An intelligent classification control system and method for urban garbage recycling

Abstract

The present application discloses an intelligent classification control system and method for urban garbage recycling, which belongs to the field of garbage recycling. The present application assigns weights to misclassified garbage based on the error analysis results of the misclassified garbage, performs classification effect analysis based on the weight assignment results of the misclassified garbage, classification data of the garbage classification, and classification accuracy data, and issues classification anomaly warnings based on the obtained classification effect analysis results. The present application accurately identifies classification anomalies by performing comprehensive data analysis on classification errors in the garbage classification process, avoids the influence of image recognition factors on the accuracy of judging classification anomalies, and improves the recognition accuracy and efficiency of classification anomalies.

Description

Technical Field

[0001] The present application relates to the field of waste recycling, and more specifically to an intelligent classification control system and method for urban waste recycling. Background Art

[0002] The automatic garbage sorting system first uses recognition technology to distinguish different types of garbage, which usually includes image recognition, infrared recognition, sensor technology, etc. For example, the image recognition system can capture images of garbage through a camera and use image processing algorithms to identify its type. Once the garbage is identified, the sorting mechanism will separate it according to the type of garbage. This may include robotic arms, pneumatic sorting devices, vibrating conveyor belts, etc. For example, the robotic arm can grab a specific type of garbage according to instructions and put it into the corresponding recycling bin. The automatic garbage sorting system usually integrates artificial intelligence algorithms such as deep learning, machine learning, etc. to continuously improve the accuracy and efficiency of classification. The algorithm can learn from a large amount of data. Learning from the experience of the user, thus better identifying and classifying garbage, the principle of automatic garbage classification aims to reduce manual intervention, improve the efficiency and accuracy of garbage classification, while reducing environmental pollution and promoting resource recycling and reuse; in the process of garbage classification, classification errors often occur. The cause of classification errors is most often due to failure of the classification module, and a small part of the cause is the accuracy of the image recognition algorithm. In the process of identifying abnormal classification, it is impossible to quickly analyze whether it is due to the accuracy of the image recognition algorithm or the failure of the classification module, resulting in low accuracy and efficiency in identifying abnormal classification. Most existing technologies have the above problems;

[0003] In order to improve the recognition accuracy and efficiency of classification anomalies, this application designs an intelligent classification control system and method for urban waste recycling. Summary of the Invention

[0004] In order to solve the deficiencies in the existing technology mentioned in the background technology, the present application proposes an intelligent classification control system and method for urban waste recycling. The present application assigns weights to classified erroneous waste based on the error analysis results of the erroneous waste, performs classification effect analysis based on the weight assignment results of the classified erroneous waste, the classification data of the waste classification, and the classification accuracy data, and performs classification anomaly warning based on the obtained classification effect analysis results. The present application conducts comprehensive data analysis on the classification errors in the waste classification process, accurately identifies the classification anomalies, avoids the influence of image recognition factors on the accuracy of classification anomaly judgment, and improves the recognition accuracy and efficiency of classification anomalies.

[0005] To achieve the above objectives, the present application provides the following technical solutions: First, the present application provides an intelligent classification control method for urban waste recycling, which includes the following specific steps:

[0006] Step 1: Obtain classification data of garbage classification by garbage classification equipment;

[0007] Step 2: Perform error spam error analysis based on the misclassified spam information, and assign misclassified spam weights based on the error spam error analysis results;

[0008] Step 3: Analyze the classification effect based on the weight distribution results of misclassified garbage, the classification data of garbage classification, and the classification accuracy data;

[0009] Step 4: Perform classification anomaly warning based on the obtained classification effect analysis results.

[0010] Preferably, the specific content of the classification data of the garbage classification equipment is:

[0011] Step 11: Obtain the successfully classified items during the operation of the garbage sorting equipment and store the obtained data;

[0012] Step 12: Obtain images and size data of classified items that fail to be classified during the operation of the garbage sorting equipment, obtain images and size data of corresponding items in the database, and simultaneously obtain images and size data of corresponding items in the database of classified items that fail to be classified, and store the obtained data.

[0013] Preferably, the error spam error analysis based on misclassified spam information includes the following specific steps:

[0014] Step 21: Acquire images and size data of the classified items that failed classification, and simultaneously acquire images and size data of the corresponding items in the database for the classified items that failed classification;

[0015] Step 22: Obtain the classified product image similarity based on the image of the classified item that failed classification and the image of the corresponding item in the database. The classified product image similarity here can be one of the image similarity calculation formulas such as cosine similarity, and obtain the maximum classified product image similarity corresponding to the classified item;

[0016] Step 23: Obtain the similarity of misclassified garbage images based on the image of the classified item that failed classification and the image of the corresponding item in the database. It should be noted that the similarity calculation formula for misclassified garbage images is the same as that for classified product images. Obtain the maximum similarity of misclassified garbage images corresponding to the classified items.

[0017] Step 24: Obtain the calculated maximum similarity of the classified product image corresponding to the classified item and the maximum similarity of the classified error garbage image corresponding to the classified item, and import them into the error garbage error value calculation formula to calculate the error garbage error value, wherein the error garbage error value is used to reflect the degree to which the classification error is affected by image recognition. The error garbage error value calculation formula of the i-th classified error garbage is: , where exp() is the power of the natural constant e, Si is the maximum similarity of the misclassified garbage image corresponding to the i-th misclassified garbage, and Mi is the maximum similarity of the classified product image corresponding to the i-th misclassified garbage.

[0018] Preferably, the step of allocating weights of classified erroneous garbage according to the error analysis results of the erroneous garbage includes the following specific steps:

[0019] Step 25: Obtain the calculated error values ​​of all misclassified garbage, and import the obtained error values ​​of all misclassified garbage into the misclassified garbage weight calculation formula to calculate the misclassified garbage weight. The weight calculation formula of the i-th misclassified garbage is: , where the formula weights the error calculation by the error difficulty of classifying the wrong garbage.

[0020] Preferably, the classification effect analysis includes the following specific contents: Step 31, obtaining the number of correct classifications, the number of incorrect classifications, and the weight distribution results of incorrectly classified garbage in the previous cycle, and simultaneously obtaining classification accuracy data, and weighting the number of incorrectly classified garbage sites based on the weight distribution results of incorrectly classified garbage;

[0021] Step 32: Perform classification effect analysis based on the number of correct classifications, the number of incorrect classifications, the weight distribution results of incorrectly classified garbage sites, and the classification accuracy data obtained in the previous cycle. The classification effect is proportional to the number of correct classifications and the classification accuracy data, and inversely proportional to the number of incorrect classifications and the weight distribution results of incorrectly classified garbage sites. The classification effect calculation formula is: , where g is the classification accuracy data, zq is the number of garbage classifications that are correct, and m is the number of classification errors. is the classification error influence coefficient, which is used to reflect the impact of classification error on classification effect.

[0022] Preferably, the classification abnormality warning based on the obtained classification effect analysis results includes the following specific contents: the classification effect obtained in the previous period is compared with the set classification effect threshold. If the classification effect is less than the set classification effect threshold, it means that the classification is abnormal and the classification module of the equipment needs to be maintained, and the maintenance personnel are warned. If the classification effect is greater than or equal to the set classification effect threshold, it means that the classification is normal.

[0023] It should be noted that the values ​​of the setting parameters of this application are obtained by those skilled in the art through historical data experiments.

[0024] In the second aspect, the present application provides an intelligent classification and control system for urban waste recycling, which is implemented based on the above-mentioned intelligent classification control method for urban waste recycling, and specifically includes an acquisition unit, an error analysis unit, a classification effect analysis unit, a classification abnormality warning unit and a control unit; wherein, the acquisition unit is used to obtain the classification data of garbage classification of the garbage classification equipment; the error analysis unit performs error analysis of erroneous garbage based on the information of misclassified garbage, and assigns weights of classified erroneous garbage according to the error analysis results of the erroneous garbage; the classification effect analysis unit performs classification effect analysis based on the weight assignment results of classified erroneous garbage, the classification data of garbage classification and the classification accuracy data; the classification abnormality warning unit is used for the classification abnormality warning unit, and the control unit is used to control the operation of the acquisition unit, the error analysis unit, the classification effect analysis unit and the classification abnormality warning unit.

[0025] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0026] The processor executes the above-mentioned intelligent classification control method for urban waste recycling by calling the computer program stored in the memory.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes an intelligent classification control method for urban waste recycling as described above.

[0028] At the same time, compared with the existing technology, the technical effects and advantages of the present application are: the present application performs error analysis on misclassified garbage information, and assigns weights to misclassified garbage based on the error analysis results, performs classification effect analysis based on the weight assignment results of misclassified garbage, the classification data of garbage classification, and the classification accuracy data, and performs classification anomaly warning based on the obtained classification effect analysis results. The present application conducts comprehensive data analysis on misclassification situations in the garbage classification process, accurately identifies classification anomalies, avoids the influence of image recognition factors on the accuracy of classification anomaly judgment, and improves the recognition accuracy and efficiency of classification anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0030] Figure 1 This is a schematic diagram of the overall process of an embodiment of the present application method;

[0031] Figure 2 This is a schematic diagram of the specific flow of step 2 of the embodiment of the method of this application;

[0032] Figure 3 This is a schematic diagram of the specific process of step 4 of the embodiment of the method of this application;

[0033] Figure 4 This is a schematic diagram of the overall framework of the system embodiment of this application. DETAILED DESCRIPTION

[0034] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only some embodiments of the present application, rather than all embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present application, its application, or use.

[0035] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0036] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and a similar second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0037] Example 1

[0038] In order to solve the technical problems raised in the background technology, this application provides a preferred embodiment: Figure 1-Figure 3 As shown, a method for intelligent classification control of urban waste recycling includes the following specific steps:

[0039] Step 1: Obtain classification data of garbage classification by garbage classification equipment;

[0040] In this embodiment, the specific content of the classification data of the garbage classification device is:

[0041] Step 11: Obtain the successfully classified items during the operation of the garbage sorting device. For example, if the input garbage is a glass, the identified garbage is also a glass, which means the classification is successful, and the obtained data is stored;

[0042] Step 12: Acquire images and size data of classified items that failed to be classified during the operation of the garbage sorting device. For example, if the input garbage is a glass, but the identified garbage is a kettle, which will cause classification failure, obtain the image and size data of the corresponding classified items in the database, that is, the image and size data of the glass in the database. At the same time, obtain the image and size data of the corresponding classified items in the database that failed to be classified, that is, the image and size data of the kettle in the database, and store the obtained data; the image and size data here are obtained by the sensors configured in the system;

[0043] Step 2: Perform error spam error analysis based on the misclassified spam information, and assign misclassified spam weights based on the error spam error analysis results;

[0044] In this embodiment, performing error spam error analysis based on misclassified spam includes the following specific steps:

[0045] Step 21: Acquire images and size data of the classified items that failed classification, and simultaneously acquire images and size data of the corresponding items in the database for the classified items that failed classification;

[0046] Step 22: Obtain the classified product image similarity based on the image of the classified item that failed classification and the image of the corresponding item in the database. The classified product image similarity here can be one of the image similarity calculation formulas such as cosine similarity, and obtain the maximum classified product image similarity corresponding to the classified item;

[0047] Image similarity calculation is used to measure the similarity between two images in terms of content, color, shape, etc. For example: Histogram comparison: Color histogram comparison: Determine the similarity of two images by comparing their color histograms. Common methods include: Chi-square test, cross entropy, etc. Texture histogram comparison: Analyze image texture features, such as using local binary pattern (LBP); Feature point matching: SIFT (Scale Invariant Feature Transform): Extract key points and perform feature description, and then calculate similarity by the distance between feature points; SURF (Speeded Robust Features): Similar to SIFT, but faster to calculate and suitable for real-time applications; ORB (Oriented FAST and Rotated BRIEF: It is an efficient and high-performance feature point detection and description algorithm; Structural Similarity Metric (SSIM): The SSIM index is a metric that measures the similarity between two images, taking into account changes in brightness, contrast, and structure; Template Matching: Template matching is a method for finding small images within a large image, usually using correlation coefficients or other similarity metrics as matching criteria; Convolutional Neural Networks (CNNs) are used to extract high-level features of images and then calculate the similarity between feature vectors, such as using cosine similarity and Euclidean distance;

[0048] Step 23: Obtain the similarity of misclassified garbage images based on the image of the classified item that failed classification and the image of the corresponding item in the database. It should be noted that the similarity calculation formula for misclassified garbage images is the same as that for classified product images. Obtain the maximum similarity of misclassified garbage images corresponding to the classified items.

[0049] Step 24: Obtain the calculated maximum similarity of the classified product image corresponding to the classified item and the maximum similarity of the classified error garbage image corresponding to the classified item, and import them into the error garbage error value calculation formula to calculate the error garbage error value, wherein the error garbage error value is used to reflect the degree to which the classification error is affected by image recognition. The error garbage error value calculation formula of the i-th classified error garbage is: , where exp() is the power of the natural constant e, Si is the maximum similarity of the misclassified garbage image corresponding to the i-th misclassified garbage, and Mi is the maximum similarity of the classified product image corresponding to the i-th misclassified garbage. At the same time, the weight allocation of misclassified garbage according to the error analysis results includes the following specific steps:

[0050] Step 25: Obtain the calculated error values ​​of all misclassified garbage, and import the obtained error values ​​of all misclassified garbage into the misclassified garbage weight calculation formula to calculate the misclassified garbage weight. The weight calculation formula of the i-th misclassified garbage is: ,In this formula, the error calculation is weighted by the error difficulty of classifying the wrong garbage;

[0051] Step 3: Analyze the classification effect based on the weight distribution results of misclassified garbage, the classification data of garbage classification, and the classification accuracy data;

[0052] In this embodiment, the classification effect analysis includes the following specific contents: Step 31, obtaining the number of correct classifications, the number of incorrect classifications, and the weight distribution results of incorrectly classified garbage in the previous cycle, and simultaneously obtaining the classification accuracy data, and weighting the incorrectly classified garbage sites based on the weight distribution results;

[0053] Step 32: Perform classification effect analysis based on the number of correct classifications, the number of incorrect classifications, the weight distribution results of incorrectly classified garbage sites, and the classification accuracy data obtained in the previous cycle. The classification effect is proportional to the number of correct classifications and the classification accuracy data, and inversely proportional to the number of incorrect classifications and the weight distribution results of incorrectly classified garbage sites. The classification effect calculation formula is: , where g is the classification accuracy data, zq is the number of garbage classifications that are correct, and m is the number of classification errors. is the classification error influence coefficient, which is used to reflect the impact of classification error on classification effect;

[0054] Step 4: Perform classification anomaly warning based on the obtained classification effect analysis results;

[0055] In this embodiment, the classification abnormality warning based on the obtained classification effect analysis results includes the following specific contents: the classification effect obtained in the previous period is compared with the set classification effect threshold. If the classification effect is less than the set classification effect threshold, it means that the classification is abnormal and the classification module of the equipment needs to be maintained, and the maintenance personnel are warned. If the classification effect is greater than or equal to the set classification effect threshold, it means that the classification is normal.

[0056] Secondly, it should be noted that in this embodiment, the value-taking method of the setting parameters in this embodiment is obtained by technical personnel in this field through historical data experiments. Here, one of the acquisition methods is as follows: obtain the classification data of garbage classification and the cause data of classification abnormalities of historical garbage classification equipment, substitute them into the steps of this embodiment and finally substitute them into the classification effect calculation formula to calculate the classification effect, substitute the judgment result of the cause of the classification abnormality and the classification effect calculation result into the fitting software (such as matlab or python) to obtain the value of the relevant setting parameters that meet the maximum judgment accuracy.

[0057] Finally, the benefits of this embodiment are explained here. This embodiment conducts comprehensive data analysis on classification errors in the garbage classification process, accurately identifies classification anomalies, avoids the influence of image recognition factors on the accuracy of classification anomalies, and improves the accuracy and efficiency of identifying classification anomalies.

[0058] Example 2

[0059] like Figure 4 As shown, this embodiment provides an intelligent classification control system for urban waste recycling, which is implemented based on the above-mentioned intelligent classification control method for urban waste recycling, and specifically includes an acquisition unit, an error analysis unit, a classification effect analysis unit, a classification abnormality warning unit and a control unit; wherein the acquisition unit is used to obtain classification data of garbage classification of the garbage classification equipment; the error analysis unit performs error analysis of erroneous garbage based on the information of misclassified garbage, and assigns weights of misclassified garbage according to the error analysis results of the erroneous garbage; the classification effect analysis unit performs classification effect analysis according to the weight assignment results of misclassified garbage, classification data of garbage classification and classification accuracy data; the classification abnormality warning unit is used for the classification abnormality warning unit, and the control unit is used to control the operation of the acquisition unit, the error analysis unit, the classification effect analysis unit and the classification abnormality warning unit. At the same time, it should be noted that Figure 4 The direction of the arrow in the figure represents the direction of control signal transmission. The control unit controls the operation of other units through control systems such as PLC.

[0060] Example 3

[0061] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0062] The processor executes the above-mentioned intelligent classification control method for urban garbage recycling by calling the computer program stored in the memory.

[0063] The electronic device may vary significantly due to configuration or performance, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the intelligent classification control method for urban waste recycling provided by the above-mentioned method embodiment. The electronic device may also include other components for implementing the device's functions. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface to facilitate data input and output. This embodiment will not be described in detail here.

[0064] Example 4

[0065] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0066] When the computer program runs on a computer device, the computer device executes the above-mentioned intelligent classification control method for urban garbage recycling.

[0067] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0068] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0069] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0070] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0071] The above-described specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for intelligent classification control of urban waste recycling, characterized in that: It includes the following specific steps: Obtain classification data of waste classification by waste classification equipment; Perform error spam error analysis based on misclassified spam information, and assign misclassified spam weights based on the error spam error analysis results; Analyze the classification effect based on the weight distribution results of misclassified garbage, the classification data of garbage classification, and the classification accuracy data; The classification effect analysis includes the following specific contents: Obtain the number of correct classifications, the number of incorrect classifications, and the weight distribution results of incorrectly classified garbage in the previous cycle, as well as the classification accuracy data. Use the weight distribution results of incorrectly classified garbage to weight the number of incorrectly classified garbage sites. The classification effect is analyzed by obtaining the number of correct classifications of garbage in the previous cycle, the number of incorrect classifications, the weight distribution results of the incorrectly classified garbage sites, and the classification accuracy data. The classification effect is proportional to the number of correct classifications of garbage and the classification accuracy data, and the classification effect is inversely proportional to the number of incorrect classifications and the weight distribution results of the incorrectly classified garbage sites. Based on the obtained classification effect analysis results, classification abnormality warning is issued.

2. The intelligent classification control method for urban waste recycling according to claim 1, characterized in that: The error spam error analysis based on misclassified spam information includes the following specific steps: Acquire images and size data of the classified items that failed classification, and simultaneously acquire images and size data of the corresponding items in the database for the classified items that failed classification; Acquire the similarity of the classified product images based on the images of the classified items that failed to be classified and the images of the corresponding classified items in the database, and obtain the maximum similarity of the classified product images corresponding to the classified items; Obtaining the similarity of misclassified garbage images based on the image of the classified object that failed to be classified and the image of the corresponding object in the database corresponding to the classified object that failed to be classified, and obtaining the maximum misclassified garbage image similarity corresponding to the classified object; The calculated maximum classified product image similarity corresponding to the classified items and the maximum classified error garbage image similarity corresponding to the classified items are obtained, and the error garbage error value calculation formula is imported to calculate the error garbage error value.

3. The intelligent classification control method for urban waste recycling according to claim 1, characterized in that: The weight allocation of classified error garbage according to the error garbage error analysis result includes the following specific steps: Obtain the calculated error garbage error values ​​of all misclassified garbage, and import the obtained error garbage error values ​​of all misclassified garbage into a classification error garbage weight calculation formula to calculate the classification error garbage weight.

4. The intelligent classification control method for urban waste recycling according to claim 1, characterized in that: The classification abnormality warning based on the obtained classification effect analysis results includes the following specific contents: the classification effect of the previous cycle is obtained and compared with the set classification effect threshold. If the classification effect is less than the set classification effect threshold, it means that the classification is abnormal, and the classification module of the equipment needs to be maintained, and the maintenance personnel are warned. If the classification effect is greater than or equal to the set classification effect threshold, it means that the classification is normal.

5. The intelligent classification control method for urban waste recycling according to claim 4, characterized in that: The specific content of the classification data of the garbage classification equipment is: Obtaining successfully classified items during the operation of the garbage sorting equipment and storing the obtained data; Obtain the image and size data of the classified items that failed to be classified during the operation of the garbage sorting equipment, obtain the image and size data of the corresponding items in the database of the corresponding classified items, and at the same time obtain the image and size data of the corresponding items in the database of the classified items that failed to be classified, and store the obtained data.

6. The intelligent classification control method for urban waste recycling according to claim 2, characterized in that: The method for calculating the similarity of the classified product images is one of the image similarity calculation formulas, and the similarity calculation formula of the classified error junk images is the same as the similarity of the classified product images.

7. An intelligent classification control system for urban waste recycling, which is implemented based on the intelligent classification control method for urban waste recycling according to any one of claims 1 to 6, characterized in that: It specifically includes an acquisition unit, an error analysis unit, a classification effect analysis unit, a classification abnormality warning unit and a control unit; wherein the acquisition unit is used to obtain the classification data of garbage classification of the garbage classification equipment; the error analysis unit performs error analysis of erroneous garbage based on the misclassified garbage information, and assigns weights of classified erroneous garbage according to the error analysis results; the classification effect analysis unit performs classification effect analysis according to the weight assignment results of classified erroneous garbage, the classification data of garbage classification and the classification accuracy data; the classification abnormality warning unit is used for the classification abnormality warning unit, and the control unit is used to control the operation of the acquisition unit, the error analysis unit, the classification effect analysis unit and the classification abnormality warning unit.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the urban waste recycling intelligent classification control method as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

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