Kitchen garbage classification system based on Internet of Things

Abstract

The invention relates to the field of garbage classification, and discloses a kitchen garbage classification system based on Internet of Things, which comprises a garbage classification module, a garbage collection module and a monitoring alarm module. The kitchen garbage classification module is arranged on the conveying belt and used for recognizing the types of garbage. The kitchen garbage classification device is used for classifying kitchen garbage after the garbage is identified as the kitchen garbage. And the kitchen garbage can is used for throwing kitchen garbage into the garbage can after classification is completed. The kitchen garbage collection module is arranged in a garbage treatment plant and used for detecting the volume of kitchen garbage in a garbage can. The system is used for calling the transport vehicle to collect kitchen garbage. And the monitoring alarm module is arranged in a garbage treatment plant and is used for monitoring the throwing condition of the kitchen garbage. By arranging the garbage classification module, the garbage collection module and the monitoring alarm module, after the kitchen garbage is recognized, the kitchen garbage is classified, the kitchen garbage is collected when the size of the kitchen garbage is too large, and the alarm is given out when the kitchen garbage is put wrongly.

Description

Technical Field

[0001] The present invention relates to the field of garbage classification, and in particular to a kitchen waste classification system based on the Internet of Things. Background Art

[0002] With the enhancement of global environmental awareness, reducing garbage pollution and promoting resource recycling has become a social consensus. However, in actual operation, due to unclear classification standards and irregular disposal behaviors, the effect of garbage classification is not ideal. Traditional garbage disposal methods often have problems such as insufficient processing capacity and low efficiency, resulting in frequent problems such as garbage accumulation and environmental pollution. Especially in the treatment of kitchen waste, due to the high water content and easy decay of kitchen waste, it is more difficult to handle. Traditional garbage classification methods often rely on manual judgment, which is inaccurate and inefficient, and often rely on manual inspections and judgment of the fullness of the garbage bin, which is untimely and inaccurate. It is impossible to detect the placement of garbage in real time. Therefore, a kitchen waste classification system based on the Internet of Things is proposed. Summary of the invention

[0003] The present invention aims to solve the technical problem of how to classify kitchen waste after identifying it, collect it when the volume of the kitchen waste is too large, and issue an alarm when the kitchen waste is placed incorrectly, and provide a kitchen waste classification system based on the Internet of Things.

[0004] The technical solution adopted by the present invention to solve its technical problems is: a kitchen waste classification system based on the Internet of Things, including a garbage classification module, a garbage collection module, and a monitoring and alarm module. The kitchen waste classification module is arranged on the conveyor belt, and is used to identify the type of garbage. It is used to classify the kitchen waste after identifying the garbage as kitchen waste. It is used to put the kitchen waste into the garbage can after the classification is completed. The kitchen waste collection module is arranged in the garbage treatment plant, and is used to detect the volume of the kitchen waste inside the garbage can. It is used to call a transport vehicle to collect the kitchen waste. The monitoring and alarm module is arranged in the garbage treatment plant, and is used to monitor the delivery of the kitchen waste. It is used to issue an alarm.

[0005] Through the setting of the garbage classification module, the types of garbage can be automatically identified on the conveyor belt, and kitchen waste can be accurately distinguished. This automatic identification technology not only improves the accuracy of classification, but also greatly reduces the burden of manual classification, making the garbage classification process more efficient and accurate. In the garbage treatment plant, the garbage collection module monitors the volume of kitchen waste in the garbage bin in real time, and calls the transport vehicle to collect the kitchen waste in time, effectively avoiding the environmental pollution caused by kitchen waste, while also optimizing the allocation of transportation resources and reducing unnecessary transportation costs. The monitoring and alarm module continuously monitors the delivery of kitchen waste, and at the same time issues an alarm to remind management personnel to intervene. It not only ensures the accuracy of garbage classification, but also promotes the standardization of garbage classification behavior and enhances the public's environmental awareness and participation.

[0006] Preferably, the kitchen waste classification module includes an odor sensor, a first microprocessor, and a plurality of mechanical arms. The odor sensor is arranged on the conveyor belt, and is used to obtain the characteristic value of the garbage odor feature vector at a certain moment, and send it to the first microprocessor. The first microprocessor is arranged on the conveyor belt, and is connected to the odor sensor for communication, and is used to identify the type of garbage after receiving the characteristic value of the garbage odor feature vector at a certain moment. It is used to calculate the characteristic value of the label classification feature vector at a certain moment by the characteristic value of the garbage odor feature vector at a certain moment, the first weight coefficient, and the second weight coefficient after receiving the characteristic value of the garbage odor feature vector at a certain moment. It is used to identify the garbage as kitchen waste after the characteristic value of the label classification feature vector at a certain moment exceeds the preset odor threshold. It is used to start a plurality of mechanical arms after identifying the garbage as kitchen waste. The plurality of mechanical arms are arranged on the conveyor belt, and are electrically connected to the first microprocessor, and are used to classify the kitchen waste. It is used to put the kitchen waste into the trash can after the classification is completed.

[0007] The odor sensor captures the odor feature vectors of the garbage on the conveyor belt in real time. These feature vectors contain the unique odor information of the garbage, which is convenient for identifying the type of garbage. After receiving the garbage odor feature vector, the first microprocessor not only identifies the type of garbage, but also calculates the label classification feature vector through a specific algorithm to further confirm whether the garbage is kitchen waste. The accuracy of classification is improved and the possibility of misjudgment is reduced. Once the garbage is identified as kitchen waste, several robotic arms are started to automatically perform classification operations, accurately classify the garbage, and accurately put the kitchen waste into the trash can after the classification is completed, which greatly reduces the burden of manual classification and improves the classification efficiency.

[0008] Preferably, the calculation formula of the eigenvalue of the label classification feature vector at a certain moment is:

[0009]

[0010] Among them, Ei ′ is the eigenvalue of the label classification feature vector at the i-th moment, α is the first weight coefficient, which is 0.98, β is the second weight coefficient, which is 0.85, sigmoid is the activation function, E i is the eigenvalue of the garbage odor feature vector at the i-th moment, and e is the base of the natural logarithm.

[0011] Preferably, the plurality of robotic arms are made of carbon fiber material.

[0012] Carbon fiber has high strength, which makes the carbon fiber robotic arm have lower inertia when moving at high speed, improving operation accuracy and response speed.

[0013] Preferably, the kitchen waste collection module includes a laser range finder sensor, an ultrasonic sensor, a timer, and a second microprocessor. The laser range finder sensor is arranged in a garbage disposal plant, and is used to measure the bottom area of ​​the garbage bin and send it to the second microprocessor. The ultrasonic sensor is arranged in a garbage disposal plant, and is used to emit ultrasonic waves vertically to the bottom of the garbage bin, and is used to measure the height of the garbage bin after the ultrasonic wave touches the garbage and forms a reflected wave, and sends it to the second microprocessor. The timer is arranged in the garbage disposal plant, and is used to record the time when the ultrasonic wave is emitted when the ultrasonic sensor emits ultrasonic waves vertically to the bottom of the garbage bin, and send it to the second microprocessor. It is used to record the time when the reflected wave is received after the ultrasonic wave touches the garbage and forms a reflected wave, and send it to the second microprocessor. The second microprocessor is arranged in the garbage disposal plant, and is connected to the laser range finder sensor, the ultrasonic sensor, the kitchen waste classification module, and the communication center of the transport vehicle, and is used to calculate the volume of kitchen waste inside the garbage bin after receiving the bottom area of ​​the garbage bin, the height of the garbage bin, the time when the ultrasonic wave is emitted, and the time when the reflected wave is received, through the bottom area of ​​the garbage bin, the height of the garbage bin, the propagation speed of the ultrasonic wave in the air, the time when the ultrasonic wave is emitted, and the time when the reflected wave is received. After obtaining the volume of kitchen waste and the garbage odor feature vector inside the garbage bin, the kitchen waste odor index is calculated by the eigenvalue of the volume of kitchen waste and the garbage odor feature vector at a certain moment. When the volume of kitchen waste inside the garbage bin exceeds a preset volume threshold and the kitchen waste odor index exceeds a preset kitchen waste odor index threshold, a transport vehicle is called to collect the kitchen waste.

[0014] The laser rangefinder accurately measures the bottom area of ​​the trash can, and the ultrasonic sensor accurately calculates the height of the trash can by emitting ultrasonic waves to the bottom of the trash can and measuring the time difference of the reflected waves, and then calculates the volume of the garbage in combination with the bottom area. This method is not only non-contact and non-destructive, but also has a wide measurement range and high accuracy. The second microprocessor receives data from the laser sensor and the ultrasonic sensor, and calculates the volume of the kitchen waste inside the trash can through a complex algorithm. At the same time, combined with the garbage odor feature vector from the kitchen waste classification module, the kitchen waste odor index is calculated to comprehensively evaluate and judge the freshness and degree of corruption of the garbage. When the volume of the kitchen waste inside the trash can exceeds the preset volume threshold, and the kitchen waste odor index exceeds the preset odor index threshold, the second microprocessor automatically triggers the instruction to call the transport vehicle to ensure that the garbage is collected in a timely and effective manner to prevent environmental pollution.

[0015] Preferably, the calculation formula for the volume of kitchen waste inside the trash can is:

[0016]

[0017] Among them, V is the volume of kitchen waste inside the trash can, in m 3 , S is the bottom area of ​​the trash can, in m 2 , H is the height of the trash can, the unit is m, C is the propagation speed of ultrasound in the air, the unit is m / s, the value of C is 340, T1 is the time when the ultrasound is emitted, the unit is s, and T2 is the time when the reflected wave is received, the unit is s.

[0018] The calculation formula of kitchen waste odor index is:

[0019] J=V*E i

[0020] Among them, J is the kitchen waste odor index.

[0021] Preferably, the laser ranging sensor is a pulsed laser ranging sensor.

[0022] The laser ranging sensor has strong anti-interference ability. In complex environments such as lighting changes and dust interference, the pulsed laser signal can still be transmitted and reflected stably to ensure the accuracy of the ranging results.

[0023] Preferably, the monitoring and alarm module includes an infrared camera, an image processor, a timer, a width gauge, an electronic digital height gauge, a third microprocessor, and an alarm. The infrared camera is set in the garbage treatment plant to monitor the delivery of kitchen waste and send it to the image processor and the third microprocessor. The image processor is set in the garbage treatment plant and is connected to the infrared camera for detecting the total amount of garbage inside the garbage bin after receiving the delivery of kitchen waste and sending it to the third microprocessor. The timer is set in the garbage treatment plant to record the time interval for taking images when the garbage is delivered and send it to the third microprocessor. The width gauge is set in the garbage treatment plant to measure the maximum width value of the garbage piled in the garbage bin within the time interval and send it to the third microprocessor. The electronic digital height gauge is set in the garbage treatment plant to measure the maximum height value of the garbage piled in the garbage bin within the time interval and the maximum height value of the garbage piled in the garbage bin within the time interval of the previous minute and send it to the third microprocessor. The third microprocessor is arranged in the garbage disposal plant, and is communicated with the infrared camera, the image processor, the timer, the width gauge, the electronic digital height gauge, and the kitchen waste collection module. The third microprocessor is arranged in the garbage disposal plant, and is communicated with the infrared camera, the image processor, the timer, the width gauge, the electronic digital height gauge, and the kitchen waste collection module. The third microprocessor is used for receiving the kitchen waste delivery situation, the total amount of garbage inside the garbage bin, the time interval for taking images when the garbage is delivered, the maximum width value of the garbage piled in the garbage bin within the time interval, the maximum height value of the garbage piled in the garbage bin within the time interval, and the maximum height value of the garbage piled in the garbage bin within the time interval of the previous minute, and then calculating the garbage existence status index of the garbage bin and its surrounding area by the total amount of garbage inside the garbage bin, the time interval for taking images when the garbage is delivered, the maximum width value of the garbage piled in the garbage bin within the time interval, the maximum height value of the garbage piled in the garbage bin within the time interval, and the maximum height value of the garbage piled in the garbage bin within the time interval of the previous minute. After receiving the total amount of garbage inside the garbage bin, the maximum width of the garbage piled in the garbage bin within the time interval, the maximum length of the garbage piled in the garbage bin within the time interval, and the bottom area of ​​the garbage bin, the garbage overflow index is calculated by the total amount of garbage inside the garbage bin, the maximum width of the garbage piled in the garbage bin within the time interval, the maximum length of the garbage piled in the garbage bin within the time interval, and the bottom area of ​​the garbage bin. When the garbage existence state index of the garbage bin and its vicinity exceeds a preset placement threshold and the garbage overflow index is greater than a preset garbage overflow index threshold, an alarm is activated. The alarm is set in the garbage treatment plant and is electrically connected to the third microprocessor to issue an alarm.

[0024] The infrared camera monitors the delivery of kitchen waste in the waste treatment plant in real time to ensure comprehensive coverage of waste delivery behavior. The image processor accurately detects the total amount of garbage in the trash bin by processing the images captured by the camera, and the timer records the time interval for taking images when the garbage is delivered to ensure the continuity and timeliness of the data. The width gauge and the electronic digital height gauge respectively measure the maximum width and maximum height of the garbage piled in the trash bin. Combined with the data changes within the time interval, the accumulation state and change trend of the garbage can be accurately reflected. The third microprocessor receives data from various sensors and calculates the garbage existence state index and garbage overflow index of the trash bin and its surrounding area through complex algorithms. These two indexes comprehensively reflect the accuracy of garbage delivery and the full load state of the trash bin. When the garbage existence state index exceeds the preset delivery threshold and the garbage overflow index is greater than the preset garbage overflow index threshold, the system automatically determines that the kitchen waste is delivered incorrectly or the trash bin is about to be full, and triggers an alarm. After receiving the instruction from the third microprocessor, the alarm immediately issues an alarm to remind the management personnel to intervene. This helps to promptly detect and correct garbage disposal errors, prevent environmental pollution and odor problems caused by full garbage bins, reduce the need for manual monitoring, and reduce labor costs. At the same time, it avoids environmental pollution and odor problems caused by full garbage bins and optimizes the efficiency of resource use.

[0025] Preferably, the calculation formula for the garbage existence status index of the garbage bin and its surrounding area is:

[0026]

[0027] Where R is the garbage status index of the trash bin and its surrounding area, n is the total amount of garbage in the trash bin, the unit is pieces, and the value of n is a positive integer between 4 and 10, t is the time interval for taking images of garbage, the unit is min, and its value is a positive integer multiple of 3, d t Indicates the maximum length of the garbage piled in the garbage bin within the time interval t, in meters, w t Indicates the maximum width of the garbage piled in the garbage bin within the time interval t, in meters, hours t Indicates the maximum height of the garbage piled in the garbage bin within the time interval t, in meters, h t-1 It indicates the maximum height of the garbage piled in the garbage bin during the previous one-minute time interval, in meters.

[0028] The calculation formula for the garbage overflow index is:

[0029]

[0030] Where x is the garbage overflow index.

[0031] Preferably, the alarm is an audible and visual alarm.

[0032] The sound and light alarm can quickly attract people's auditory attention by emitting specific sounds, especially in noisy environments. The sound alarm can penetrate the noise and ensure the effectiveness of information transmission. The flashing lights can visually attract people's attention and more clearly indicate the alarm location.

[0033] Beneficial effects of the present invention:

[0034] 1. The present invention automatically identifies the types of garbage on the conveyor belt and accurately distinguishes kitchen waste through the setting of the garbage classification module. This automatic identification technology not only improves the accuracy of classification, but also greatly reduces the burden of manual classification, making the garbage classification process more efficient and accurate. In the garbage treatment plant, the garbage collection module monitors the volume of kitchen waste inside the garbage bin in real time, and calls the transport vehicle to collect the kitchen waste in time, effectively avoiding the environmental pollution caused by kitchen waste, while also optimizing the allocation of transportation resources and reducing unnecessary transportation costs. The monitoring and alarm module continuously monitors the delivery of kitchen waste and issues an alarm to remind management personnel to intervene and deal with it. It not only ensures the accuracy of garbage classification, but also promotes the standardization of garbage classification behavior and enhances the public's environmental awareness and participation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a front view cross-sectional structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0036] The concept and technical effects of the present invention will be clearly and completely described below in conjunction with embodiments to fully understand the purpose, features and effects of the present invention.

[0037] like Figure 1 The technical solution adopted by the present invention to solve its technical problems is: a kitchen waste classification system based on the Internet of Things, including a garbage classification module, a garbage collection module, and a monitoring and alarm module. The kitchen waste classification module is arranged on the conveyor belt, and is used to identify the type of garbage. It is used to classify the kitchen waste after identifying the garbage as kitchen waste. It is used to put the kitchen waste into the trash can after the classification is completed. The kitchen waste collection module is arranged in the garbage treatment plant, and is used to detect the volume of the kitchen waste inside the trash can. It is used to call a transport vehicle to collect the kitchen waste. The monitoring and alarm module is arranged in the garbage treatment plant, and is used to monitor the placement of the kitchen waste. It is used to issue an alarm.

[0038] Through the setting of the garbage classification module, the types of garbage can be automatically identified on the conveyor belt, and kitchen waste can be accurately distinguished. This automatic identification technology not only improves the accuracy of classification, but also greatly reduces the burden of manual classification, making the garbage classification process more efficient and accurate. In the garbage treatment plant, the garbage collection module monitors the volume of kitchen waste in the garbage bin in real time, and calls the transport vehicle to collect the kitchen waste in time, effectively avoiding the environmental pollution caused by kitchen waste, while also optimizing the allocation of transportation resources and reducing unnecessary transportation costs. The monitoring and alarm module continuously monitors the delivery of kitchen waste, and at the same time issues an alarm to remind management personnel to intervene. It not only ensures the accuracy of garbage classification, but also promotes the standardization of garbage classification behavior and enhances the public's environmental awareness and participation.

[0039] The kitchen waste classification module includes an odor sensor, a first microprocessor, and a plurality of mechanical arms. The odor sensor is arranged on the conveyor belt, and is used to obtain the characteristic value of the garbage odor feature vector at a certain moment, and send it to the first microprocessor. The first microprocessor is arranged on the conveyor belt, and is connected to the odor sensor for communication, and is used to identify the type of garbage after receiving the characteristic value of the garbage odor feature vector at a certain moment. It is used to calculate the characteristic value of the label classification feature vector at a certain moment by the characteristic value of the garbage odor feature vector at a certain moment, the first weight coefficient, and the second weight coefficient after receiving the characteristic value of the garbage odor feature vector at a certain moment. It is used to identify the garbage as kitchen waste after the characteristic value of the label classification feature vector at a certain moment exceeds the preset odor threshold. It is used to start a plurality of mechanical arms after identifying the garbage as kitchen waste. The plurality of mechanical arms are arranged on the conveyor belt, and are electrically connected to the first microprocessor, and are used to classify the kitchen waste. It is used to put the kitchen waste into the trash can after the classification is completed.

[0040] The odor sensor captures the odor feature vectors of the garbage on the conveyor belt in real time. These feature vectors contain the unique odor information of the garbage, which is convenient for identifying the type of garbage. After receiving the garbage odor feature vector, the first microprocessor not only identifies the type of garbage, but also calculates the label classification feature vector through a specific algorithm to further confirm whether the garbage is kitchen waste. The accuracy of classification is improved and the possibility of misjudgment is reduced. Once the garbage is identified as kitchen waste, several robotic arms are started to automatically perform classification operations, accurately classify the garbage, and accurately put the kitchen waste into the trash can after the classification is completed, which greatly reduces the burden of manual classification and improves the classification efficiency.

[0041] The calculation formula of the eigenvalue of the label classification feature vector at a certain moment is:

[0042]

[0043] Among them, Ei ′ is the eigenvalue of the label classification feature vector at the i-th moment, α is the first weight coefficient, which is 0.98, β is the second weight coefficient, which is 0.85, sigmoid is the activation function, E i is the eigenvalue of the garbage odor feature vector at the i-th moment, and e is the base of the natural logarithm.

[0044] Some of the robotic arms are made of carbon fiber.

[0045] Carbon fiber has high strength, which makes the carbon fiber robotic arm have lower inertia when moving at high speed, improving operation accuracy and response speed.

[0046] The kitchen waste collection module includes a laser ranging sensor, an ultrasonic sensor, a timer, and a second microprocessor. The laser ranging sensor is set in the garbage treatment plant to measure the bottom area of ​​the garbage can and send it to the second microprocessor. The ultrasonic sensor is set in the garbage treatment plant to vertically transmit ultrasonic waves to the bottom of the garbage can, and to measure the height of the garbage can after the ultrasonic wave touches the garbage and forms a reflected wave, and send it to the second microprocessor. The timer is set in the garbage treatment plant to record the time when the ultrasonic wave is transmitted when the ultrasonic sensor vertically transmits the ultrasonic wave to the bottom of the garbage can and send it to the second microprocessor. It is used to record the time when the reflected wave is received after the ultrasonic wave touches the garbage and forms a reflected wave, and send it to the second microprocessor. The second microprocessor is set in the garbage treatment plant and is connected to the laser ranging sensor, the ultrasonic sensor, the kitchen waste classification module, and the transport vehicle communication center. After receiving the bottom area of ​​the garbage can, the height of the garbage can, the time when the ultrasonic wave is transmitted, and the time when the reflected wave is received, the volume of the kitchen waste inside the garbage can is calculated through the bottom area of ​​the garbage can, the height of the garbage can, the propagation speed of the ultrasonic wave in the air, the time when the ultrasonic wave is transmitted, and the time when the reflected wave is received. After obtaining the volume of kitchen waste and the garbage odor feature vector inside the garbage bin, the kitchen waste odor index is calculated by the eigenvalue of the volume of kitchen waste and the garbage odor feature vector at a certain moment. When the volume of kitchen waste inside the garbage bin exceeds a preset volume threshold and the kitchen waste odor index exceeds a preset kitchen waste odor index threshold, a transport vehicle is called to collect the kitchen waste.

[0047] The laser rangefinder accurately measures the bottom area of ​​the trash can, and the ultrasonic sensor accurately calculates the height of the trash can by emitting ultrasonic waves to the bottom of the trash can and measuring the time difference of the reflected waves, and then calculates the volume of the garbage in combination with the bottom area. This method is not only non-contact and non-destructive, but also has a wide measurement range and high accuracy. The second microprocessor receives data from the laser sensor and the ultrasonic sensor, and calculates the volume of the kitchen waste inside the trash can through a complex algorithm. At the same time, combined with the garbage odor feature vector from the kitchen waste classification module, the kitchen waste odor index is calculated to comprehensively evaluate and judge the freshness and degree of corruption of the garbage. When the volume of the kitchen waste inside the trash can exceeds the preset volume threshold, and the kitchen waste odor index exceeds the preset odor index threshold, the second microprocessor automatically triggers the instruction to call the transport vehicle to ensure that the garbage is collected in a timely and effective manner to prevent environmental pollution.

[0048] The calculation formula for the volume of kitchen waste inside the trash can is:

[0049]

[0050] Among them, V is the volume of kitchen waste inside the trash can, in m 3 , S is the bottom area of ​​the trash can, in m 2 , H is the height of the trash can, the unit is m, C is the propagation speed of ultrasound in the air, the unit is m / s, the value of C is 340, T1 is the time when the ultrasound is emitted, the unit is s, and T2 is the time when the reflected wave is received, the unit is s.

[0051] The calculation formula of kitchen waste odor index is:

[0052] J=V*E i

[0053] Among them, J is the kitchen waste odor index.

[0054] The laser distance sensor is a pulsed laser distance sensor.

[0055] The laser ranging sensor has strong anti-interference ability. In complex environments such as lighting changes and dust interference, the pulsed laser signal can still be transmitted and reflected stably to ensure the accuracy of the ranging results.

[0056] The monitoring and alarm module includes an infrared camera, an image processor, a timer, a width gauge, an electronic digital height gauge, a third microprocessor, and an alarm. The infrared camera is set in the garbage treatment plant to monitor the delivery of kitchen waste and send it to the image processor and the third microprocessor. The image processor is set in the garbage treatment plant and is connected to the infrared camera for detecting the total amount of garbage inside the garbage bin after receiving the delivery of kitchen waste and sending it to the third microprocessor. The timer is set in the garbage treatment plant to record the time interval for taking images when the garbage is delivered and send it to the third microprocessor. The width gauge is set in the garbage treatment plant to measure the maximum width value of the garbage piled in the garbage bin within the time interval and send it to the third microprocessor. The electronic digital height gauge is set in the garbage treatment plant to measure the maximum height value of the garbage piled in the garbage bin within the time interval and the maximum height value of the garbage piled in the garbage bin within the time interval of the previous minute and send it to the third microprocessor. The third microprocessor is arranged in the garbage disposal plant, and is communicated with the infrared camera, the image processor, the timer, the width gauge, the electronic digital height gauge, and the kitchen waste collection module. The third microprocessor is arranged in the garbage disposal plant, and is communicated with the infrared camera, the image processor, the timer, the width gauge, the electronic digital height gauge, and the kitchen waste collection module. The third microprocessor is used for receiving the kitchen waste delivery situation, the total amount of garbage inside the garbage bin, the time interval for taking images when the garbage is delivered, the maximum width value of the garbage piled in the garbage bin within the time interval, the maximum height value of the garbage piled in the garbage bin within the time interval, and the maximum height value of the garbage piled in the garbage bin within the time interval of the previous minute, and then calculating the garbage existence status index of the garbage bin and its surrounding area by the total amount of garbage inside the garbage bin, the time interval for taking images when the garbage is delivered, the maximum width value of the garbage piled in the garbage bin within the time interval, the maximum height value of the garbage piled in the garbage bin within the time interval, and the maximum height value of the garbage piled in the garbage bin within the time interval of the previous minute. After receiving the total amount of garbage inside the garbage bin, the maximum width of the garbage piled in the garbage bin within the time interval, the maximum length of the garbage piled in the garbage bin within the time interval, and the bottom area of ​​the garbage bin, the garbage overflow index is calculated by the total amount of garbage inside the garbage bin, the maximum width of the garbage piled in the garbage bin within the time interval, the maximum length of the garbage piled in the garbage bin within the time interval, and the bottom area of ​​the garbage bin. When the garbage existence state index of the garbage bin and its vicinity exceeds a preset placement threshold and the garbage overflow index is greater than a preset garbage overflow index threshold, an alarm is activated. The alarm is set in the garbage treatment plant and is electrically connected to the third microprocessor to issue an alarm.

[0057] The infrared camera monitors the delivery of kitchen waste in the waste treatment plant in real time to ensure comprehensive coverage of waste delivery behavior. The image processor accurately detects the total amount of garbage in the trash bin by processing the images captured by the camera, and the timer records the time interval for taking images when the garbage is delivered to ensure the continuity and timeliness of the data. The width gauge and the electronic digital height gauge respectively measure the maximum width and maximum height of the garbage piled in the trash bin. Combined with the data changes within the time interval, the accumulation state and change trend of the garbage can be accurately reflected. The third microprocessor receives data from various sensors and calculates the garbage existence state index and garbage overflow index of the trash bin and its surrounding area through complex algorithms. These two indexes comprehensively reflect the accuracy of garbage delivery and the full load state of the trash bin. When the garbage existence state index exceeds the preset delivery threshold and the garbage overflow index is greater than the preset garbage overflow index threshold, the system automatically determines that the kitchen waste is delivered incorrectly or the trash bin is about to be full, and triggers an alarm. After receiving the instruction from the third microprocessor, the alarm immediately issues an alarm to remind the management personnel to intervene. This helps to promptly detect and correct garbage disposal errors, prevent environmental pollution and odor problems caused by full garbage bins, reduce the need for manual monitoring, and reduce labor costs. At the same time, it avoids environmental pollution and odor problems caused by full garbage bins and optimizes the efficiency of resource use.

[0058] The calculation formula for the garbage existence status index of the garbage bin and its surrounding area is:

[0059]

[0060] Where R is the garbage status index of the trash bin and its surrounding area, n is the total amount of garbage in the trash bin, the unit is pieces, and the value of n is a positive integer between 4 and 10, t is the time interval for taking images of garbage, the unit is min, and its value is a positive integer multiple of 3, d t Indicates the maximum length of the garbage piled in the garbage bin within the time interval t, in meters, w t Indicates the maximum width of the garbage piled in the garbage bin within the time interval t, in meters, hours t Indicates the maximum height of the garbage piled in the garbage bin within the time interval t, in meters, h t-1 It indicates the maximum height of the garbage piled in the garbage bin during the previous one-minute time interval, in meters.

[0061] The calculation formula for the garbage overflow index is:

[0062]

[0063] Where x is the garbage overflow index.

[0064] The alarm is an audible and visual alarm.

[0065] The sound and light alarm can quickly attract people's auditory attention by emitting specific sounds, especially in noisy environments. The sound alarm can penetrate the noise and ensure the effectiveness of information transmission. The flashing lights can visually attract people's attention and more clearly indicate the alarm location.

[0066] For example, in a garbage treatment plant, in order to improve the classification efficiency and treatment quality of kitchen waste, an IoT-based kitchen waste classification system was installed.

[0067] The user throws the garbage onto the conveyor belt, and the odor sensor captures the odor feature vector of the garbage and sends it to the first microprocessor. After receiving the odor feature vector, the first microprocessor calculates the label classification feature vector. When the label classification feature vector exceeds the preset odor threshold, the garbage is identified as kitchen waste. After the first microprocessor identifies the garbage as kitchen waste, it starts several robotic arms, which accurately classify the kitchen waste, such as distinguishing food residues, fruit peels, etc. After classification, the classified kitchen waste is put into the designated trash can.

[0068] The laser ranging sensor measures the bottom area of ​​the trash can and sends it to the second microprocessor. The ultrasonic sensor transmits ultrasonic waves vertically to the bottom of the trash can, measures the height of the trash can, and sends it to the second microprocessor. When the second microprocessor receives the bottom area and height of the trash can, it calculates the volume of the kitchen waste inside the trash can based on the propagation speed of the ultrasonic wave, the emission and reception time. At the same time, after receiving the eigenvalue of the garbage odor feature vector, the second microprocessor calculates the kitchen waste odor index through the eigenvalue of the garbage odor feature vector and the volume of the kitchen waste inside the trash can. When the volume of the kitchen waste inside the trash can exceeds the preset volume threshold and the kitchen waste odor index exceeds the preset odor index threshold, the second microprocessor remotely calls a transport vehicle to collect the kitchen waste.

[0069] The infrared camera monitors the placement of kitchen waste in the waste treatment plant in real time and sends the image to the image processor and the third microprocessor. The image processor detects the total amount of garbage inside the trash bin and sends it to the third microprocessor. The timer records the time interval of image shooting and sends it to the third microprocessor. The width gauge and the electronic digital height gauge respectively measure the maximum width value and the maximum height value of the garbage piled in the trash bin and send them to the third microprocessor respectively. After receiving the total amount of garbage inside the trash bin, the time interval of image shooting, the maximum width value and the maximum height value of the garbage piled in the trash bin, the third microprocessor calculates the garbage existence state index of the trash bin and its surrounding area to determine whether the kitchen waste is placed incorrectly. At the same time, the third microprocessor calculates the garbage overflow index to evaluate whether the trash bin is about to overflow. When the garbage existence state index exceeds the preset placement threshold and the garbage overflow index is greater than the preset overflow index threshold, the third microprocessor activates the alarm and issues an alarm to remind relevant personnel to handle it in time.

[0070] The above embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work shall all fall within the scope of protection of the present invention.

Claims

1. The kitchen waste classification system based on the Internet of Things is characterized by: Including garbage classification module, garbage collection module, monitoring and alarm module; The kitchen waste classification module is arranged on the conveyor belt and is used to identify the type of waste; Used to classify kitchen waste after identifying the waste as kitchen waste; Used to put the kitchen waste into the trash can after the classification is completed; The kitchen waste collection module is arranged in a waste treatment plant, and is used to detect the volume of kitchen waste inside the waste bin; and is used to call a transport vehicle to collect the kitchen waste; The monitoring and alarm module is arranged in a garbage disposal plant, and is used for monitoring the delivery of kitchen waste, and for issuing an alarm.

2. The kitchen waste classification system based on the Internet of Things according to claim 1 is characterized in that : The kitchen waste classification module includes an odor sensor, a first microprocessor, and several robotic arms; The odor sensor is arranged on the conveyor belt, and is used to obtain the characteristic value of the garbage odor characteristic vector at a certain moment, and send it to the first microprocessor; The first microprocessor is disposed on the conveyor belt and is in communication with the odor sensor, and is used to identify the type of garbage after receiving the characteristic value of the garbage odor characteristic vector at a certain moment; After receiving the feature value of the garbage smell feature vector at a certain moment, the feature value of the label classification feature vector at a certain moment is calculated by using the feature value of the garbage smell feature vector at a certain moment, the first weight coefficient, and the second weight coefficient; Used to identify garbage as kitchen waste after the characteristic value of the label classification characteristic vector at a certain moment exceeds a preset odor threshold; used to start a plurality of the robotic arms after the garbage is identified as kitchen waste; A plurality of the mechanical arms are arranged on the conveyor belt and are electrically connected to the first microprocessor for classifying the kitchen waste; Used to put kitchen waste into the trash can after classification is completed.

3. The kitchen waste classification system based on the Internet of Things according to claim 2 is characterized in that : The calculation formula of the eigenvalue of the label classification feature vector at a certain moment is: Among them, E i ′ is the eigenvalue of the label classification feature vector at the i-th moment, α is the first weight coefficient, β is the second weight coefficient, sigmoid is the activation function, E i is the eigenvalue of the garbage odor feature vector at the i-th moment, and e is the base of the natural logarithm.

4. The kitchen waste classification system based on the Internet of Things according to claim 2 is characterized in that :Some of the robotic arms are made of carbon fiber.

5. The kitchen waste classification system based on the Internet of Things according to claim 3 is characterized in that : The kitchen waste collection module includes a laser ranging sensor, an ultrasonic sensor, a timer, and a second microprocessor; The laser distance measuring sensor is arranged in the garbage treatment plant to measure the bottom area of ​​the garbage can and send it to the second microprocessor; The ultrasonic sensor is arranged in a garbage disposal plant, and is used to emit ultrasonic waves vertically to the bottom of the garbage bin, and is used to measure the height of the garbage bin after the ultrasonic waves touch the garbage and form reflected waves, and send the reflected waves to the second microprocessor; The timer is set in the garbage treatment plant, and is used to record the time when the ultrasonic sensor emits ultrasonic waves vertically to the bottom of the garbage bin, and send it to the second microprocessor; and is used to record the time when the reflected wave is received after the ultrasonic wave touches the garbage and forms a reflected wave, and send it to the second microprocessor; The second microprocessor is arranged in the garbage treatment plant, and is communicatively connected with the laser ranging sensor, the ultrasonic sensor, the kitchen waste classification module, and the transport vehicle communication center. The second microprocessor is used to calculate the volume of the kitchen waste inside the garbage bin after receiving the bottom area of ​​the garbage bin, the height of the garbage bin, the time when the ultrasonic wave is emitted, and the time when the reflected wave is received through the bottom area of ​​the garbage bin, the height of the garbage bin, the propagation speed of the ultrasonic wave in the air, the time when the ultrasonic wave is emitted, and the time when the reflected wave is received; the second microprocessor is used to calculate the kitchen waste odor index after obtaining the volume of the kitchen waste inside the garbage bin and the garbage odor feature vector through the eigenvalues ​​of the volume of the kitchen waste inside the garbage bin and the garbage odor feature vector at a certain moment; and the second microprocessor is used to call a transport vehicle to collect the kitchen waste when the volume of the kitchen waste inside the garbage bin exceeds a preset volume threshold and the kitchen waste odor index exceeds a preset kitchen waste odor index threshold.

6. The kitchen waste classification system based on the Internet of Things according to claim 5 is characterized in that The calculation formula for the volume of kitchen waste inside the trash can is: Among them, V is the volume of kitchen waste inside the trash can, in m 3 , S is the bottom area of ​​the trash can, in m 2 , H is the height of the trash can, in meters, C is the propagation speed of ultrasound in the air, in meters / seconds, T1 is the time when the ultrasound is emitted, in seconds, and T2 is the time when the reflected wave is received, in seconds; The calculation formula of kitchen waste odor index is: J=V*E i Among them, J is the kitchen waste odor index.

7. The kitchen waste classification system based on the Internet of Things according to claim 5 is characterized in that :The laser ranging sensor is a pulsed laser ranging sensor.

8. The kitchen waste classification system based on the Internet of Things according to claim 5 is characterized in that : The monitoring alarm module includes an infrared camera, an image processor, a timer, a width gauge, an electronic digital height gauge, a third microprocessor, and an alarm; The infrared camera is set in the garbage treatment plant to monitor the placement of kitchen waste and send it to the image processor and the third microprocessor; The image processor is arranged in the garbage treatment plant and is in communication connection with the infrared camera, and is used to detect the total amount of garbage inside the garbage bin after receiving the delivery of kitchen waste, and send it to the third microprocessor; The timer is set in the garbage treatment plant, and is used to record the time interval for taking images when the garbage is put in, and send it to the third microprocessor; The width measuring instrument is arranged in the garbage treatment plant, and is used to measure the maximum width value of the garbage piled in the garbage bin within a time interval, and send it to the third microprocessor; The electronic digital height gauge is arranged in the garbage disposal plant, and is used to measure the maximum height value of the garbage piled in the garbage bin within the time interval, and the maximum height value of the garbage piled in the garbage bin within the time interval of the previous minute, and send it to the third microprocessor; The third microprocessor is arranged in the garbage disposal plant, and is in communication connection with the infrared camera, the image processor, the timer, the width gauge, the electronic digital height ruler, and the kitchen waste collection module, and is used for receiving the delivery situation of kitchen waste, the total amount of garbage in the garbage bin, the time interval for taking images when the garbage is delivered, the maximum width value of the garbage piled in the garbage bin within the time interval, the maximum height value of the garbage piled in the garbage bin within the time interval, and the maximum height value of the garbage piled in the garbage bin within the time interval of the previous minute, and then collecting the kitchen waste from the garbage bin to the garbage bin. The garbage existence state index of the garbage bin and its vicinity is calculated by the maximum width value of the garbage piled in the garbage bin within the time interval, the maximum height value of the garbage piled in the garbage bin within the time interval, and the maximum height value of the garbage piled in the garbage bin within the time interval one minute before; after receiving the total amount of garbage in the garbage bin, the maximum width value of the garbage piled in the garbage bin within the time interval, the maximum length value of the garbage piled in the garbage bin within the time interval, and the bottom area of ​​the garbage bin, the garbage overflow index is calculated by the total amount of garbage in the garbage bin, the maximum width value of the garbage piled in the garbage bin within the time interval, the maximum length value of the garbage piled in the garbage bin within the time interval, and the bottom area of ​​the garbage bin; When the garbage existence status index of the garbage bin and its vicinity exceeds a preset placement threshold and the garbage overflow index is greater than a preset garbage overflow index threshold, the alarm is activated; The alarm is arranged in the garbage treatment plant and is electrically connected to the third microprocessor for issuing an alarm.

9. The kitchen waste classification system based on the Internet of Things according to claim 8 is characterized in that The calculation formula for the garbage existence status index of the garbage bin and its surrounding area is: Among them, R is the garbage status index of the trash can and its surrounding area, n is the total amount of garbage inside the trash can, in units of pieces, and t is the time interval for taking images when the garbage is placed, in units of min, d t Indicates the maximum length of the garbage piled in the garbage bin within the time interval t, in meters, w t Indicates the maximum width of the garbage piled in the garbage bin within the time interval t, in meters, hours t Indicates the maximum height of the garbage piled in the garbage bin within the time interval t, in meters, h t-1 It indicates the maximum height of the garbage piled in the garbage bin in the previous one-minute time interval, in meters; The calculation formula for the garbage overflow index is: Where x is the garbage overflow index.

10. The kitchen waste classification system based on the Internet of Things according to claim 8, characterized in that :The alarm is an audible and visual alarm.

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