Garbage classification and delivery management system and method

Abstract

The present invention belongs to the technical field of garbage classification and delivery management, and specifically relates to a garbage classification and delivery management system and method. The invention effectively identifies the standard delivery time period and delivery volume interval of each garbage bin through comprehensive analysis of the garbage delivery time, delivery volume and delivery type, and provides a basis for evaluating garbage delivery behavior. At the same time, the setting of the standard delivery time period can assist in judging the garbage cleaning time, so that the garbage cleaning process will not conflict with the time when the user delivers the garbage. In addition, through the real-time collection of garbage bin delivery information, the rationality of garbage delivery behavior can be judged in many aspects, unreasonable delivery behavior can be discovered in time, the effectiveness of the intervention of relevant management personnel can be improved, and unnecessary intervention can be avoided. In addition, users are reminded to pay attention to garbage classification through optimization suggestions, and residents are guided to change their irregular garbage classification and delivery habits.

Description

Technical Field

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

[0002] With the acceleration of urbanization, garbage classification has become an important part of urban management. However, the traditional garbage classification management method often relies on manual inspection and on-site supervision, which is inefficient and has blind spots in supervision. Therefore, the development of an intelligent and efficient garbage classification management system and method has become an urgent problem to be solved.

[0003] In the prior art, although there are some sensor-based garbage classification and delivery management systems, these systems often only focus on simple classification and delivery. They will issue warnings after garbage delivery errors and remind relevant staff to intervene. However, the amount of incorrectly delivered garbage may be small, and it will not increase the difficulty of subsequent cleaning. Such frequent warnings will undoubtedly cause a waste of human resources. Based on this, this solution provides a quantitative analysis of the amount of garbage delivered, combined with multi-dimensional information such as delivery time and delivery type, to achieve a management method for garbage classification and delivery. Summary of the invention

[0004] The purpose of the present invention is to provide a garbage classification and delivery management system and method, which can combine the amount of garbage delivered, delivery time and delivery type to perform multi-dimensional analysis, ensure the rationality of garbage delivery, and avoid excessive waste of human resources.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A garbage classification and placement management method, comprising:

[0007] Collecting garbage placement information in each garbage bin, wherein the garbage placement information includes placement time, placement amount, and garbage type;

[0008] Obtaining the garbage collection category corresponding to each of the garbage bins, and filtering out garbage placement information of different categories in each of the garbage bins according to the garbage collection category, and recording it as benchmark feature information;

[0009] Acquire feature extraction conditions, perform feature extraction on the reference feature information according to the feature extraction conditions, and record the result of the feature extraction as a key feature;

[0010] Evaluate the garbage placement situation of each garbage bin based on the key features, and output the rationality of the garbage placement behavior of each garbage bin;

[0011] When the garbage disposal behavior of the garbage bin is reasonable, the current garbage disposal behavior is recorded as a normal mode and stored as historical data;

[0012] When the placement behavior of the trash can is unreasonable, an alarm prompt is triggered, and the unreasonable behavior is output synchronously, and then optimization suggestions are matched according to the unreasonable behavior.

[0013] In a preferred embodiment, the step of obtaining the garbage collection category corresponding to each of the garbage bins, filtering out garbage placement information of different categories in each of the garbage bins according to the garbage collection category, and recording as the benchmark feature information includes:

[0014] Obtaining identification information of each of the trash cans, and determining the garbage collection category of each trash can according to the identification information of the trash can;

[0015] According to the garbage collection category, the garbage placement information in each of the garbage bins is screened and processed to obtain garbage placement information of multiple categories;

[0016] The garbage placement information belonging to the same garbage collection category in each of the garbage bins is classified and processed respectively, and the classification results are recorded as the benchmark feature information.

[0017] In a preferred embodiment, the step of obtaining a feature extraction condition and extracting a feature of the reference feature information according to the feature extraction condition to obtain a key feature includes:

[0018] Acquire preset feature extraction conditions, wherein the feature extraction conditions include garbage delivery time, garbage delivery amount, and garbage delivery type;

[0019] According to the feature extraction condition, the reference feature information is traversed and processed to screen out the types of garbage in each of the garbage bins;

[0020] If the garbage type is unique and the garbage type is consistent with the garbage collection category of the garbage bin, it means that the garbage is put into the garbage bin normally, and the garbage putting time is recorded as the key feature;

[0021] If the type of garbage is not unique, the garbage placement time and garbage placement amount of each type of garbage in the garbage bin will continue to be collected and summarized as the key feature together with the garbage type.

[0022] In a preferred solution, after the garbage delivery time is collected, multiple parallel time periods are constructed, and historical garbage delivery time nodes that meet the garbage collection category of the garbage bin in each parallel time period are counted, and the number of garbage delivery times at the historical garbage delivery time nodes is recorded as the first condition parameter;

[0023] Obtaining an evaluation threshold, and comparing the first condition parameter with the evaluation threshold;

[0024] If the first condition parameter is less than the evaluation threshold, the historical garbage placement time node corresponding to the first condition parameter is recorded as an occasional placement node, and is simultaneously screened out;

[0025] If the first condition parameter is greater than or equal to the evaluation threshold, the historical garbage disposal time nodes in each of the parallel time periods are arranged in chronological order, and the historical garbage disposal time node with the highest ranking is recorded as the starting node, and the historical garbage disposal time node with the lowest ranking is recorded as the ending node;

[0026] A standard garbage disposal period is generated according to the starting node and the ending node, and the starting node and the ending node are offset to obtain a garbage cleaning period.

[0027] In a preferred solution, after the garbage delivery amount is collected, multiple delivery amount intervals are constructed, and the historical garbage delivery amount that meets the garbage collection category of the garbage bin in each delivery amount interval is counted, and the historical garbage delivery amount is recorded as the second condition parameter;

[0028] Obtaining a preset delivery amount threshold, and comparing the second condition parameter with the delivery amount threshold;

[0029] If the second condition parameter is less than or equal to the delivery amount threshold, the historical garbage delivery amount corresponding to the second condition parameter is recorded as normal delivery, and the delivery amount interval corresponding to the second condition parameter is recorded as the normal delivery interval;

[0030] If the second condition parameter is greater than the delivery amount threshold, the historical garbage delivery amount corresponding to the second condition parameter is recorded as high-frequency delivery, and the corresponding delivery amount interval is recorded as a high-frequency delivery interval.

[0031] In a preferred embodiment, the step of evaluating the garbage placement situation of each garbage bin based on the key features and outputting the rationality of the garbage placement behavior of each garbage bin includes:

[0032] Obtain in real time the types of garbage in each garbage bin that are inconsistent with the garbage collection category, and record them as the first evaluation parameter;

[0033] Obtaining the amount of garbage put in under the first evaluation parameter and recording it as the second evaluation parameter;

[0034] Obtaining the garbage delivery time under the second evaluation parameter and mapping it to a third evaluation parameter;

[0035] Obtaining an evaluation function, and inputting the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter into the evaluation function, and marking the output result of the evaluation function as an evaluation score;

[0036] Obtaining an evaluation threshold, and comparing the evaluation threshold with the evaluation score;

[0037] If the evaluation score is greater than or equal to the evaluation threshold, it indicates that the garbage placement in the corresponding garbage bin is unreasonable, and an alarm signal is issued simultaneously;

[0038] If the evaluation score is less than the evaluation threshold, it indicates that the garbage placement in the corresponding garbage bin is reasonable.

[0039] In a preferred solution, the value of the garbage delivery time mapped under the third evaluation parameter is associated with the delivery interval;

[0040] Among them, the value of the third evaluation parameter in the high-frequency delivery interval is greater than the mapped value of the third evaluation parameter in the normal delivery interval.

[0041] In a preferred embodiment, the step of matching optimization suggestions according to the unreasonable behavior includes:

[0042] Obtain the garbage delivery time, garbage delivery amount, garbage delivery type and corresponding garbage bin identification information under the unreasonable behavior, and record it as information to be optimized;

[0043] Obtaining preset optimization suggestions, wherein there are multiple optimization suggestions, each of which corresponds to an optimization condition information including garbage delivery time, garbage delivery amount, garbage delivery type and corresponding garbage bin identification information;

[0044] The information to be optimized under the unreasonable behavior is matched with the optimization condition information, and optimization suggestions are output according to the matching results.

[0045] The present invention also provides a garbage classification and delivery management system, which is applied to the above-mentioned garbage classification and delivery management method, comprising:

[0046] An information collection module, the information collection module is used to collect garbage placement information in each garbage bin, wherein the garbage placement information includes placement time, placement amount and garbage type;

[0047] An information screening module, the information screening module is used to obtain the garbage collection category corresponding to each of the garbage bins, and screen out the garbage placement information of different categories in each garbage bin according to the garbage collection category, and record it as the benchmark feature information;

[0048] A feature extraction module, the feature extraction module is used to obtain feature extraction conditions, and perform feature extraction on the reference feature information according to the feature extraction conditions, and record the result of the feature extraction as a key feature;

[0049] A status output module, the status output module is used to evaluate the garbage placement status of each garbage bin according to the key features, and output the rationality of the garbage placement behavior of each garbage bin;

[0050] An information storage module, wherein when the garbage disposal behavior of the garbage bin is reasonable, the information storage module is used to record the current garbage disposal behavior as a normal mode and store it as historical data;

[0051] The optimization module is used to trigger an alarm prompt when the placement behavior of the trash can is unreasonable, and simultaneously output the unreasonable behavior, and then match the optimization suggestions according to the unreasonable behavior.

[0052] And, an electronic device, the electronic device comprising:

[0053] at least one processor;

[0054] and a memory communicatively coupled to the at least one processor;

[0055] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned garbage classification and management method.

[0056] The technical effects achieved by the present invention are:

[0057] The present invention effectively identifies the standard delivery time period and delivery volume range of each garbage bin through comprehensive analysis of the garbage delivery time, delivery volume and delivery type, and provides a basis for evaluating garbage delivery behavior. At the same time, the setting of the standard delivery time period can assist in determining the garbage cleaning time, so that the garbage cleaning process will not conflict with the time when users deliver garbage. In addition, through the real-time collection of garbage bin delivery information, the rationality of garbage delivery behavior can be judged in many aspects, unreasonable delivery behavior can be discovered in time, the effectiveness of intervention by relevant management personnel can be improved, and unnecessary intervention can be avoided. In addition, users are reminded to pay attention to garbage classification through optimization suggestions, and residents are guided to change their irregular garbage classification and delivery habits. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic flow chart of the method of the present invention;

[0059] Figure 2 It is a system module diagram of the present invention;

[0060] Figure 3 It is a structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] See also Figure 1 As shown, the present invention provides a garbage classification management method, comprising:

[0065] S1. Collecting garbage placement information in each garbage bin, wherein the garbage placement information includes the time of placement, the amount of placement, and the type of garbage;

[0066] S2. Obtain the garbage collection category corresponding to each garbage bin, and filter out the garbage placement information of different categories in each garbage bin according to the garbage collection category, and record it as the benchmark feature information;

[0067] S3, obtaining feature extraction conditions, and extracting features from the reference feature information according to the feature extraction conditions, and recording the result of the feature extraction as a key feature;

[0068] S4. Evaluate the garbage placement situation of each garbage bin based on key features and output the rationality of the garbage placement behavior of each garbage bin;

[0069] S5. When the garbage disposal behavior is reasonable, the current garbage disposal behavior is recorded as a normal mode and stored as historical data;

[0070] S6. When the placement of trash cans is unreasonable, an alarm prompt is triggered, and the unreasonable behavior is output simultaneously, and then optimization suggestions are matched according to the unreasonable behavior.

[0071] As described in the above steps S1-S6, as urban garbage management becomes increasingly strict, classified garbage disposal has become a key link in improving the city's image and protecting the environment. In order to further ensure the accuracy and rationality of classified garbage disposal, this embodiment provides a garbage classification management method for supervising the garbage disposal process. In this embodiment, it is first necessary to collect garbage disposal information in each garbage bin. The garbage disposal information includes the garbage disposal time, the amount of garbage disposed, and the type of garbage, etc., to provide pre-data support for subsequent garbage classification and processing, and then determine the garbage collection category corresponding to each garbage bin. On this basis, the garbage disposal information of different categories in each garbage bin is screened according to the garbage collection category and recorded as benchmark feature information. Subsequently, feature extraction conditions are obtained, and the benchmark feature information is characterized according to these conditions. Extraction is performed to output key features. After the key features are extracted, the garbage placement situation of each garbage bin will be evaluated based on these key features. The evaluation result will output the rationality of the garbage placement behavior of each garbage bin. When the garbage placement behavior of the garbage bin is evaluated as reasonable, the current garbage placement behavior will be recorded as a normal mode and stored as historical data. On the contrary, when the garbage placement behavior of the garbage bin is evaluated as unreasonable, an alarm will be triggered and the unreasonable behavior will be output simultaneously. On this basis, optimization suggestions will be matched according to the unreasonable behavior to avoid excessive mixing of different types of garbage, so as to improve the effect of garbage classification. In addition, relevant display equipment can be added at the garbage placement point to display the unreasonable garbage placement, so that users can understand the correct way to place garbage, and gradually improve the efficiency and accuracy of garbage classification.

[0072] In a preferred embodiment, the steps of obtaining the garbage collection categories corresponding to each garbage bin, filtering out garbage placement information of different categories in each garbage bin according to the garbage collection categories, and recording as the benchmark feature information include:

[0073] S201, obtaining identification information of each trash can, and determining the garbage collection category of each trash can according to the identification information of the trash can;

[0074] S202, filtering and processing the garbage placement information in each garbage bin according to the garbage collection category to obtain garbage placement information of multiple categories;

[0075] S203: Classify the garbage placement information belonging to the same garbage collection category in each garbage bin, and record the classification results as benchmark feature information.

[0076] As described in the above steps S201-S203, in order to identify and record the classification of garbage in different garbage bins, corresponding labels will be added to each garbage bin. This process first requires us to identify the unique identification of each garbage bin. These labels help to determine the garbage collection category corresponding to each garbage bin, such as recyclables, hazardous waste, wet garbage (kitchen waste) and dry garbage (other garbage). The label of each garbage bin will indicate which type of garbage it is mainly used to collect, thereby providing clear guidance for garbage disposers. The garbage disposal information in the garbage bin is then screened and processed, that is, different categories of garbage disposal information are extracted from each garbage bin according to the garbage collection category. After screening out different categories of garbage disposal information, the garbage disposal information of each category needs to be further classified, that is, the garbage disposal information of the same category is summarized, and finally the results of these classification processes are recorded as benchmark feature information.

[0077] In a preferred embodiment, the step of obtaining feature extraction conditions, and extracting features from the reference feature information according to the feature extraction conditions to obtain key features includes:

[0078] S301, obtaining preset feature extraction conditions, where the feature extraction conditions include garbage delivery time, garbage delivery amount, and garbage delivery type;

[0079] S302, traversing the reference feature information according to the feature extraction conditions to filter out the types of garbage in each garbage bin;

[0080] If the garbage type is unique and the garbage type is consistent with the garbage collection category of the garbage bin, it means that the garbage is put into the garbage bin normally, and the garbage putting time is recorded as the key feature;

[0081] If the garbage type is not unique, the garbage placement time and garbage amount of each type of garbage in the garbage bin will continue to be collected and summarized as the key feature together with the garbage type.

[0082] As described in the above steps S301-S302, in order to extract useful information from garbage data, it is first necessary to establish feature extraction conditions, and then traverse the baseline feature information according to the feature extraction conditions, that is, check the type of garbage in each trash can one by one. When a unique type of garbage is found in the trash can, it will be compared with the garbage collection category of the trash can. If they are consistent, it means that the garbage placement in this trash can is normal. At this time, the garbage placement time in the trash can will be recorded as the key feature. However, if multiple types of garbage are found in the trash can, then it is necessary to continue to collect the placement time and amount of various types of garbage in this trash can, and summarize them together with the garbage type, so as to obtain the key feature.

[0083] In a preferred embodiment, after the garbage delivery time is collected, multiple parallel time periods are constructed, and historical garbage delivery time nodes that meet the garbage collection category of the garbage bin in each parallel time period are counted, and the number of garbage delivery times at the historical garbage delivery time nodes is recorded as the first condition parameter;

[0084] Obtaining an evaluation threshold, and comparing the first condition parameter with the evaluation threshold;

[0085] If the first condition parameter is less than the evaluation threshold, the historical garbage placement time node corresponding to the first condition parameter is recorded as an occasional placement node, and is simultaneously screened out;

[0086] If the first condition parameter is greater than or equal to the evaluation threshold, the historical garbage disposal time nodes in each parallel period are arranged in chronological order, and the highest ranked historical garbage disposal time node is recorded as the starting node, and the lowest ranked historical garbage disposal time node is recorded as the ending node;

[0087] A standard garbage disposal period is generated based on the starting node and the ending node, and the starting node and the ending node are offset to obtain the garbage cleaning period.

[0088] In this implementation, after completing the collection of garbage delivery time, it is necessary to establish multiple parallel time periods, and the duration of the parallel time period is set to 24 hours. Then, the data in each parallel time period is deeply analyzed to screen out all historical garbage delivery time points that meet the garbage collection category of a specific garbage bin, and the number of garbage delivery at these time points is recorded as the first condition parameter. Then, it is necessary to determine an evaluation threshold, which will be used as a standard to judge the first condition parameter, that is, to compare the collected historical garbage delivery time nodes with this evaluation threshold in detail. If it is found that the number of garbage delivery at a certain historical garbage delivery time node is less than this evaluation threshold, then this garbage delivery node is identified as an occasional delivery node, and it is immediately It will be eliminated. On the contrary, if a historical garbage disposal time node is greater than or equal to this evaluation threshold, then the historical garbage disposal time nodes in each parallel time period need to be sorted in the chronological order of their occurrence. After the sorting is completed, the front historical garbage disposal time node is set as the starting node, and the last node is set as the ending node. The period between the starting node and the ending node is recorded as the standard garbage disposal period. In addition, after the starting node and the ending node are determined, the starting node and the ending node will be offset to obtain the corresponding garbage cleaning period, that is, the cleaning process of the garbage bin will not affect the normal placement of users, ensuring the orderliness of garbage disposal and avoiding conflicts in the garbage disposal process.

[0089] In a preferred embodiment, after the garbage delivery amount is collected, multiple delivery amount intervals are constructed, and the historical garbage delivery amount that meets the garbage collection category of the garbage bin within each delivery amount interval is counted, and the historical garbage delivery amount is recorded as the second condition parameter;

[0090] Obtaining a preset delivery amount threshold, and comparing the second condition parameter with the delivery amount threshold;

[0091] If the second condition parameter is less than or equal to the delivery amount threshold, the historical garbage delivery amount corresponding to the second condition parameter is recorded as normal delivery, and the delivery amount interval corresponding to the second condition parameter is recorded as the normal delivery interval;

[0092] If the second condition parameter is greater than the delivery amount threshold, the historical garbage delivery amount corresponding to the second condition parameter is recorded as high-frequency delivery, and the corresponding delivery amount interval is recorded as the high-frequency delivery interval.

[0093] In this embodiment, after the collection of garbage delivery volume is completed, a plurality of different delivery volume intervals will be established, that is, a plurality of time intervals will be set within the standard garbage delivery period, and the specific intervals can be set according to the actual storage capacity of the garbage bin, and the preferred interval is 1 hour. Then, the historical garbage delivery volume in each interval will be statistically analyzed in detail, and the historical garbage delivery volume that meets the garbage storage category of the garbage bin will be recorded as the second condition parameter, and then a pre-set delivery volume threshold will be obtained. The delivery volume threshold can be set according to previous garbage delivery data and experience, and then the second condition parameter and the delivery volume threshold will be compared. If the value of the second condition parameter is less than or equal to the delivery volume threshold, it indicates that the historical garbage delivery volume is within the normal range, and this will be recorded. The historical garbage delivery volume is recorded as normal delivery, and the corresponding delivery volume interval is also recorded as the normal delivery interval. On the contrary, if the value of the second condition parameter is greater than the delivery volume threshold, this indicates that the historical garbage delivery volume exceeds the normal range and is a high-frequency delivery. In this case, the historical garbage delivery volume will be recorded as a high-frequency delivery, and the corresponding delivery volume interval will be recorded as a high-frequency delivery interval. In the normal delivery interval, when different types of garbage appear in the trash can, the relevant management personnel can clean them up in time, while in the high-frequency delivery interval, different types of garbage in the trash can cannot be effectively cleaned up. Therefore, in the high-frequency delivery interval, relevant personnel need to pay closer attention to the garbage delivery information in the trash can to avoid excessive mixing of different types of garbage.

[0094] In a preferred embodiment, the step of evaluating the garbage placement situation of each garbage bin based on the key features and outputting the rationality of the garbage placement behavior of each garbage bin includes:

[0095] S401, obtaining in real time the types of garbage in each garbage bin that are inconsistent with the garbage collection category, and recording them as the first evaluation parameter;

[0096] S402, obtaining the amount of garbage put in under the first evaluation parameter, and recording it as the second evaluation parameter;

[0097] S403, obtaining the garbage delivery time under the second evaluation parameter, and mapping it to the third evaluation parameter;

[0098] S404, obtaining an evaluation function, and inputting the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter into the evaluation function, and marking the output result of the evaluation function as an evaluation score;

[0099] S405, obtaining an evaluation threshold, and comparing the evaluation threshold with the evaluation score;

[0100] If the evaluation score is greater than or equal to the evaluation threshold, it indicates that the garbage placement in the corresponding garbage bin is unreasonable, and an alarm signal is issued simultaneously;

[0101] If the evaluation score is less than the evaluation threshold, it means that the garbage placement in the corresponding trash can is reasonable.

[0102] As described in the above steps S401-S405, when there are multiple categories of garbage in the garbage bin, a rationality assessment will be performed based on the key features that have been collected, that is, the appearance of a small amount of different categories of garbage will not affect the normal placement of the garbage bin, nor will it increase the difficulty of subsequent garbage classification, so there is no need to arrange relevant management personnel separately for maintenance. In this implementation, the type of garbage that does not meet the garbage classification standard marked on the garbage bin is defined as the first evaluation parameter, and then the garbage in the first evaluation parameter is quantitatively analyzed, and the number of garbage placed is counted, which is recorded as the second evaluation parameter. In addition, the time of garbage placement is also an aspect of the evaluation, because the staggered placement of different categories of garbage will increase the difficulty of subsequent classification. Based on this, in this implementation, the time of garbage placement is mapped to the third evaluation parameter. The mapped value of the garbage placement time under the third evaluation parameter is associated with the placement interval, and the value of the third evaluation parameter in the high-frequency placement interval is greater than the mapped value of the third evaluation parameter in the normal placement interval. This is because in the high-frequency placement interval, when different categories of garbage appear, the difficulty of relevant personnel to intervene is is larger, so in this interval, its mapping value will be increased so as to remind relevant personnel to deal with it in time, and then an evaluation function is obtained. The evaluation function can integrate the first evaluation parameter, the second evaluation parameter and the third evaluation parameter to output an evaluation score, wherein the expression of the evaluation function is: R=aS1+bS2+cS3, wherein R represents the evaluation score, a, b and c represent the weight coefficients of the first evaluation parameter, the second evaluation parameter and the third evaluation parameter respectively, S1, S2 and S3 represent the first evaluation parameter, the second evaluation parameter and the third evaluation parameter respectively, and the evaluation score will directly reflect the rationality of the garbage disposal behavior in the trash can. Then, the evaluation score will be compared with the preset evaluation threshold to determine whether the garbage disposal behavior is reasonable. If the evaluation score reaches or exceeds the threshold, it means that the garbage disposal behavior in the trash can is unreasonable. At this time, the alarm mechanism will be triggered immediately to send an alarm signal to the management personnel so that timely measures can be taken to correct the classification error. On the contrary, if the evaluation score is lower than the threshold, it means that the garbage disposal behavior in the trash can meets the classification requirements and is reasonable, and no management personnel need to intervene.

[0103] In a preferred embodiment, the step of matching optimization suggestions according to unreasonable behaviors includes:

[0104] S601, obtaining the garbage placement time, garbage placement amount, garbage placement type and corresponding garbage bin identification information under unreasonable behavior, and recording it as information to be optimized;

[0105] S602: Obtain preset optimization suggestions, wherein there are multiple optimization suggestions, each of which corresponds to optimization condition information including garbage delivery time, garbage delivery amount, garbage delivery type, and corresponding garbage bin identification information;

[0106] S603: Match the information to be optimized under the unreasonable behavior with the optimization condition information, and output optimization suggestions according to the matching results.

[0107] As described in the above steps S601-S603, in order to ensure the effective implementation of garbage classification and disposal, it is first necessary to collect specific information about the user's unreasonable garbage classification and disposal behavior, such as the garbage disposal time, that is, the time period in which the garbage is usually thrown, the amount of garbage placed, that is, how much garbage is placed each time, the type of garbage placed, that is, the garbage placement category, and the corresponding garbage bin identification information. After unreasonable behavior occurs, the garbage disposal time, garbage disposal amount, garbage disposal type and corresponding garbage bin identification information under the unreasonable behavior will be recorded as information to be optimized, and optimization suggestions will be output based on the specific unreasonable behavior. Each Optimization suggestions are associated with a specific set of optimization condition information. By comparing the information to be optimized with the optimization condition information, the corresponding optimization suggestions can be matched. The optimization suggestions can be: "Please dispose of garbage within the standard delivery time", "Please reduce the amount of single delivery", "Please confirm that the type of garbage is consistent with the garbage can label" or "Wrong garbage delivery, please pay attention to classification", etc., to remind users to pay attention to garbage classification and guide residents to change their irregular garbage classification and delivery habits. When the optimization suggestions are output, relevant personnel will also intervene in the unreasonable delivery of garbage cans to avoid excessive mixing of different types of garbage in the same garbage can.

[0108] See also Figure 2 A garbage classification and delivery management system is applied to the above-mentioned garbage classification and delivery management method, comprising:

[0109] The information collection module is used to collect the garbage placement information in each garbage bin, wherein the garbage placement information includes the placement time, the placement amount and the type of garbage;

[0110] An information screening module is used to obtain the garbage collection category corresponding to each garbage bin, and screen out the garbage placement information of different categories in each garbage bin according to the garbage collection category, and record it as the benchmark feature information;

[0111] A feature extraction module, the feature extraction module is used to obtain feature extraction conditions, and perform feature extraction on the reference feature information according to the feature extraction conditions, and record the result of the feature extraction as a key feature;

[0112] The state output module is used to evaluate the garbage placement status of each garbage bin based on key features and output the rationality of the garbage placement behavior of each garbage bin;

[0113] The information storage module is used to record the current garbage placement behavior as a normal mode and store it as historical data when the garbage placement behavior of the garbage bin is reasonable;

[0114] Optimization module: When the placement behavior of trash cans is unreasonable, the optimization module triggers an alarm prompt, outputs the unreasonable behavior simultaneously, and then matches optimization suggestions based on the unreasonable behavior.

[0115] In the above, the system includes an information collection module, an information screening module, a feature extraction module, a state output module, an information storage module and an optimization module. The information collection module is responsible for real-time monitoring and collecting garbage placement data in various types of garbage bins, including but not limited to placement time, placement amount and type of garbage. The information screening module is used to determine the garbage collection category corresponding to each garbage bin, and screen the different types of garbage placement information in the garbage bin according to the collection category, and record the screened information as benchmark feature information. The function of the feature extraction module is to determine the conditions for feature extraction, and perform in-depth feature extraction on the benchmark feature information based on these conditions. The extracted features will be used as key The characteristics are recorded, and the status output module uses key characteristics to conduct a detailed assessment of the garbage placement situation of each trash can, and then judge whether the garbage placement behavior is reasonable, and output the evaluation results. The information storage module is used to record the garbage placement behavior when the garbage placement behavior of the trash can is judged to be reasonable, classify it as a normal mode, and save it as historical data for future analysis and reference. The optimization module is used to trigger an alarm prompt when it detects that the garbage placement behavior of the trash can is unreasonable, and simultaneously output the specific circumstances of the unreasonable behavior, and provide targeted optimization suggestions based on these circumstances. At the same time, after the alarm signal is issued, the relevant management personnel can also intervene in the classification to improve the effect of garbage classification.

[0116] See also Figure 3 , an electronic device, the electronic device comprising:

[0117] at least one processor;

[0118] and a memory communicatively coupled to the at least one processor;

[0119] Among them, the memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned garbage classification and management method.

[0120] In the above, the processor of the electronic device can be a central processing unit (CPU), a microprocessor (MCU), a digital signal processor (DSP) or other types of processors, which are responsible for executing computer program instructions to realize the various functions of the garbage classification and management method.

[0121] Memory is a storage unit in electronic devices that is used to store computer programs and various types of data generated when executing programs. Memory can be random access memory (RAM), read-only memory (ROM), flash memory, hard disk (HDD) or solid-state drive (SSD), etc., or a combination of these memories.

[0122] In addition, the electronic device may also include input and output devices, such as a touch screen, keyboard, mouse, display, etc., for receiving user input instructions and displaying processing results. At the same time, the electronic device may also include a network interface for connecting to the network to achieve remote data exchange and remote control.

[0123] And, the arithmetic unit can provide basic mathematical and logical operations for electronic devices, and assist the processor in completing computing tasks, such as processing calculations related to the amount of garbage put in, the time of putting in, the type of garbage, etc.

[0124] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0125] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A garbage classification management method, characterized by: include: Collecting garbage placement information in each garbage bin, wherein the garbage placement information includes placement time, placement amount, and garbage type; Obtaining the garbage collection category corresponding to each of the garbage bins, and filtering out garbage placement information of different categories in each of the garbage bins according to the garbage collection category, and recording it as benchmark feature information; Acquire feature extraction conditions, perform feature extraction on the reference feature information according to the feature extraction conditions, and record the result of the feature extraction as a key feature; Evaluate the garbage placement situation of each garbage bin based on the key features, and output the rationality of the garbage placement behavior of each garbage bin; When the garbage disposal behavior of the garbage bin is reasonable, the current garbage disposal behavior is recorded as a normal mode and stored as historical data; When the placement behavior of the trash can is unreasonable, an alarm prompt is triggered, and the unreasonable behavior is output synchronously, and then optimization suggestions are matched according to the unreasonable behavior; The step of obtaining the feature extraction conditions and extracting the reference feature information according to the feature extraction conditions to obtain the key features includes: Acquire preset feature extraction conditions, wherein the feature extraction conditions include garbage delivery time, garbage delivery amount, and garbage delivery type; According to the feature extraction condition, the reference feature information is traversed and processed to screen out the types of garbage in each of the garbage bins; If the garbage type is unique and the garbage type is consistent with the garbage collection category of the garbage bin, it means that the garbage is put into the garbage bin normally, and the garbage putting time is recorded as the key feature; If the type of garbage is not unique, the garbage placement time and garbage placement amount of each type of garbage in the garbage bin will continue to be collected and summarized as the key feature together with the garbage type.

2. A garbage classification management method according to claim 1, characterized in that: The step of obtaining the garbage collection category corresponding to each of the garbage bins, filtering out garbage placement information of different categories in each of the garbage bins according to the garbage collection category, and recording as the benchmark feature information includes: Obtaining identification information of each of the trash cans, and determining the garbage collection category of each trash can according to the identification information of the trash can; According to the garbage collection category, the garbage placement information in each of the garbage bins is screened and processed to obtain garbage placement information of multiple categories; The garbage placement information belonging to the same garbage collection category in each of the garbage bins is classified and processed respectively, and the classification results are recorded as the benchmark feature information.

3. A garbage classification management method according to claim 1, characterized in that: After the garbage delivery time is collected, multiple parallel time periods are constructed, and historical garbage delivery time nodes that meet the garbage collection category of the garbage bin in each parallel time period are counted, and the number of garbage delivery times at the historical garbage delivery time nodes is recorded as the first condition parameter; Obtaining an evaluation threshold, and comparing the first condition parameter with the evaluation threshold; If the first condition parameter is less than the evaluation threshold, the historical garbage placement time node corresponding to the first condition parameter is recorded as an occasional placement node, and is simultaneously screened out; If the first condition parameter is greater than or equal to the evaluation threshold, the historical garbage disposal time nodes in each of the parallel time periods are arranged in chronological order, and the historical garbage disposal time node with the highest ranking is recorded as the starting node, and the historical garbage disposal time node with the lowest ranking is recorded as the ending node; A standard garbage disposal period is generated according to the starting node and the ending node, and the starting node and the ending node are offset to obtain a garbage cleaning period.

4. A garbage classification management method according to claim 1, characterized in that: After the garbage delivery amount is collected, multiple delivery amount intervals are constructed, and historical garbage delivery amounts that meet the garbage collection category of the garbage bin within each delivery amount interval are counted, and the historical garbage delivery amounts are recorded as the second condition parameter; Obtaining a preset delivery amount threshold, and comparing the second condition parameter with the delivery amount threshold; If the second condition parameter is less than or equal to the delivery amount threshold, the historical garbage delivery amount corresponding to the second condition parameter is recorded as normal delivery, and the delivery amount interval corresponding to the second condition parameter is recorded as the normal delivery interval; If the second condition parameter is greater than the delivery amount threshold, the historical garbage delivery amount corresponding to the second condition parameter is recorded as high-frequency delivery, and the corresponding delivery amount interval is recorded as a high-frequency delivery interval.

5. A garbage classification management method according to claim 4, characterized in that: The step of evaluating the garbage placement situation of each garbage bin based on the key features and outputting the rationality of the garbage placement behavior of each garbage bin includes: Obtain in real time the types of garbage in each garbage bin that are inconsistent with the garbage collection category, and record them as the first evaluation parameter; Obtaining the amount of garbage put in under the first evaluation parameter and recording it as the second evaluation parameter; Obtaining the garbage delivery time under the second evaluation parameter and mapping it to a third evaluation parameter; Obtain an evaluation function, and input the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter into the evaluation function together, and calibrate the output result of the evaluation function as an evaluation score, wherein the expression of the evaluation function is: R=aS1+bS2+cS3, wherein R represents the evaluation score, a, b, and c represent the weight coefficients of the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter, respectively, and S1, S2, and S3 represent the first evaluation parameter, the second evaluation parameter, and the third evaluation parameter, respectively; Obtaining an evaluation threshold, and comparing the evaluation threshold with the evaluation score; If the evaluation score is greater than or equal to the evaluation threshold, it indicates that the garbage placement in the corresponding garbage bin is unreasonable, and an alarm signal is issued simultaneously; If the evaluation score is less than the evaluation threshold, it indicates that the garbage placement in the corresponding garbage bin is reasonable.

6. A garbage classification management method according to claim 5, characterized in that: The value of the garbage delivery time mapped under the third evaluation parameter is associated with the delivery interval; Among them, the value of the third evaluation parameter in the high-frequency delivery interval is greater than the mapped value of the third evaluation parameter in the normal delivery interval.

7. A garbage classification management method according to claim 1, characterized in that: The step of matching optimization suggestions according to the unreasonable behavior includes: Obtain the garbage delivery time, garbage delivery amount, garbage delivery type and corresponding garbage bin identification information under the unreasonable behavior, and record it as information to be optimized; Obtaining preset optimization suggestions, wherein there are multiple optimization suggestions, each of which corresponds to an optimization condition information including garbage delivery time, garbage delivery amount, garbage delivery type and corresponding garbage bin identification information; The information to be optimized under the unreasonable behavior is matched with the optimization condition information, and optimization suggestions are output according to the matching results.

8. A garbage classification and delivery management system, applied to the garbage classification and delivery management method according to any one of claims 1 to 7, characterized in that: include: An information collection module, the information collection module is used to collect garbage placement information in each garbage bin, wherein the garbage placement information includes placement time, placement amount and garbage type; An information screening module, the information screening module is used to obtain the garbage collection category corresponding to each of the garbage bins, and screen out the garbage placement information of different categories in each garbage bin according to the garbage collection category, and record it as the benchmark feature information; A feature extraction module, the feature extraction module is used to obtain feature extraction conditions, and perform feature extraction on the reference feature information according to the feature extraction conditions, and record the result of the feature extraction as a key feature; A status output module, the status output module is used to evaluate the garbage placement status of each garbage bin according to the key features, and output the rationality of the garbage placement behavior of each garbage bin; An information storage module, wherein when the garbage disposal behavior of the garbage bin is reasonable, the information storage module is used to record the current garbage disposal behavior as a normal mode and store it as historical data; The optimization module is used to trigger an alarm prompt when the placement behavior of the trash can is unreasonable, and simultaneously output the unreasonable behavior, and then match the optimization suggestions according to the unreasonable behavior.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the garbage classification and management method as described in any one of claims 1 to 7.

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