Intelligent garbage classification and recycled material automatic valuation system

Abstract

The invention discloses an intelligent garbage classification and recycled material automatic valuation system, which comprises a garbage throwing unit, a garbage classification unit, a weighing sensor and an image acquisition device, and is characterized in that the garbage throwing unit is provided with an RFID reader-writer, a weighing sensor and an image acquisition device, and the RFID reader-writer is used for reading RFID label information of recycled materials so as to obtain garbage types and throwing user IDs; the weighing sensor is used for detecting the weight of the recycled material; the image acquisition device is used for acquiring an image of the recycled material; the cloud server is in communication connection with the garbage throwing unit, the cloud server is provided with a market recycling price database, and the cloud server is used for calculating the final value of recycled objects according to the garbage category transmitted by the RFID reader-writer, the weight transmitted by the weighing sensor, the image transmitted by the image acquisition device and a preset value evaluation formula; according to the invention, efficient and accurate classification of garbage and transparent value evaluation of recycled objects are realized, the transaction process is simplified, and the user experience is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of garbage recycling, and in particular to an intelligent garbage classification and recycling object automatic valuation system. BACKGROUND

[0002] At present, in urban residential areas and commercial areas, garbage disposal is an important environmental problem. Traditional garbage classification relies on manual identification and manual sorting, which is not only inefficient but also prone to errors. With the development of technology, some automated equipment and technology have begun to be applied to garbage classification, such as barcode scanning, image recognition, etc., but these methods still have many limitations, such as low recognition accuracy, high cost, etc. In recent years, the development of RFID technology and Internet of Things technology has provided a new solution for garbage classification, but it is still in the initial stage and has not been widely popularized and applied.

[0003] Specific solutions of prior art:

[0004] Barcode scanning: by attaching a barcode to the garbage bag, the user scans the barcode when disposing of the garbage, and the system classifies according to the preset rules. This method is simple and easy to implement, but the recognition accuracy is low, it is easily affected by pollution, and detailed information cannot be recorded.

[0005] Image recognition: use a camera to take pictures of the garbage and identify the type of garbage through image processing algorithms. Although the recognition accuracy is high, the computational complexity is high, the hardware performance requirements are high, and the recognition effect is unstable in complex environments.

[0006] Manual sorting: a dedicated person is responsible for the classification and disposal of garbage, which has high classification accuracy, but the labor cost is high, the work efficiency is low, and it is difficult to meet the large-scale demand.

[0007] The existing garbage classification technology generally has low recognition accuracy, high cost, and low efficiency. Barcode scanning and image recognition technology are easily affected by external factors in actual application, resulting in high classification error rate. Manual sorting, although accurate, cannot meet the demand of large-scale and high-frequency, and has high labor intensity and is difficult to sustain. Therefore, there is an urgent need for an efficient, accurate, and reliable garbage classification and recycling value evaluation technology. SUMMARY

[0008] The purpose of the present application is to provide an intelligent garbage classification and recycling object automatic valuation system, which realizes efficient and accurate classification of garbage and transparent evaluation of the value of recycling objects in residential areas and commercial areas, simplifies the transaction process, improves user experience, and improves overall operational efficiency.

[0009] According to one aspect of the present disclosure, the following technical solutions are provided

[0010] An intelligent garbage classification and recycling object automatic valuation system comprises:

[0011] A garbage throwing unit is provided with an RFID reader-writer, a weighing sensor and an image acquisition device, the RFID reader-writer is used to read the RFID tag information of the recycling object to obtain the garbage category and the throwing user ID; the weighing sensor is used to detect the weight of the recycling object; and the image acquisition device is used to acquire the image of the recycling object;

[0012] A cloud server is in communication connection with the garbage throwing unit, the cloud server is configured with a market recycling price database, and is used to calculate the final value of the recycling object according to the garbage category transmitted by the RFID reader-writer, the weight transmitted by the weighing sensor, the image transmitted by the image acquisition device and a preset value evaluation formula;

[0013] A central management system is in communication connection with the garbage throwing unit and the cloud server respectively, and is used to receive the sensor data transmitted by the garbage throwing unit, monitor the fullness of the garbage can and perform recycling scheduling according to the fullness information;

[0014] A user terminal is in communication connection with the cloud server, and is used to receive and display the final value of the recycling object and the user point information.

[0015] Further, the formula for calculating the final value of the recycling object by the cloud server is:

[0016] V=P×W×C p ×C m ×C u ,

[0017] Wherein:

[0018] V is the final value of the recycling object; P is the basic unit price, which is obtained from the market recycling price database by the cloud server according to the garbage category in real time; W is the weight of the recycling object, which is detected by the weighing sensor and calibrated by the RFID tag; c p is the purity coefficient, which is calculated by an image recognition model according to the image acquired by the image acquisition device; C m

[0019] is the market fluctuation coefficient, which is the ratio of the average price of the category on the day to the average price in the past 30 days; C u is the user point coefficient, which is determined according to the historical throwing records of the throwing user.

[0020] Further, the garbage throwing unit is also provided with a distance sensor, which is an ultrasonic sensor or an infrared sensor, used to detect the real-time distance D t of the sensor to the surface of the recycling object; and the central management system calculates the final value of the recycling object according to the real-time distance D tand the initial distance D0 of the empty bin, the fullness L of the bin is calculated by the formula L=(1-D t / D0)×100%, and the following rules are used for judgment: when L≥80%, it is marked as to be recycled and triggers the recycling schedule; when L≥95%, it is determined as "overloaded" and a replacement reminder is pushed to the user terminal.

[0021] Further, a recycling route optimization module is also included, which is configured to: obtain the position coordinates and fullness of all "to be recycled" bins; obtain the distance D i，i+1 and the time T i，i+1 spent between any two recycling points through a map API; and calculate the total recycling cost according to the formula , where C d is the distance cost coefficient, C t is the time cost coefficient, O i is the overload penalty coefficient, and C o is the penalty cost coefficient; the genetic algorithm is used to iteratively optimize the recycling point order, and the optimal route with the lowest cost is output.

[0022] Further, the cloud server is also used to calculate the user's points according to the final value of the recyclables, and the point calculation formula is I=int(V×K)+B, where I is the user's points, K is the point conversion coefficient, B is the additional bonus points, and int() is the integer function.

[0023] Further, the image acquisition device is optionally configured; when the image acquisition device is not enabled, the purity coefficient C p is set to 0.8 by default.

[0024] Further, the distance sensor has a collection frequency of once every 5 minutes, and the collected real-time distance D t is transmitted to the central management system in real time through a wireless communication module.

[0025] Further, the market fluctuation coefficient C m is calculated by the cloud server every day by grabbing the index of the price of bulk commodities for recycling; when the average price of the category on the current day is higher than the average price in the past 30 days, C m >1; and when the average price of the category on the current day is lower than the average price in the past 30 days, C m <1.

[0026] Further, the user terminal supports the function of redeeming points, and after the user selects a product to redeem through the terminal, the cloud server checks whether the user's current points meet the redemption requirements, and if so, deducts the corresponding points and generates a redemption order, and the central management system synchronizes the order information and arranges for delivery.

[0027] Further, the optimal route information includes the recycling point order, the estimated time consumption, the total mileage, and the overload status of each recycling point, and the information is synchronously pushed to the recycling vehicle terminal and the user terminal, and the user terminal can display the estimated recycling time of each drop-off point.

[0028] The technical effects and advantages of the present application are as follows:

[0029] The specific implementation of the intelligent garbage classification and automatic recycling value estimation system has many benefits, mainly in the following aspects:

[0030] 1) Achieving efficient and accurate classification of garbage and transparent value assessment of recyclables, simplifying the transaction process and improving user experience.

[0031] 2) The multi-level data processing and transmission architecture ensures the stability and reliability of the system, improves the efficiency and accuracy of data processing.

[0032] 3) The application of Internet of Things technology further optimizes the recycling process, improves the overall operational efficiency, and reduces operational costs. DETAILED DESCRIPTION

[0033] For descriptive purposes, the present disclosure can use spatially relative terms such as "under", "below", "lower", "down", "above", "upper", "higher", and "side" (e.g., as in "side wall"), which can embody the direction of the spatial orientation of one feature to another feature,

[0034] Intelligent garbage classification and automatic recycling value estimation system

[0035] I. Core calculation formula supplement For the value assessment of recyclables, the fullness monitoring of garbage cans, and the recycling route optimization link, the following calculation formulas and parameter explanations are supplemented: 1. Recyclable value assessment formula Core formula:

[0036] V = P x W x C p x C m x C u

[0037] Parameter explanation:

[0038] V: Final value of recyclables (unit: yuan);

[0039] P: Basic unit price (unit: yuan / kg), updated by the cloud server in real time according to the garbage category (such as waste paper, plastic, metal, etc.) (associated with the market recycling price database);

[0040] W: Recyclable weight (unit: kg), recorded by RFID tag (calibrated by garbage container built-in weighing sensor);

[0041] C p: Purity coefficient (value range 0-1), adjust according to the cleanliness of garbage / impurity proportion (such as clean plastic bottle C p = 0.9, contaminated plastic C p = 0.5, determined by image recognition or historical data training model output);

[0042] C m : Market fluctuation coefficient (value range 0.8-1.2), calculated by cloud server daily commodity recycling price index (such as C m = average price of the day / average price of the past 30 days);

[0043] C u : User points coefficient (value range 1.0-1.5), encourage users to correctly classify (such as consecutive 30 days of correct delivery C u = 1.2, new user first month C u = 1.5).

[0044] Garbage can fullness calculation formula core formula:

[0045]

[0046] Parameter description:

[0047] L: Garbage can fullness (unit: %);

[0048] D t : Real-time sensor monitoring distance (unit: meters), measured by ultrasonic / infrared sensor (vertical distance from sensor to garbage surface);

[0049] D0: Initial distance (unit: meters), the distance from the sensor to the bottom of the garbage can when the garbage can is empty (calibrated when the system is installed).

[0050] Determination rule: when L≥80%, it is determined as "fullness warning", and the central management system triggers the recycling scheduling; when L≥95%, it is determined as "overload", and the APP pushes a reminder to the user to change the delivery point.

[0051] Recycling route optimization cost formula

[0052] Core formula:

[0053]

[0054] Parameter description:

[0055] C: Total recycling cost (unit: yuan);

[0056] n: Number of recycling points;

[0057] D i，i+1Distance between the ith recycling point and the (i+1)th recycling point (unit: km), obtained from a map API (such as Gaode, Baidu Map);

[0058] C d Distance cost coefficient (unit: yuan / km), i.e. vehicle unit mileage fuel consumption + maintenance cost; i，i+1 Time consumed from the ith recycling point to the (i+1)th recycling point (unit: hour), calculated from real-time traffic data;

[0059] C t Time cost coefficient (unit: yuan / hour), including labor cost and vehicle standby cost;

[0060] O i Overload penalty coefficient of the ith recycling point (O i = 1.5 when overloaded, and O i = 1 when normal), used to prioritize scheduling full-load points.

[0061] User points calculation formula

[0062] Core formula:

[0063] I = int(V x K) + B

[0064] Parameter description:

[0065] I: Points obtained by the user;

[0066] V: Value of recyclables (yuan);

[0067] K: Points conversion coefficient (e.g. 1 yuan = 10 points, i.e. K = 10);

[0068] B: Additional bonus points (e.g. B = 50 for the first correct drop-off, and B = 100 for 7 consecutive drop-offs);

[0069] int(): Integer function.

[0070] Recyclable value evaluation process (executed by cloud server)

[0071] Step 1: Receive basic data (trash category (T), weight (W), drop-off user ID (U)) transmitted by RFID reader;

[0072] Step 2: Call market database to obtain the daily basic unit price P of category T;

[0073] Step 3: Take pictures (optional solution) through camera, and call image recognition model to calculate purity coefficient C p (If image recognition is not enabled, C p = 0.8 by default);

[0074] Step 4: Calculate market volatility coefficient C m

[0075] (C m = Daily category average price / 30-day average price);

[0076] Step 5: Query the user U's historical investment records to determine the user's credit coefficient C u (If the number of consecutive correct investment days is ≥30, C u = 1.2);

[0077] Step 6: Substitute into the formula

[0078] V = P × W × C p × C m × C u

[0079] Calculate the value V;

[0080] Step 7: Simultaneously calculate the user's credit I = int(V × K) + B, and update the user's account credit;

[0081] Step 8: Transmit V and I to the user's APP and store them in the historical record database.

[0082] Garbage can fullness monitoring process (sensor network + central management system execution)

[0083] Step 1: The sensor inside the garbage can (ultrasonic / infrared) collects real-time distance D t every 5 minutes;

[0084] Step 2: The sensor transmits D t to the central management system through the wireless communication module;

[0085] Step 3: The system calls the initial distance D0 (recorded during installation) and substitutes into the formula

[0086]

[0087] Calculate the fullness L;

[0088] Step 4: If L ≥ 80%, mark as "to be recycled" state and add to the dispatch queue; if L ≥ 95%, trigger emergency dispatch.

[0089] Recycling vehicle route optimization process (central management system execution)

[0090] Step 1: Collect the position coordinates (x i , y i ) and fullness L i of all "to be recycled" garbage cans;

[0091] Step 2: Obtain real-time traffic data (through map API), calculate the distance D between any two recycling points i，j and time-consuming T i，j ;

[0092] Step 3: Initialize the vehicle scheduling scheme (e.g. generate an initial route according to the nearest distance principle);

[0093] Step 4: Substitute the total path cost formula C = ∑(...), calculate the total cost of the initial scheme;

[0094] Step 5: Optimize the route using genetic algorithm (iterate 50 times), adjust the recycling point order each time, recalculate the total cost, and keep the lowest cost scheme;

[0095] Step 6: Output the optimal route (including recycling point order, estimated time-consuming, total mileage), push to the recycling vehicle terminal, and synchronize to the user APP (display "estimated recycling time").

[0096] User points exchange process (smartphone APP execution)

[0097] Step 1: User selects the exchange goods (such as daily necessities, coupons) in the APP "points center", checks the required points I 需 ;

[0098] Step 2: The system checks the user's current points (I 现 ), if (I 现 ≥ I 需 ), allow exchange;

[0099] Step 3: After the user confirms the exchange, the system deducts the points (I 新 = I 现 I 需 ), generates an exchange order;

[0100] Step 4: The central management system synchronizes the order information, arranges the goods distribution or sends the electronic coupon code to the user APP.

[0101] In addition, it should be noted that the contents not described in detail in the present specification belong to the existing technology known to those skilled in the art.

[0102] Those skilled in the art should understand that the above embodiments are only for clearly illustrating the present disclosure, and are not intended to limit the scope of the present disclosure. Based on the above disclosure, other changes or modifications can also be made by those skilled in the art, and these changes or modifications are still within the scope of the present disclosure.

Claims

1. An intelligent waste sorting and recyclable material automatic valuation system, characterized in that, include: The waste disposal unit is equipped with an RFID reader, a weighing sensor, and an image acquisition device. The RFID reader is used to read the RFID tag information of the recyclables to obtain the waste category and the user ID of the disposer; the weighing sensor is used to detect the weight of the recyclables; and the image acquisition device is used to acquire images of the recyclables. A cloud server is communicatively connected to the waste disposal unit. The cloud server is configured with a market recycling price database, which is used to calculate the final value of the recyclables based on the waste type transmitted by the RFID reader, the weight transmitted by the weighing sensor, the image transmitted by the image acquisition device, and the preset value assessment formula. The central management system is connected to both the waste disposal unit and the cloud server to receive sensor data transmitted by the waste disposal unit, monitor the fullness of the waste bins, and execute recycling scheduling based on the fullness information. The user terminal is connected to the cloud server to receive and display the final value of the recyclables and user points information.

2. The intelligent waste sorting and recyclable automatic valuation system according to claim 1, characterized in that, The formula used by the cloud server to calculate the final value of recyclables is: H=P×W×C p ×C m ×Cu, in: V represents the final value of the recyclables; P is the base unit price, obtained in real-time from the market recycling price database by the cloud server based on the waste category; W is the weight of the recyclables, detected by a weighing sensor and recorded and calibrated via an RFID tag; C p C is the purity coefficient, calculated using an image recognition model based on images acquired by the image acquisition device. m C is the market volatility coefficient, which is the ratio of the average price of the category on the current day to the average price over the past 30 days. u The user points coefficient is determined based on the historical delivery records of the user.

3. The intelligent waste sorting and recyclable automatic valuation system according to claim 1, characterized in that, The waste disposal unit is also equipped with a distance sensor, which is either an ultrasonic sensor or an infrared sensor, used to detect the real-time distance D between the sensor and the surface of the recyclable. t The central management system is based on the real-time distance D. t The initial distance D0 between the trash can and the empty trash can is calculated using the formula L = (1 - D0) / (1 - D0). t The fullness L of the trash can is calculated by multiplying ( / D0) by 100%, and the following rules are applied: when L≥80%, it is marked as pending recycling and recycling scheduling is triggered; when L≥95%, it is judged as "overloaded" and a replacement reminder for the disposal point is pushed to the user terminal.

4. The intelligent waste sorting and automatic valuation system for recyclables according to claim 1, characterized in that, It also includes a recycling route optimization module, which is configured to: obtain the location coordinates and fullness of all "to be recycled" trash cans; and obtain the distance D between any two recycling points via a map API. i，i+1 and time T i，i+1 According to the formula Calculate the total recovery cost, where C d C is the distance cost coefficient. t O is the time cost coefficient. i C is the overload penalty coefficient. o To penalize the cost coefficient, a genetic algorithm is used to iteratively optimize the order of recycling points and output the optimal route with the lowest cost.

5. The intelligent waste sorting and automatic valuation system for recyclables according to claim 1, characterized in that, The cloud server is also used to calculate user points based on the final value of the recyclables. The point calculation formula is: I = int(V×K) + B, where I is the user points, K is the point conversion coefficient, B is the extra reward points, and int() is the rounding function.

6. The intelligent waste sorting and automatic valuation system for recyclables according to claim 2, characterized in that, The image acquisition device is an optional configuration; when the image acquisition device is not enabled, the purity coefficient C p The default value is 0.

8.

7. The intelligent waste sorting and automatic valuation system for recyclables according to claim 3, characterized in that, The distance sensor collects data every 5 minutes, and the real-time distance D collected is... t The data is transmitted to the central management system in real time via a wireless communication module.

8. The intelligent waste sorting and automatic valuation system for recyclables according to claim 2, characterized in that, The market volatility coefficient C m The daily calculation of the commodity recycling price index is performed by a cloud server. When the average price of a category on that day is higher than the average price over the past 30 days, C... m >1; When the average price of the category on that day is lower than the average price of the past 30 days, C m <1.

9. The intelligent waste sorting and recyclable automatic valuation system according to claim 5, characterized in that, The user terminal supports a points redemption function. After the user selects a redemption item through the terminal, the cloud server verifies whether the user's current points meet the redemption requirements. If they do, the corresponding points are deducted and a redemption order is generated. The central management system synchronizes the order information and arranges delivery.

10. The intelligent waste sorting and automatic valuation system for recyclables according to claim 4, characterized in that, The optimal route information includes the order of collection points, estimated time, total mileage, and overload status of each collection point. This information is simultaneously pushed to the collection vehicle terminal and the user terminal, and the user terminal can display the estimated collection time for each collection point.

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