Garbage dumping request processing method, device and equipment and computer readable medium

Abstract

The embodiment of the invention discloses a garbage dumping request processing method, device and equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: in response to a received garbage disposal request sent by a target client, analyzing the garbage disposal request to obtain request information; carrying out decryption processing on the encrypted privacy information to obtain user privacy information; in response to determining that the user account is not in the account blacklist, executing the following equipment control steps: identifying at least one to-be-classified junk image to generate junk classification information; in response to the received garbage dumping request, determining identification information of each target garbage can, and opening a can cover of each target classification garbage can; and in response to determining that the target infrared rays are not shielded within the preset duration, closing a can cover of each target classification garbage can. According to the embodiment, the garbage throwing accuracy of the user can be improved, and wrong garbage throwing is reduced.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, apparatus, device, and computer-readable medium for processing a garbage dumping request. Background Art

[0002] Smart garbage disposal stations are smart sites used to classify and temporarily store residents' domestic garbage for subsequent disposal. At present, when users classify and dump garbage, they usually do so in the following way: users classify the garbage on their own and dump it into the corresponding classified garbage bins.

[0003] However, the inventors have found that when the above method is used for garbage dumping, the following technical problems often occur:

[0004] First, users classify their garbage independently, but the accuracy of classification is low, which causes the garbage to be mistakenly thrown into garbage classification bins of incompatible types.

[0005] Second, when users obtain garbage classification information but still incorrectly classify the garbage and dump it, there is no way to trace the user and deal with the incorrectly classified garbage in a timely manner.

[0006] Third, when users fail to completely dump their garbage into the disposal station, it is impossible to trace the user and dispose of the remaining garbage in a timely manner.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0008] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0009] Some embodiments of the present disclosure propose methods, devices, equipment and computer-readable media for identifying garbage dumping behavior to solve one or more of the technical problems mentioned in the above background technology section.

[0010] In a first aspect, some embodiments of the present disclosure provide a method for processing a garbage dumping request, the method comprising: in response to receiving a garbage disposal request sent by a target client, parsing the garbage disposal request to obtain request information, wherein the request information includes encrypted privacy information and at least one image of garbage to be classified; decrypting the encrypted privacy information to obtain user privacy information, wherein the user privacy information includes a user account, user virtual value information and identification information of an intelligent garbage disposal station, the target client is a client that has logged in to the user account by scanning a login QR code displayed by the target intelligent garbage disposal station, and the target intelligent garbage disposal station is an intelligent garbage disposal station corresponding to the identification information of the intelligent garbage disposal station. Garbage disposal station; in response to determining that the above-mentioned user account is not in the account blacklist, executing the following device control steps: according to the pre-trained garbage recognition model, identifying and processing the above-mentioned at least one garbage image to be classified to generate garbage classification information; in response to receiving the garbage dumping request sent by the target client, determining the identification information of each target garbage bin according to the above-mentioned garbage classification information, and opening the lid of each target classified garbage bin, wherein the above-mentioned each target classified garbage bin is the classified garbage bin corresponding to the identification information of each target garbage bin; in response to determining that the target infrared ray is not blocked within a preset time length, closing the lid of each target classified garbage bin, wherein the above-mentioned target infrared ray is located above each classified garbage bin and parallel to the ground.

[0011] In a second aspect, some embodiments of the present disclosure provide a garbage dumping request processing device, the device comprising: a parsing unit, configured to parse the garbage disposal request in response to receiving the garbage disposal request sent by a target client, and obtain request information, wherein the request information includes encrypted privacy information and at least one image of garbage to be classified; a decryption unit, configured to decrypt the encrypted privacy information and obtain user privacy information, wherein the user privacy information includes a user account, user virtual value information and smart garbage disposal station identification information, the target client is a client that has logged in to the user account by scanning the login QR code displayed by the target smart garbage disposal station, and the target smart garbage disposal station corresponds to the smart garbage disposal station identification information. An intelligent garbage disposal station; a control unit, configured to, in response to determining that the above-mentioned user account is not in the account blacklist, execute the following device control steps: according to a pre-trained garbage recognition model, identify and process the above-mentioned at least one garbage image to be classified to generate garbage classification information; in response to receiving a garbage dumping request sent by a target client, determine the identification information of each target garbage bin according to the above-mentioned garbage classification information, and open the lid of each target classified garbage bin, wherein the above-mentioned each target classified garbage bin is the classified garbage bin corresponding to the identification information of each target garbage bin; in response to determining that the target infrared ray is not blocked within a preset time length, close the lid of each target classified garbage bin, wherein the above-mentioned target infrared ray is located above each classified garbage bin and parallel to the ground.

[0012] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0013] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0014] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the garbage dumping request processing method of some embodiments of the present disclosure can improve the accuracy of user garbage placement and reduce the wrong placement of garbage. Specifically, the reason for the low accuracy of user garbage placement and the wrong placement of garbage is that the user independently classifies the garbage, and the accuracy of garbage classification is low, which causes the garbage to be mistakenly placed in the garbage classification bin of mismatched type. Based on this, the garbage dumping request processing method of some embodiments of the present disclosure, first, in response to receiving the garbage disposal request sent by the target client, the above-mentioned garbage disposal request is parsed to obtain request information. Among them, the above-mentioned request information includes encrypted private information and at least one image of garbage to be classified. In this way, the necessary user information and garbage image can be obtained, and since sensitive information such as user account is encrypted for transmission, the security of user information is improved. Then, the above-mentioned encrypted private information is decrypted to obtain user privacy information. Among them, the above-mentioned user privacy information includes user account, user virtual value information and intelligent garbage placement station identification information. The above-mentioned target client is a client that logs in to the above-mentioned user account by scanning the login QR code displayed by the target intelligent garbage placement station. The target intelligent garbage delivery station is an intelligent garbage delivery station corresponding to the identification information of the intelligent garbage delivery station. Thus, the user account can be obtained and the user identity can be verified through decryption processing, and the intelligent garbage delivery station to be delivered by the user can be determined. Afterwards, in response to determining that the user account is not in the account blacklist, the following device control steps are performed: the first step is to identify and process the at least one garbage image to be classified according to the pre-trained garbage recognition model to obtain garbage classification information. Thus, the garbage type can be directly identified and classified by the pre-trained garbage recognition model, thereby avoiding the user's subjective judgment error in garbage classification. The second step is to respond to the garbage dumping request sent by the target client, determine the identification information of each target garbage bin according to the garbage recognition information, and open the lid of each target classified garbage bin. Among them, the above-mentioned each target classified garbage bin is each classified garbage bin corresponding to the identification information of each target garbage bin. Thus, when the user wants to dump garbage, the lid of each garbage bin corresponding to the identified garbage type is automatically controlled to be opened, further improving the accuracy of the user's garbage delivery and reducing user error operations. The third step is to close the lids of the target classified trash cans in response to determining that the target infrared ray is not blocked within a preset time. The target infrared ray is located above the classified trash cans and is level with the ground. Therefore, when the target infrared ray is not blocked within a preset time, it indicates that the user's garbage dumping behavior has ended, and the lids of the trash cans can be automatically controlled to close.Therefore, by introducing a garbage recognition model to accurately identify garbage and automatically control the opening or closing of each garbage can lid corresponding to the identified garbage type, the accuracy of user garbage placement can be improved and the wrong garbage placement can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0016] Figure 1 is a flow chart of some embodiments of a method for processing a garbage dumping request according to the present disclosure;

[0017] Figure 2 is a schematic diagram of the structure of some embodiments of the garbage dumping request processing device according to the present disclosure;

[0018] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0020] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0021] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0022] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0023] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0024] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0025] Figure 1 The process 100 of some embodiments of the method for processing a garbage dumping request according to the present disclosure is shown. The method for processing a garbage dumping request comprises the following steps:

[0026] Step 101: In response to receiving a garbage processing request sent by a target client, the garbage processing request is parsed to obtain request information.

[0027] In some embodiments, the execution subject (e.g., a computing device) of the garbage dumping request processing method may parse the garbage disposal request in response to receiving the garbage disposal request sent by the target client via a wired connection or a wireless connection to obtain request information. The execution subject may be a computing device in an intelligent garbage disposal station. The request information includes encrypted private information and at least one image of garbage to be classified. The encrypted private information may be information encrypted from user-related information. The at least one image of garbage to be classified may be an image obtained by a user of the target client taking a photo of the garbage to be disposed of. In practice, the execution subject may parse the garbage disposal request by extracting Json attributes to obtain request information.

[0028] It should be noted that the above-mentioned computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here. It should be understood that the number of computing devices can be any number according to the implementation needs.

[0029] It should be noted that the above-mentioned wireless connection methods may include but are not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or developed in the future.

[0030] It should be noted that the above-mentioned computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here. It should be understood that the number of computing devices can be any number according to the implementation needs.

[0031] Step 102: decrypt the encrypted private information to obtain the user's private information.

[0032] In some embodiments, the execution subject may decrypt the encrypted private information to obtain user privacy information. The user privacy information includes a user account, user virtual value information, and smart garbage station identification information. The target client is a client that logs in to the user account by scanning the login QR code displayed by the target smart garbage station. The client may be a small program that interacts with the target smart garbage station. The target smart garbage station is a smart garbage station corresponding to the smart garbage station identification information. The user virtual value information may be information representing the value of the user account (user account points). The smart garbage station identification information may be a string representing the smart garbage station. As an example, the smart garbage station identification information may be the device number of the corresponding smart garbage delivery. In practice, the execution subject may decrypt the encrypted private information by a preset decryption algorithm and a decryption key pre-agreed with the target client to obtain user privacy information. As an example, the preset decryption algorithm may be an AES encryption algorithm.

[0033] It should be noted that the execution subject and the target client pre-agree on a pair of keys. The target client stores the encryption key used for encryption. The execution subject stores the decryption key used for decryption.

[0034] Step 103, in response to determining that the user account is not in the account blacklist, executing the following device control steps:

[0035] Step 1031 , based on the pre-trained garbage recognition model, at least one garbage image to be classified is recognized and processed to obtain garbage classification information.

[0036] In some embodiments, the execution subject may identify and process the at least one garbage image to be classified according to a pre-trained garbage recognition model to obtain garbage classification information. The account blacklist may be composed of user accounts that have abnormal dumping behaviors more than a preset number of times or have illegal dumping behaviors. The abnormal dumping behaviors include but are not limited to: incomplete dumping of garbage (residual garbage near the delivery station) and incorrect dumping classification. The illegal dumping behaviors may be but are not limited to: picking up garbage from an open classified garbage bin.

[0037] In some optional implementations of some embodiments, the execution subject may perform recognition processing on the at least one garbage image to be classified according to a pre-trained garbage recognition model to generate garbage classification information through the following steps:

[0038] In the first step, for each of the at least one garbage image to be classified, the following identification steps are performed:

[0039] The first sub-step is to perform image preprocessing on the junk image to be classified to obtain the processed junk image to be classified. The image preprocessing may include image normalization processing and image denoising processing. As an example, the image normalization processing may be mean normalization processing. The image denoising processing may be median filtering processing.

[0040] In the second sub-step, the processed garbage image to be classified is input into the garbage recognition model to obtain garbage recognition information. The garbage recognition model may be a neural network model that takes the image as input and the garbage recognition information as output. The garbage recognition information may be the type of garbage. For example, the garbage recognition information may be "books", "fruit peels", or "batteries". As an example, the garbage recognition model may be a YOLO series model.

[0041] The second step is to input each obtained garbage identification information into a pre-built garbage classification model to obtain garbage classification information. The above-mentioned garbage classification information may include various garbage classification types. The above-mentioned garbage classification types may include recyclable garbage types, kitchen waste types, hazardous waste types and other garbage types. The above-mentioned garbage classification model may be a comparison table model for classifying garbage identification information. The above-mentioned garbage classification model will compare each input garbage identification information with each garbage type in the pre-built garbage type comparison table one by one, and then output the garbage classification type corresponding to the garbage type that is the same as the input garbage identification information. Each garbage type in the above-mentioned garbage type comparison table corresponds to a preset garbage classification type. In practice, the above-mentioned executor can input each obtained garbage identification information into the pre-built garbage classification model one by one to obtain each garbage classification type. Then, the obtained garbage classification types are deduplicated as garbage classification information.

[0042] Step 1032, in response to receiving the garbage dumping request sent by the target client, determining the identification information of each target garbage bin according to the garbage classification information, and opening the lid of each target classification garbage bin.

[0043] In some embodiments, the execution subject may determine the identification information of each target trash can according to the garbage classification information in response to receiving the garbage dumping request sent by the target client, and open the lid of each target classified trash can. Among them, the classified trash cans included in the target intelligent garbage delivery station correspond to the garbage classification type and the trash can identification information. The trash can identification information may be the device number representing the corresponding classified trash can. The target classified trash cans are the classified trash cans corresponding to the target trash can identification information. The garbage dumping request may be a request from the user of the target client to open the lid of the classified trash can. In practice, in response to receiving the garbage dumping request sent by the target client, the execution subject may determine the identification information of each trash can whose corresponding garbage classification type is the same as the garbage classification type included in the garbage classification information as the target trash can identification information, and control the opening of the lid of each target classified trash can represented by the target trash can identification information.

[0044] Optionally, after opening the lids of the target classified trash cans, the method may further include the following steps:

[0045] In the first step, the external camera of the above-mentioned target intelligent garbage disposal station is used to collect the user's real-time video as the video to be identified. The start time of the above-mentioned video to be identified is the same as the time when the lid of each of the above-mentioned target classified garbage bins is opened, and the end time of the above-mentioned video to be identified is the same as the time when the lid of each of the above-mentioned target classified garbage bins is closed.

[0046] The second step is to parse the video to be identified to obtain a sequence of video frames to be identified. The video frames to be identified in the sequence of video frames to be identified correspond to bit sequence information. In practice, the execution subject can parse the video to be identified frame by frame through a video encoder (such as an FFmpeg encoder) to obtain a sequence of video frames to be identified.

[0047] The third step is to perform behavior recognition processing on the video frame sequence to be identified through a pre-trained garbage dumping behavior recognition model to obtain a dumping behavior recognition result. The garbage dumping behavior recognition model can be a neural network model that takes the video frame sequence as input and the dumping behavior recognition result as output. The dumping behavior recognition result includes the start time of the dumping behavior, the end time of the dumping behavior and the type of dumping behavior. As an example, the garbage dumping behavior recognition model can be a YOLO-8 model. The dumping behavior type can include a normal dumping behavior type and an illegal dumping behavior type.

[0048] In step 4, in response to determining that the dumping behavior identification result indicates illegal dumping behavior, the following exception handling steps are performed:

[0049] The first sub-step is to insert the above user account into the above account blacklist to update the account blacklist.

[0050] The second sub-step is to send a reminder message of illegal dumping to the target client. For example, the reminder message of abnormal dumping can be "You have illegal dumping at [dumping start time], and your account has been blacklisted!"

[0051] Optionally, before determining the identification information of each target trash can and opening the lid of each target classified trash can, the method may further include the following steps:

[0052] Control each target scale in the target intelligent garbage delivery station to weigh each classified garbage bin included in the target intelligent garbage delivery station, and obtain the weight of each garbage bin as the weight of each garbage bin before dumping. Among them, the classified garbage bin included in the target intelligent garbage delivery station corresponds to the garbage classification type and garbage bin identification information. The weight of each garbage bin before dumping corresponds to the identification information of each garbage bin. The target scales are respectively located under each classified garbage bin.

[0053] Step 1033, in response to determining that the target infrared rays are not blocked within a preset time period, closing the lids of each target classified trash can.

[0054] In some embodiments, the execution entity may close the lids of the target classified trash cans in response to determining that the target infrared rays are not blocked within a preset time period. The target infrared rays are located at a preset height above the target classified trash cans and are level with the ground. The target infrared rays may be emitted by an infrared emitting device in the target intelligent garbage disposal station. The target infrared rays are used to determine whether the user is currently dumping garbage. When the target infrared rays are blocked, it can indicate that the user can only dump garbage at present. When the target infrared rays are not blocked within a preset time period, it can indicate that the user has ended the garbage dumping behavior. The preset height may be 10 cm.

[0055] Optionally, after closing the lids of the target classified trash cans, the method may further include the following steps:

[0056] In the first step, in response to determining that the dumping behavior identification result represents normal dumping behavior, the following account information update steps are performed:

[0057] The first sub-step is to control each target scale corresponding to the identification information of each target trash can, and perform secondary weighing on each target classified trash can to obtain the weight of each trash can as the weight of the trash can after dumping.

[0058] In the second sub-step, for each of the above-mentioned weights of the emptied garbage bins, perform the following steps:

[0059] Sub-step 1: determining the difference between the weight of the garbage bin after dumping and the corresponding weight of the garbage bin before dumping as the weight difference.

[0060] Sub-step two, according to the garbage classification type corresponding to the weight of the garbage bin after dumping and the above-mentioned weight difference, determine the virtual value information of the garbage. Among them, the classified garbage bins in the above-mentioned target intelligent garbage delivery station correspond to the garbage bin identification information and the garbage classification type. The above-mentioned garbage classification type corresponds to a preset virtual value ratio. The above-mentioned preset virtual value ratio can be a coefficient used to convert the weight difference of the garbage into the virtual value information of the garbage (user account points). As an example, the preset virtual value ratio corresponding to the "recyclable garbage" type can be 0.1. The preset virtual value ratio corresponding to the "hazardous waste" type can be 0.5. The preset virtual value ratio corresponding to the "kitchen waste" type can be 0.05. The preset virtual value ratio corresponding to the "other garbage" type can be 0.2. In practice, the above-mentioned execution entity can multiply the above-mentioned weight difference and the corresponding preset virtual value ratio to determine the virtual value information of the garbage.

[0061] Sub-step three: adding the junk virtual value information and the user virtual value information to update the user virtual value information.

[0062] Optionally, before closing the lids of the target classified trash cans, the method further includes:

[0063] The first step is to collect the images inside the target classified trash cans as the images inside the trash cans to be identified through the built-in image acquisition devices of the target intelligent garbage disposal station. The built-in image acquisition device included in the target intelligent garbage disposal station is located above the classified trash can. The built-in image acquisition device can be a miniature camera. It should be noted that each of the images inside the trash cans to be identified is collected from different target classified trash cans, and each image acquisition device has corresponding trash can identification information, which indicates the pairing with the corresponding (i.e., directly below) classified trash can and the source of the collected image.

[0064] In the second step, for each of the above barrel images to be identified, perform the following image processing steps:

[0065] In the first sub-step, the image in the bucket to be identified is subjected to quality enhancement processing to obtain an enhanced image in the bucket. In practice, the execution subject may perform quality enhancement processing on the image in the bucket to be identified by using a preset image enhancement algorithm to obtain an enhanced image in the bucket. As an example, the preset image enhancement algorithm may be a global histogram equalization algorithm.

[0066] The second sub-step is to perform image preprocessing on the enhanced image in the barrel to obtain a processed image in the barrel. It should be noted that the implementation steps of "performing image preprocessing on the enhanced image in the barrel to obtain a processed image in the barrel" can refer to the implementation steps of "performing image preprocessing on the garbage image to be classified to obtain a processed garbage image to be classified", which will not be repeated here.

[0067] The third sub-step is to input the processed image in the bin into the garbage recognition model to obtain garbage recognition information in the bin. The garbage recognition information in the bin may be the garbage type of each garbage in the bin corresponding to the processed image in the bin.

[0068] The fourth sub-step is to input the above-mentioned garbage identification information in the bucket into the above-mentioned garbage classification model to generate the garbage classification information in the bucket. In practice, the above-mentioned execution subject can input each garbage type included in the above-mentioned garbage identification information in the bucket into the above-mentioned garbage classification model one by one to obtain the classification type of each garbage in the bucket. Then, the above-mentioned execution subject can deduplicate the classification type of the garbage in the bucket as the classification information of the garbage in the bucket.

[0069] In the third step, in response to determining that at least one of the generated garbage classification information in each bucket does not meet the garbage classification type condition, the following account alarm steps are performed:

[0070] In the first sub-step, in response to determining that the user account is in the account gray list, the user account is deleted from the account gray list, and the user account is inserted into the account black list. The garbage classification type condition may be that the garbage classification type in the bucket included in the garbage classification information in the bucket is different from the garbage classification type corresponding to the corresponding target classification garbage bucket. The account gray list may be composed of user accounts that exist but have committed illegal dumping less than the preset number of times.

[0071] The second sub-step is, in response to determining that the user account is not in the account gray list, inserting the user account into the account gray list.

[0072] The third sub-step is to send a garbage dumping error prompt message to the target client, wherein the garbage dumping error prompt message may be "You have a garbage dumping classification error, please pay attention!".

[0073] The relevant contents of the above-mentioned embodiments serve as an inventive point of the embodiments of the present disclosure, and can solve the second technical problem "When the user obtains the garbage classification information but still misclassifies the garbage and dumps it, it is impossible to trace the user and timely misclassify the garbage". The factors that lead to the inability to deal with the residual garbage in time are often as follows: When the user obtains the garbage classification information but still misclassifies the garbage and dumps it, it is impossible to trace the user and timely misclassify the garbage. In order to achieve this effect, the present disclosure uses a built-in camera device to capture the image inside the classified garbage bin after each user finishes dumping the garbage. Since the garbage just dumped into the garbage bin is often at the top of the bin, by identifying the garbage type and determining the garbage classification of the image inside the bin, it can be determined whether the user has made an erroneous dumping behavior due to misclassification, thereby promptly discovering the user's misclassification behavior and reminding the corresponding user.

[0074] Optionally, the above method may further include the following steps:

[0075] The first step is to generate air quality information through the gas concentration sensor in the above-mentioned target intelligent garbage disposal station. The above-mentioned air quality information includes the concentration of the first type of gas and the concentration of the second type of gas. The above-mentioned gas concentration sensor is located inside the above-mentioned target intelligent garbage disposal station and is in the same space as each classified garbage bin. The above-mentioned gas concentration sensor may include an ammonia concentration sensor and a hydrogen sulfide concentration sensor. The above-mentioned first type of gas concentration may be the concentration of hydrogen sulfide. The second type of gas concentration may be the concentration of ammonia. In practice, the above-mentioned execution entity may control the gas concentration sensor in the above-mentioned target intelligent garbage disposal station to detect the concentration of ammonia and hydrogen sulfide in the air to generate air quality information.

[0076] The second step is to control the first spray device in the target intelligent garbage disposal station to spray the first type of solution in response to determining that the air quality information includes that the concentration of the first type of gas is greater than or equal to the first preset concentration threshold, and the first spray device in the target intelligent garbage disposal station can be controlled to spray the first type of solution to each classified garbage bin below. The first type of solvent can be a 3% hydrogen peroxide solution. In practice, in response to determining that the air quality information includes that the concentration of the first type of gas is greater than or equal to the first preset concentration threshold, the execution entity can control the first spray device in the target intelligent garbage disposal station to spray the first type of solution to each classified garbage bin below.

[0077] The third step is to control the second spray device of the target intelligent garbage disposal station to spray the second solution in response to determining that the air quality information includes that the concentration of the second type of gas is greater than or equal to the second preset concentration threshold, wherein the second type of solution may be a 4% citric acid solution. In practice, in response to determining that the air quality information includes that the concentration of the second type of gas is greater than or equal to the second preset concentration threshold, the execution entity may control the second spray device of the target intelligent garbage disposal station to spray the second type of solution to each classified garbage bin below.

[0078] It should be noted that the first spray device and the second spray device are located above each classified trash can in the target intelligent trash delivery station. Here, the specific setting of the first preset concentration threshold and the second preset concentration threshold is not limited.

[0079] Optionally, the above method may further include the following steps:

[0080] In the first step, the surrounding environment image of the target intelligent garbage disposal station is collected through the external camera of the above-mentioned target intelligent garbage disposal station as the environment image to be detected.

[0081] The second step is to insert the pre-built target area recognition model into the above garbage recognition model, and use the inserted garbage recognition model as the residual garbage recognition model.

[0082] The third step is to perform image preprocessing on the above-mentioned environment image to be detected to obtain a processed environment image. It should be noted that the implementation steps of "performing image preprocessing on the above-mentioned environment image to be detected to obtain a processed environment image" can refer to the implementation steps of "performing image preprocessing on the above-mentioned garbage image to be classified to obtain a processed garbage image to be classified", which will not be repeated here.

[0083] The fourth step is to input the processed environment image into the target area recognition model included in the residual garbage recognition model to obtain the location information of the residual garbage area. Among them, the target distinction recognition model can be used to identify the residual garbage area. The target area recognition model can be a neural network model that takes the processed environment image as input and the location information of the residual garbage area as output. The location information of the residual garbage area can be the image position of the detected residual garbage area in the processed environment image. As an example, the location information of the residual garbage area can be the pixel coordinates of a pair of diagonals corresponding to the residual garbage area. The target area recognition model can be a U-Net model or an R-CNN (Region-Based Convolutional Neural Networks) model.

[0084] Step 5: According to the garbage residue area location information, the processed environment image is cropped to obtain the garbage residue area image. In practice, the execution subject can crop the processed environment image according to the garbage residue area range in the image represented by the garbage residue area location information to obtain the garbage residue area image.

[0085] In the sixth step, the garbage residual area image is input into the garbage recognition model included in the residual garbage recognition model to obtain residual garbage recognition information.

[0086] Step 7: insert the user account into the account blacklist, and send a garbage residue prompt message to the target client. The garbage residue prompt message includes the garbage residue identification information, the garbage residue area image and prompt text. The prompt text may be "There is currently garbage residue, please deal with it in time!".

[0087] The relevant content of the above-mentioned embodiment, as an inventive point of the embodiment of the present disclosure, can solve the third technical problem "When the user has not completely dumped the garbage into the delivery station, it is impossible to trace the user and deal with the residual garbage in time". The factors that lead to the inability to trace the user and deal with the residual garbage in time are often as follows: When the user has not completely dumped the garbage into the delivery station, it is impossible to trace the user and deal with the residual garbage in time. In order to achieve this effect, the present disclosure collects images of the surrounding environment of the smart delivery station after each user finishes the garbage dumping behavior, and identifies the residual area and the type of residual garbage in the image, so that it can timely detect whether the user has residual garbage and prompt and trace the user.

[0088] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the garbage dumping request processing method of some embodiments of the present disclosure can improve the accuracy of user garbage placement and reduce the wrong placement of garbage. Specifically, the reason for the low accuracy of user garbage placement and the wrong placement of garbage is that the user independently classifies the garbage, and the accuracy of garbage classification is low, which causes the garbage to be mistakenly placed in the garbage classification bin of mismatched type. Based on this, the garbage dumping request processing method of some embodiments of the present disclosure, first, in response to receiving the garbage disposal request sent by the target client, the above-mentioned garbage disposal request is parsed to obtain request information. Among them, the above-mentioned request information includes encrypted private information and at least one image of garbage to be classified. In this way, the necessary user information and garbage image can be obtained, and since sensitive information such as user account is encrypted for transmission, the security of user information is improved. Then, the above-mentioned encrypted private information is decrypted to obtain user privacy information. Among them, the above-mentioned user privacy information includes user account, user virtual value information and intelligent garbage placement station identification information. The above-mentioned target client is a client that logs in to the above-mentioned user account by scanning the login QR code displayed by the target intelligent garbage placement station. The target intelligent garbage delivery station is an intelligent garbage delivery station corresponding to the identification information of the intelligent garbage delivery station. Thus, the user account can be obtained and the user identity can be verified through decryption processing, and the intelligent garbage delivery station to be delivered by the user can be determined. Afterwards, in response to determining that the user account is not in the account blacklist, the following device control steps are performed: the first step is to identify and process the at least one garbage image to be classified according to the pre-trained garbage recognition model to obtain garbage classification information. Thus, the garbage type can be directly identified and classified by the pre-trained garbage recognition model, thereby avoiding the user's subjective judgment error in garbage classification. The second step is to respond to the garbage dumping request sent by the target client, determine the identification information of each target garbage bin according to the garbage recognition information, and open the lid of each target classified garbage bin. Among them, the above-mentioned each target classified garbage bin is each classified garbage bin corresponding to the identification information of each target garbage bin. Thus, when the user wants to dump garbage, the lid of each garbage bin corresponding to the identified garbage type is automatically controlled to be opened, further improving the accuracy of the user's garbage delivery and reducing user error operations. The third step is to close the lids of the target classified trash cans in response to determining that the target infrared ray is not blocked within a preset time. The target infrared ray is located above the classified trash cans and is level with the ground. Therefore, when the target infrared ray is not blocked within a preset time, it indicates that the user's garbage dumping behavior has ended, and the lids of the trash cans can be automatically controlled to close.Therefore, by introducing a garbage recognition model to accurately identify garbage and automatically control the opening or closing of each garbage can lid corresponding to the identified garbage type, the accuracy of user garbage placement can be improved and the wrong garbage placement can be reduced.

[0089] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a garbage dumping request processing device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the garbage dumping request processing device can be specifically applied to various electronic devices.

[0090] like Figure 2 As shown, some embodiments of the garbage dumping request processing device 200 include: a parsing unit 201, a decryption unit 202 and a control unit 203. The parsing unit 201 is configured to parse the garbage disposal request in response to receiving the garbage disposal request sent by the target client to obtain request information, wherein the request information includes encrypted privacy information and at least one image of garbage to be classified; the decryption unit 202 is configured to decrypt the encrypted privacy information to obtain user privacy information, wherein the user privacy information includes a user account, user virtual value information and intelligent garbage station identification information, the target client is a client that logs in to the user account by scanning the login QR code displayed by the target intelligent garbage station, and the target intelligent garbage station is an intelligent garbage station corresponding to the identification information of the intelligent garbage station; the control unit 20 3 is configured to, in response to determining that the above-mentioned user account is not in the account blacklist, execute the following device control steps: according to a pre-trained garbage recognition model, identify and process the above-mentioned at least one garbage image to be classified to generate garbage classification information; in response to receiving a garbage dumping request sent by a target client, determine the identification information of each target garbage bin according to the above-mentioned garbage classification information, and open the lid of each target classified garbage bin, wherein the above-mentioned each target classified garbage bin is the classified garbage bin corresponding to the identification information of each target garbage bin; in response to determining that the target infrared ray is not blocked within a preset time length, close the lid of each target classified garbage bin, wherein the above-mentioned target infrared ray is located above each classified garbage bin and parallel to the ground.

[0091] It is understandable that the units recorded in the garbage dumping request processing device 200 are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the garbage dumping request processing device 200 and the units contained therein, and will not be described in detail here.

[0092] Reference below Figure 3, which shows a structural schematic diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0093] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory 302 or a program loaded from a storage device 308 into a random access memory 303. Various programs and data required for the operation of the electronic device 300 are also stored in the random access memory 303. The processing device 301, the read-only memory 302, and the random access memory 303 are connected to each other via a bus 304. An input / output interface 305 is also connected to the bus 304.

[0094] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0095] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the read-only memory 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0096] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0097] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0098] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: in response to receiving a garbage disposal request sent by a target client, parses the above-mentioned garbage disposal request to obtain request information, wherein the above-mentioned request information includes encrypted privacy information and at least one image of garbage to be classified; decrypts the above-mentioned encrypted privacy information to obtain user privacy information, wherein the above-mentioned user privacy information includes user account, user virtual value information and smart garbage disposal station identification information, the above-mentioned target client is a client that has logged in to the above-mentioned user account by scanning the login QR code displayed by the target smart garbage disposal station, and the above-mentioned target smart garbage disposal station is a client that is connected to the above-mentioned smart garbage disposal station identification information. In response to determining that the user account is not in the account blacklist, the following device control steps are executed: according to a pre-trained garbage recognition model, the at least one image of the garbage to be classified is recognized and processed to generate garbage classification information; in response to receiving a garbage dumping request sent by the target client, according to the garbage classification information, the identification information of each target garbage bin is determined, and the lid of each target classified garbage bin is opened, wherein each target classified garbage bin is corresponding to the identification information of each target garbage bin; in response to determining that the target infrared ray is not blocked within a preset time length, the lid of each target classified garbage bin is closed, wherein the target infrared ray is located above each classified garbage bin and parallel to the ground.

[0099] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0100] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0101] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be arranged in a processor, for example, may be described as: a processor includes a parsing unit, a decryption unit and a control unit. The names of these units do not constitute a limitation on the units themselves in some cases, for example, the parsing unit may also be described as "a unit that, in response to receiving a garbage disposal request sent by a target client, parses the above garbage disposal request to obtain request information".

[0102] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0103] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for processing garbage dumping requests, applied to an intelligent garbage delivery station, comprising: In response to receiving a garbage processing request sent by a target client, parsing the garbage processing request to obtain request information, wherein the request information includes the encrypted private information and at least one garbage image to be classified; Decrypting the encrypted private information to obtain user private information, wherein the user private information includes a user account, user virtual value information, and smart garbage disposal station identification information, the target client is a client that has logged into the user account by scanning a login QR code displayed by the target smart garbage disposal station, and the target smart garbage disposal station is a smart garbage disposal station corresponding to the smart garbage disposal station identification information; In response to determining that the user account is not in the account blacklist, performing the following device control steps: According to a pre-trained garbage recognition model, the at least one garbage image to be classified is recognized and processed to generate garbage classification information; In response to receiving a garbage dumping request sent by a target client, determining identification information of each target garbage bin according to the garbage classification information, and opening the lid of each target classification garbage bin, wherein each target classification garbage bin is a classification garbage bin corresponding to the identification information of each target garbage bin; In response to determining that the target infrared ray is not blocked within a preset time period, the lids of the target classified trash cans are closed, wherein the target infrared ray is located above the classified trash cans and parallel to the ground.

2. The method according to claim 1, wherein: The step of performing recognition processing on the at least one to-be-classified garbage image according to the pre-trained garbage recognition model to generate garbage classification information includes: For each of the at least one garbage image to be classified, performing the following identification steps: Performing image preprocessing on the garbage image to be classified to obtain a processed garbage image to be classified; Inputting the processed garbage image to be classified into the garbage identification model to obtain garbage identification information; The obtained garbage identification information is input into a pre-built garbage classification model to obtain garbage classification information.

3. The method according to claim 2, wherein: After opening the lids of the target classified trash cans, the method further includes: Through the external camera of the target intelligent garbage delivery station, the real-time video of the user is collected as the video to be identified, wherein the start time of the video to be identified is the same as the time when the lid of each target classified garbage bin is opened, and the end time of the video to be identified is the same as the time when the lid of each target classified garbage bin is closed; Parsing the video to be identified to obtain a sequence of video frames to be identified, wherein the video frames to be identified in the sequence of video frames to be identified correspond to bit sequence information; Using a pre-trained garbage dumping behavior recognition model, the video frame sequence to be recognized is processed for behavior recognition to obtain a dumping behavior recognition result; In response to determining that the dumping behavior identification result represents illegal dumping behavior, performing the following violation handling steps: Inserting the user account into the account blacklist to update the account blacklist; Send illegal dumping behavior prompt information to the target client.

4. The method according to claim 3, wherein: Before determining the identification information of each target trash can and opening the lid of each target classified trash can, the method further includes: Control each target scale in the target intelligent garbage disposal station, weigh each classified garbage bin included in the target intelligent garbage disposal station, and obtain the weight of each garbage bin as the weight of each garbage bin before dumping, wherein the classified garbage bin included in the target intelligent garbage disposal station corresponds to the garbage classification type and garbage bin identification information, the weight of each garbage bin before dumping corresponds to the identification information of each garbage bin, and the each target scale is respectively located under each classified garbage bin.

5. The method according to claim 4, wherein: After closing the lids of the target classified trash cans, the method further includes: In response to determining that the dumping behavior identification result represents normal dumping behavior, performing the following account information updating steps: Controlling each target floor scale corresponding to the identification information of each target trash can, and performing secondary weighing on each target classified trash can to obtain the weight of each trash can as the weight of the trash can after dumping; For each of the dumped trash can weights, perform the following steps: Determine the difference between the weight of the garbage bin after dumping and the weight of the corresponding garbage bin before dumping as a weight difference; Determine virtual value information of the garbage according to the garbage classification type corresponding to the weight of the garbage bin after dumping and the weight difference; The junk virtual value information and the user virtual value information are added to update the user virtual value information.

6. The method according to claim 1, wherein: The method further comprises: Generate air quality information through a gas concentration sensor in the target intelligent garbage delivery station, wherein the air quality information includes a first type of gas concentration and a second type of gas concentration; In response to determining that the air quality information includes that the concentration of the first type of gas is greater than or equal to a first preset concentration threshold, controlling a first spraying device in the target intelligent garbage delivery station to spray a first type of solution; In response to determining that the air quality information includes that the concentration of the second type of gas is greater than or equal to a first preset concentration threshold, a second spraying device in the target intelligent garbage disposal station is controlled to spray the second type of solution.

7. A garbage dumping request processing device, comprising: A parsing unit, configured to, in response to receiving a garbage processing request sent by a target client, parse the garbage processing request to obtain request information, wherein the request information includes the encrypted private information and at least one garbage image to be classified; a decryption unit configured to decrypt the encrypted private information to obtain user private information, wherein the user private information includes a user account, user virtual value information, and smart garbage disposal station identification information, the target client is a client that has logged into the user account by scanning a login QR code displayed by a target smart garbage disposal station, and the target smart garbage disposal station is a smart garbage disposal station corresponding to the smart garbage disposal station identification information; The control unit is configured to, in response to determining that the user account is not in the account blacklist, execute the following device control steps: identify and process the at least one garbage image to be classified according to a pre-trained garbage recognition model to generate garbage classification information; in response to receiving a garbage dumping request sent by a target client, determine the identification information of each target garbage bin according to the garbage classification information, and open the lid of each target classified garbage bin, wherein the each target classified garbage bin is the classified garbage bin corresponding to the identification information of each target garbage bin; in response to determining that the target infrared ray is not blocked within a preset time length, close the lid of each target classified garbage bin, wherein the target infrared ray is located above each classified garbage bin and parallel to the ground.

8. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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