained model for garbage classification. YOLOv8 detects all objects in the image through a single forward propagation, which greatly improves the real-time performance of the object detection task and improves the detection efficiency.

[0033] Furthermore, the resource-recyclable molecular spectrum knowledge base in S1) includes a number of resource-recyclable molecular spectrum data and their types. By comparing the key characteristic wavelength points collected in the spectrum with the existing resource-recyclable molecular spectrum data and their types in the resource-recyclable molecular spectrum knowledge base, it is possible to obtain what resource-recyclable molecules the currently identified resource-recyclable garbage contains, which is convenient for subsequent packaging and processing.

[0034] The present invention also provides a garbage room integrated management system, including: an information collection module, an intelligent robot classification and packaging module, a data module and a central control module;

[0035] The information collection module, intelligent robot classification and packaging module, and data module are all connected to the central control module to realize signal transmission; the information collection module completes the collection of garbage image information and the collection of near-infrared spectrum data of resource-recyclable waste, and transmits the collected data to the data module for processing; the intelligent robot classification and packaging module is controlled by the central control module, and the central control module issues action instructions to realize the classification and packaging of garbage; the data module stores a resource-recyclable molecular spectrum knowledge base, and is used to call the interface to identify the garbage category, and calculate and identify the resource-recyclable waste category. The central control module ensures the reliability of data transmission between the information collection module, the intelligent robot classification and packaging module, and the data module; the information collection module provides a collection window for the data source; the data module ensures the accuracy of data calculation and processing; the intelligent robot classification and packaging module provides reliable support for the implementation of classification and packaging actions.

[0036] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0037] 1. The present invention provides a comprehensive management method and system for garbage rooms, which can automatically identify garbage, separate the recyclable waste from the garbage and process it separately, thereby improving the utilization rate of waste and protecting the environment;

[0038] 2. The present invention provides a comprehensive management method and system for garbage rooms, establishes a knowledge base of resource-recyclable molecular spectra, and performs mixed similarity calculations on the characteristic spectra of resource-recyclable wastes and the resource-recyclable molecular spectra to determine their classification, thereby improving the efficiency of subsequent resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of the steps of a comprehensive garbage room management method according to the present invention;

[0040] Figure 2 It is a schematic diagram of a flow chart of a comprehensive management method for a garbage room according to the present invention;

[0041] Figure 3 This is a module diagram of a garbage room comprehensive management system described in the present invention. DETAILED DESCRIPTION

[0042] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0043] Example

[0044] In this embodiment, Figure 1 and Figure 2 The present invention discloses a comprehensive management method for garbage rooms, comprising the following steps:

[0045] S1): Establish a resourceable molecular spectroscopy knowledge base through web crawler technology;

[0046] S2): Collect image information of garbage through IoT devices;

[0047] S3): Calling the interface to identify whether the garbage is recyclable waste;

[0048] S4): The intelligent robot performs the first classification based on the recognition results, dividing the garbage into recyclable waste and non-recyclable waste;

[0049] S5): The intelligent robot packs the non-recyclable waste and adds a non-recyclable label for subsequent landfill or incineration;

[0050] S6): Collect near infrared spectral data of recyclable waste;

[0051] S7): Using the variance threshold method to screen out key characteristic wavelength points from the near-infrared spectrum data;

[0052] S8): Calculate the similarity between the characteristic wavelength point and the recyclable molecule spectrum in the recyclable molecule spectrum knowledge base to obtain the recyclable molecules of the recyclable waste;

[0053] S801): Similarity calculation is achieved by using a hybrid spectral similarity measure, combining spectral angle matching and spectral information divergence;

[0054] S9): The intelligent robot performs a second classification based on the recognition results, and classifies and packages the recyclable waste according to the recyclable molecules for subsequent resource processing.

[0055] Specifically, the web crawler technology in S1) uses the Scrapy framework in conjunction with the Selenium crawler to automatically collect data on resource-available categories and their molecular spectra on the Internet, and saves them locally to build a knowledge base.

[0056] In particular, after the near-infrared spectral data of the resource-recyclable waste in S6) is collected, a spectral data preprocessing operation can be added to perform MSC optimization on the collected spectral data, perform linear transformation on each spectrum, eliminate the baseline offset and scattering effect between spectra, and obtain higher quality data for subsequent identification.

[0057] In this embodiment, Figure 2 , the S801) includes:

[0058] S80101): Calculate the SAM value of the spectral angle matching between the characteristic wavelength point and each resourceable molecular spectrum, the formula is as follows:

[0059]

[0060] Where A and B are two spectral vectors, A·B represents the dot product of the vectors, and ||A|| and ||B|| represent the modulus of the vectors respectively;

[0061] S80102): Calculate the spectral information divergence SID value between the characteristic wavelength point and each resourceable molecular spectrum, the formula is as follows:

[0062]

[0063] Where p(i) and q(i) are the normalized probability distributions of the characteristic wavelength point and the resourceable molecule at the i-th wavelength point, respectively, and n is the total number of wavelength points;

[0064] S80103): Calculate the hybrid spectral similarity Hybrid-SS, the formula is as follows:

[0065] Hybrid-SS=SID×tan(SAM)

[0066] Among them, SAM is the spectral angle matching value, and SID is the spectral information divergence value.

[0067] Specifically, the closer the value of the hybrid spectrum similarity Hybrid-SS is to 0, the higher the similarity between the test spectrum and the reference spectrum, and the recyclable molecule corresponding to the reference spectrum with the highest similarity is selected as the recyclable category of the corresponding recyclable waste.

[0068] In this embodiment, Figure 2 In S7), the variance threshold method is used to screen the key characteristic wavelength points from the near infrared spectrum data, including:

[0069] S701): For each characteristic wavelength point, calculate its variance;

[0070] S702): Select the characteristic wavelength point with the largest variance as the key characteristic wavelength point.

[0071] Specifically, let X be the data set of characteristic wavelength points, n be the number of samples, μ be the mean, then the variance of the i-th characteristic wavelength point is The calculation formula is as follows:

[0072]

[0073] Among them, X ij represents the value of the jth sample at the i-th characteristic wavelength point, μ iRepresents the mean value of the i-th characteristic wavelength point.

[0074] In particular, the sklearn library in the python package is called to implement the calculation of the variance threshold method.

[0075] In this embodiment, Figure 1 In said S6), a near-infrared fiber optic spectrometer is used to collect near-infrared spectral data.

[0076] In this embodiment, Figure 1 The interface called in S3) is a YOLOv8 pre-trained model for garbage classification.

[0077] Specifically, the YOLOv8 pre-training is completed on the COCO dataset; the YOLOv8 pre-training model interface is called through python and pytorch. When used, the pre-trained YOLOv8 model can be loaded by specifying the model path.

[0078] In particular, the pre-training interface can embed data pre-processing operations for garbage classification to ensure the accuracy of recognition.

[0079] In this embodiment, Figure 1 The resourceable molecular spectroscopy knowledge base in S1) includes a number of resourceable molecular spectroscopy data and their types.

[0080] Specifically, by calculating the similarity between the spectral data of key characteristic wavelength points collected in the spectrum and the existing spectral data of recyclable molecules in the spectral knowledge base of recyclable molecules, it is possible to determine what recyclable molecules the currently identified recyclable waste contains and the category to which the currently identified recyclable waste belongs, for subsequent packaging and processing.

[0081] In this embodiment, Figure 3 , the present invention also discloses a garbage room comprehensive management system, including: an information collection module, an intelligent robot classification and packaging module, a data module and a central control module;

[0082] The information acquisition module, the intelligent robot classification and packaging module, and the data module are all connected to the central control module to realize signal transmission; the information acquisition module completes the collection of image information of garbage and the collection of near-infrared spectrum data of resource-recyclable waste, and transmits the collected data to the data module for processing; the intelligent robot classification and packaging module is controlled by the central control module, and the central control module issues action instructions to realize the classification and packaging of garbage; the data module stores a resource-recyclable molecular spectrum knowledge base, and is used to realize the calling interface to identify the garbage category, and to calculate and identify the resource-recyclable waste category.

[0083] Specifically, the communication between the central control module and each module is realized by using PLC.

[0084] Specifically, the central control module performs logical judgment, filtering, amplification and other processing on the received signal to ensure the accuracy and reliability of the signal.

[0085] The above description is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be regarded as within the protection scope of the present invention.

Claims

1. A comprehensive management method for garbage rooms, characterized by: The steps include: S1): Establish a resourceable molecular spectroscopy knowledge base through web crawler technology; S2): Collect image information of garbage through IoT devices; S3): Calling the interface to identify whether the garbage is recyclable waste; S4): The intelligent robot performs the first classification based on the recognition results, dividing the garbage into recyclable waste and non-recyclable waste; S5): The intelligent robot packs the non-recyclable waste and adds a non-recyclable label for subsequent landfill or incineration; S6): Collect near infrared spectral data of recyclable waste; S7): Using the variance threshold method to screen out key characteristic wavelength points from the near-infrared spectrum data; S8): Calculate the similarity between the characteristic wavelength point and the recyclable molecule spectrum in the recyclable molecule spectrum knowledge base to obtain the recyclable molecules of the recyclable waste; S801): Similarity calculation is achieved by using a hybrid spectral similarity measure, combining spectral angle matching and spectral information divergence; S9): The intelligent robot performs a second classification based on the recognition results, and classifies and packages the recyclable waste according to the recyclable molecules for subsequent resource processing.

2. The comprehensive management method for garbage rooms according to claim 1, characterized in that: The S801) includes: S80101): Calculate the SAM value of the spectral angle matching between the characteristic wavelength point and each resourceable molecular spectrum, the formula is as follows: Where A and B are two spectral vectors, A·B represents the dot product of the vectors, and ||A|| and ||B|| represent the modulus of the vectors respectively; S80102): Calculate the spectral information divergence SID value between the characteristic wavelength point and each resourceable molecular spectrum, the formula is as follows: Where p(i) and q(i) are the normalized probability distributions of the characteristic wavelength point and the resourceable molecule at the i-th wavelength point, respectively, and n is the total number of wavelength points; S80103): Calculate the hybrid spectral similarity Hybrid-SS, the formula is as follows: Hybrid-SS=SID×tan(SAM) Among them, SAM is the spectral angle matching value, and SID is the spectral information divergence value.

3. The comprehensive management method for garbage rooms according to claim 1, characterized in that: The variance threshold method in S7) screens the key characteristic wavelength points from the near infrared spectrum data, including: S701): For each characteristic wavelength point, calculate its variance; S702): Select the characteristic wavelength point with the largest variance as the key characteristic wavelength point.

4. The comprehensive management method for garbage rooms according to claim 3, characterized in that: In the step S6), a near-infrared optical fiber spectrometer is used to collect near-infrared spectrum data.

5. The comprehensive management method for garbage rooms according to claim 1, characterized in that: The interface called in S3) is a YOLOv8 pre-trained model for garbage classification.

6. The comprehensive management method for garbage rooms according to claim 1, characterized in that: The resourceable molecular spectroscopy knowledge base in S1) includes a number of resourceable molecular spectroscopy data and their types.

7. A garbage room integrated management system, used to implement a garbage room integrated management method according to claims 1 to 6, characterized in that: include: Information collection module, intelligent robot classification and packaging module, data module and central control module; The information collection module, intelligent robot classification and packaging module, and data module are all connected to the central control module to realize signal transmission; the information collection module completes the collection of garbage image information and the collection of near-infrared spectrum data of resource-recyclable waste, and transmits the collected data to the data module for processing; The intelligent robot classification and packaging module is controlled by the central control module, which issues action instructions to achieve garbage classification and packaging; the data module stores a resource-recyclable molecular spectrum knowledge base and is used to implement the calling interface to identify the garbage category and calculate and identify the resource-recyclable waste category.

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