Garbage collecting and metering system based on RFID technology

Abstract

The invention relates to the technical field of intelligent environmental sanitation, in particular to a garbage collection and metering system based on an RFID (Radio Frequency Identification Device) technology, which comprises an electronic tag and a card reader, the electronic tag is arranged on a garbage bag used by a user, and tag information about capacity information of the garbage bag is pre-stored in the electronic tag; the card reader is arranged on a garbage can of a garbage recycling station, and when a user throws a garbage bag into the garbage can, the card reader is used for reading label information stored in an electronic label on the garbage bag; and the data platform is used for carrying out statistics on the garbage quantity of the garbage recycling station and carrying out data analysis on the basis of the capacity information in the label information corresponding to all the garbage bags. According to the invention, the accuracy and working efficiency of garbage collection metering can be obviously improved, and metering statistics of garbage is realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent sanitation technology, and in particular to a garbage collection and metering system based on RFID technology. Background Art

[0002] With the improvement of global environmental awareness and the popularization of the concept of resource recycling, garbage classification, as a key measure to reduce environmental pollution and improve resource recycling rate, has been widely implemented in many developed countries and has achieved remarkable results. However, in the actual process of promoting garbage classification, there is still the problem of uneven distribution of residents' participation rate, and some residents still have weak classification awareness. At the same time, the management of classified garbage still mainly relies on manual labor, especially the measurement of classified garbage, which is still mainly based on manual operation and manual record, resulting in slow garbage classification management process and low operational efficiency. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a garbage collection and metering system based on RFID technology, which solves the technical problem that the existing garbage metering work mainly relies on manual labor and has low operating efficiency.

[0005] (II) Technical solution

[0006] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0007] The embodiment of the present invention provides a garbage collection and metering system based on RFID technology, including: an electronic tag and a card reader;

[0008] The electronic tag is set on the garbage bag used by the user, and the electronic tag pre-stores label information about the capacity information of the garbage bag;

[0009] The card reader is arranged on a trash can at a trash recycling site. When a user puts a trash bag into the trash can, the card reader is used to read the tag information stored in the electronic tag on the trash bag.

[0010] The data platform is used to count the amount of garbage at the garbage recycling site and perform data analysis based on the capacity information in the label information corresponding to all garbage bags.

[0011] Optionally, the electronic tag is a passive electronic tag, the card reader is a radio frequency reader, and the radio frequency reader activates the passive electronic tag by transmitting a first radio frequency signal, so that the passive electronic tag transmits a second radio frequency signal containing tag information.

[0012] Optionally, the card reader includes:

[0013] A controller module, configured to generate an initial first radio frequency signal;

[0014] A radio frequency circuit, used for modulating an initial first radio frequency signal;

[0015] An antenna, used for transmitting the modulated first radio frequency signal;

[0016] as well as,

[0017] When the card reader is used to receive the second radio frequency signal,

[0018] The antenna is further used to receive a second radio frequency signal;

[0019] The radio frequency circuit is further used to demodulate the second radio frequency signal;

[0020] The controller module is also used to receive the demodulated second radio frequency signal.

[0021] Optionally, the label information includes: the volume capacity of the garbage bag and the type of garbage it is used to contain.

[0022] Optionally, the metering system further comprises:

[0023] The image acquisition device is arranged on the top of the trash can and is used to obtain a first image in the trash can when the card reader reads the electronic tag of a newly-added trash bag;

[0024] An image analysis device is used to identify a newly thrown garbage bag in the first image based on the first image; and, based on the image of the newly thrown garbage bag, identify the fullness of the newly thrown garbage bag; and predict the predicted volume of the newly thrown garbage bag based on the fullness of the newly thrown garbage bag and the garbage type in the label information.

[0025] Optionally, the image analysis device comprises:

[0026] A first image recognition module is used to perform a subtraction between the first image and a second image of the trash can obtained when the trash bag was last put in, and to use a region where the difference is greater than a first preset value as a region of interest corresponding to the newly put in trash bag in the first image;

[0027] The second image recognition module is used to predict the fullness of a newly-thrown garbage bag in the region of interest;

[0028] The volume prediction module is used to determine the predicted volume of the newly-thrown garbage bag based on the volume capacity and fullness of the newly-thrown garbage bag.

[0029] Optionally, the image analysis device further includes:

[0030] An image processing module, used for performing a difference operation on the first image and the second image to obtain a difference image, and assigning zero to the pixel values ​​of the region of interest in the difference image to obtain a feature image;

[0031] A third image recognition module is used to predict the predicted density of the newly-thrown garbage bag based on the feature image and the garbage type of the newly-thrown garbage bag;

[0032] The weight prediction module is used to determine the predicted weight of the newly-thrown garbage bag based on the predicted density and predicted volume of the newly-thrown garbage bag.

[0033] Optionally, the image analysis device further includes:

[0034] a fourth image recognition module, for identifying the type of garbage in a newly-added garbage bag based on the region of interest of the first image;

[0035] A judgment module is used to judge whether the garbage types in the newly-thrown garbage bag only include the garbage types currently contained in the garbage bin. If so, it is determined that the garbage classification is correct;

[0036] Among them, the second image recognition module, the third image recognition module and the fourth image recognition module are respectively: deep learning models with suitable model parameters obtained through a pre-training process.

[0037] Optionally, the data platform includes:

[0038] The sequence processing module is used to obtain the total amount of garbage in all garbage bins at the garbage collection site, and to count the total amount of garbage at the garbage collection site after each preset sampling period since the last emptying, so as to form a time series of the total amount of garbage;

[0039] A time prediction model, used to predict a future time point when the total amount of garbage at the garbage collection site reaches a preset value based on the time series;

[0040] A scheduling module, used for determining the time to schedule the garbage truck to go to the garbage collection site based on the future time point;

[0041] The time prediction model is a machine learning model with suitable model parameters obtained through a pre-training process.

[0042] Optionally, the data platform further includes:

[0043] The data preprocessing module is used to record the total amount of new garbage at the garbage recycling site based on the predicted volume and predicted weight of the newly-thrown garbage bag predicted by the image analysis device each time a new garbage bag is thrown in.

[0044] (III) Beneficial effects

[0045] The garbage collection and metering system proposed in the embodiment of the present invention includes an electronic tag and a card reader; the electronic tag is set on the garbage bag used by the user, and the electronic tag pre-stores tag information about the capacity information of the garbage bag; the card reader is set on the garbage bin of the garbage recycling site, and when the user puts the garbage bag into the garbage bin, the card reader is used to read the tag information stored in the electronic tag on the garbage bag; the data platform is used to count the garbage volume of the garbage recycling site and perform data analysis based on the capacity information in the tag information corresponding to all garbage bags.

[0046] Based on the electronic tags set on the garbage bags and the card readers set on the garbage bins, whenever a garbage bag is put into the garbage bin, the garbage bag can be automatically identified and the number of garbage bags can be counted through the card reader, which significantly improves the accuracy and work efficiency of garbage collection and measurement, and realizes the measurement and statistics of garbage. Moreover, based on the metering system provided by the present invention, it is possible to provide real-time monitoring and alarm functions for the amount of garbage at the garbage collection site, so as to help managers optimize the garbage management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of the architecture of a garbage collection and metering system based on RFID technology provided in an embodiment;

[0048] Figure 2 It is a structural schematic diagram of a card reader provided in an embodiment;

[0049] Figure 3 Schematic diagram of the architecture of the image acquisition device and the image analysis device in the embodiment;

[0050] Figure 4 Schematic diagram of the architecture of the data platform in the embodiment. DETAILED DESCRIPTION

[0051] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0052] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0053] Embodiment 1

[0054] like Figure 1As shown, this embodiment provides a garbage collection and metering system based on RFID technology, including: an electronic tag and a card reader.

[0055] The electronic tag is arranged on a garbage bag used by a user, and the electronic tag stores label information about the capacity information of the garbage bag in advance.

[0056] The card reader is arranged on a trash can at a trash recycling site. When a user puts a trash bag into the trash can, the card reader is used to read the tag information stored in the electronic tag on the trash bag.

[0057] The data platform is used to count the amount of garbage at the garbage recycling site and perform data analysis based on the capacity information in the label information corresponding to all garbage bags.

[0058] In order to more precisely count the number of garbage bags or the amount of garbage for each classification, the label information may include: the number of the garbage bag, the capacity information and the type of garbage used to hold. The types of garbage may specifically include: recyclable garbage, hazardous garbage, kitchen waste, and other garbage. The label information may also be distinguished by the numbers of different segments in the number of the garbage bag. For example, when the last digit of the number is 0, 1, 2, or 3, it indicates dry garbage, wet garbage, hazardous garbage, and recyclable garbage, respectively. The capacity information of the garbage bag may be volume capacity or weight capacity. The capacity information may also be distinguished by the numbers of different segments in the number of the garbage bag. For example, the first digit of the number indicates the volume capacity. For example, 1 indicates that the volume capacity of the garbage bag is 1L, 2 indicates that the volume capacity of the garbage bag is 2L, and 5 indicates that the volume capacity of the garbage bag is 5L. In addition, in order to facilitate users to distinguish garbage bags used to hold different types of garbage, four colors of garbage bags may be set, namely blue, red, green, and black, corresponding to recyclable garbage, hazardous garbage, kitchen waste, and other garbage.

[0059] Based on the electronic tags set on the garbage bags and the card readers set on the garbage bins, whenever a garbage bag is put into the garbage bin, the card reader can be used to automatically identify the type of garbage contained in the garbage bag and count the number of garbage bags, significantly improving the accuracy and work efficiency of garbage collection and measurement, and realizing the measurement and statistics of classified garbage. Moreover, based on the metering system provided by the present invention, it is possible to provide real-time monitoring and alarm functions for the amount of garbage at the garbage recycling site, so as to help managers optimize the garbage management process.

[0060] In a preferred implementation of this embodiment, the electronic tag is a passive electronic tag, and the card reader is a radio frequency card reader. The radio frequency card reader activates the passive electronic tag by transmitting a first radio frequency signal, so that the passive electronic tag transmits a second radio frequency signal containing tag information. The passive electronic tag includes a coil and a chip. The coil converts electrical energy from the received first radio frequency signal to start the chip inside the electronic tag; then, the electronic tag sends out the tag information pre-stored in the chip through the second radio frequency signal. Passive electronic tags do not require built-in batteries and are activated by radio frequency signals emitted by radio frequency card readers, which are suitable for large-scale low-cost applications.

[0061] Specifically, each electronic tag is pre-assigned a unique identification number to identify the uniqueness of each garbage bag, ensuring that the information of different garbage bags in the metering system will not be confused or repeated. The electronic tag has the characteristics of waterproof, dustproof and high temperature resistance, ensuring that it can still work stably in various complex environments of garbage disposal. At the same time, the electronic tag should meet the specific working frequency band and reading distance requirements. Preferably, the electronic tag can use a high-frequency RFID tag that can be accurately identified within a range of 1 meter. The electronic tag is fixed to the side of the garbage bag. During installation, use a strong adhesive or heat sealing technology to firmly attach the electronic tag to the garbage bag to prevent it from falling off or being damaged during use, ensuring that the electronic tag always remains readable in each link of garbage bag placement, transportation and processing, so as to facilitate automatic identification in the subsequent garbage disposal process and reduce manual work.

[0062] Specifically, Figure 2 As shown, the card reader includes a controller module, a radio frequency circuit and an antenna which are sequentially connected in communication, as follows:

[0063] The controller module is used to generate an initial first radio frequency signal.

[0064] The radio frequency circuit is used to modulate the initial first radio frequency signal.

[0065] The antenna is used to transmit the modulated first radio frequency signal.

[0066] When the card reader is used to receive the second radio frequency signal,

[0067] The antenna is also used to receive a second radio frequency signal;

[0068] The radio frequency circuit is also used to demodulate the second radio frequency signal;

[0069] The controller module is also used to receive the demodulated second radio frequency signal.

[0070] In addition, the metering system provided in this embodiment may also include a communication module and a power module. The communication module is connected to all card readers in the garbage station for receiving tag information read by the card reader and forwarding it to the data platform. The power module is electrically connected to the controller module and the radio frequency circuit respectively to provide power. The power module may also have an intelligent power management function, which can issue an alarm when the power is low, or automatically enter a low power consumption mode to ensure that the device can continue to operate stably in various environments.

[0071] In a specific application mode, the antenna is responsible for transmitting the first radio frequency signal and receiving the second radio frequency signal, and is a key component for data communication between the card reader and the electronic tag. The antenna can be installed on the top or side wall of the trash can, covering the area where the garbage bag is placed, to ensure that the electronic tag can be quickly activated and the tag information can be read when the garbage bag is placed. The radio frequency circuit modulates and transmits the first radio frequency signal, and demodulates the second radio frequency signal returned by the electronic tag. The controller module is the core of the entire card reader, responsible for coordinating the work of the antenna and the radio frequency circuit, processing the acquired tag information, and interacting with the communication module for data. The controller module has built-in data processing capabilities, which can filter, verify and organize the read tag information. The controller module can communicate directly with the communication module through a network cable, or communicate wirelessly with the communication module through a radio frequency circuit. The communication module transmits the tag information read by the card reader to the data platform in real time. The communication module supports communication methods including Wi-Fi, LoRa, NB-IoT, etc., which can adapt to network conditions in different scenarios and build a local Internet of Things. Through the communication module, the data platform can realize remote monitoring and management of garbage placement and classification data.

[0072] More specifically, the controller module filters, verifies and organizes the tag information as follows:

[0073] Filtration process:

[0074] When a garbage bag is put into the bag multiple times, it may cause the card reader to read the electronic tag repeatedly. In this embodiment, when repeated tag information is recognized, only the tag information read for the first time is retained as valid information. Next, in view of the situation where multiple garbage bins for carrying different types of garbage may be set side by side, it is easy for the card reader of garbage bin A to read the electronic tag in garbage bin B. By setting a minimum signal strength threshold, the misreading caused by the signal interference of the card reader set on the current garbage bin by the relatively far electronic tags in other garbage bins can be excluded. Only when the signal strength received by the card reader exceeds the set minimum signal strength threshold, the card reader will consider this to be a valid reading event. Then, a time window can also be defined, and the continuous reading of the same tag within this time window is regarded as one operation to avoid frequent reading of the same electronic tag in a short period of time. The time window can be the average time interval between two adjacent emptying garbage collection sites.

[0075] Verification Processing:

[0076] RFID electronic tags support cyclic redundancy check (CRC). The card reader can perform CRC check after receiving tag information to ensure the integrity of data transmission. In addition, further verification can be performed based on business logic, such as checking whether the garbage type of the garbage bag is legal, whether the capacity is reasonable, whether the production date of the garbage bag is within the validity period, etc. Checking whether the garbage type of the garbage bag is legal means that after parsing and obtaining the garbage type of the garbage bag in the tag information, it is matched and verified with the garbage type of the current garbage bin. If it does not match, it means that the garbage bag has been put into the wrong garbage bin.

[0077] Finishing:

[0078] The read tag information is organized in a unified standard format so that the data platform can perform subsequent data processing and analysis. This includes but is not limited to ID numbers, classification codes, capacity units, date formats, etc. In addition, the tag information can be associated with other relevant data such as the location and delivery time of the garbage collection site to form a complete record to track the flow path of the garbage bag and provide richer contextual information for the data platform to perform data analysis. Finally, for tag information that fails the above inspection or has incorrect data format, an exception handling mechanism can be set, such as recording error logs, sending alarms to notify administrators, etc.

[0079] Embodiment 2

[0080] In actual application, the garbage bags placed by users are sometimes overfilled and exceed the designed volume capacity, and sometimes not full and fail to reach the designed volume capacity. At this time, the capacity information obtained by simply reading the tag information in the electronic tag by the card reader may not match the actual amount of garbage. In order to solve this problem, the metering system provided in this embodiment, in addition to the electronic tag and card reader provided in the first embodiment, also includes:

[0081] The image acquisition device is arranged on the top of the trash can and is used to obtain the first image in the trash can when the card reader reads the electronic tag of the newly thrown trash bag.

[0082] Specifically, the image acquisition device can be a miniature camera, which is arranged on the inner side of the top of the trash can flap. In order to ensure the brightness of the shooting environment, it can also be equipped with an LED fill light.

[0083] An image analysis device is used to identify a newly thrown garbage bag in the first image based on the first image; and, based on the image of the newly thrown garbage bag, identify the fullness of the newly thrown garbage bag; and predict the predicted volume of the newly thrown garbage bag based on the fullness of the newly thrown garbage bag and the garbage type in the label information.

[0084] Specifically, there may be multiple image analysis devices, which are arranged in a one-to-one correspondence with the trash cans at the same position as the card reader. Alternatively, in order to save costs, one image analysis device may be arranged corresponding to each trash recycling station, for receiving and analyzing the first image captured by the image acquisition devices arranged in all the trash cans in the trash recycling station.

[0085] In a specific implementation, the image analysis device includes: a first image recognition module, a second image recognition module and a volume prediction module.

[0086] The first image recognition module is used to subtract the first image from the second image in the trash can obtained when the trash bag was last thrown in, and use the area where the difference is greater than a first preset value as the area of ​​interest corresponding to the newly thrown trash bag in the first image.

[0087] The second image recognition module is used to predict the fullness of a newly-thrown garbage bag in the region of interest.

[0088] The volume prediction module is used to determine the predicted volume of the newly-added garbage bag based on the volume capacity and fullness of the newly-added garbage bag. Specifically, the predicted volume is the product of the volume capacity and fullness of the newly-added garbage bag.

[0089] More preferably, in order to simultaneously obtain the weight information of the newly-added garbage bag based on the first image, the image analysis device further includes: an image processing module, a third image recognition module and a weight prediction module.

[0090] An image processing module, used for performing a difference operation on the first image and the second image to obtain a difference image, and assigning zero to the pixel values ​​of the region of interest in the difference image to obtain a feature image;

[0091] A third image recognition module is used to predict the predicted density of the newly-thrown garbage bag based on the feature image and the garbage type of the newly-thrown garbage bag;

[0092] The weight prediction module is used to determine the predicted weight of the newly-added garbage bag based on the predicted density and predicted volume of the newly-added garbage bag. Specifically, the predicted density and the predicted volume are multiplied, and the product obtained is the predicted weight.

[0093] It should be noted that the density of a bag of garbage is proportional to its weight, and when a garbage bag is placed on other garbage bags in a garbage bin, its density is positively correlated with the collapse of other garbage bags, that is, the greater the density and weight of the garbage, the easier it is for its weight to cause other garbage bags to collapse and deform. Therefore, this embodiment extracts the deformation features of other garbage bags before and after the new garbage bag is put in based on the image processing module, and uses the third image recognition module to predict the density of the bag of garbage based on the deformation information of other garbage bags contained in the feature image. In addition, since different types of garbage have different stress deformation modes, taking the garbage type into consideration can more accurately predict the density of the garbage.

[0094] More preferably, in order to further improve the accuracy of the predicted weight output by the weight prediction module, the predicted weight may be compensated based on the following formula.

[0095] W=W0+ρ(1-λ)Ve μ

[0096] Where W is the predicted weight after compensation, W0 is the product of the predicted density and the predicted volume, ρ is the predicted density, V is the predicted volume, μ is the volume compensation coefficient corresponding to different garbage types, and λ is the density compensation coefficient corresponding to different garbage types. μ and λ are both empirical constants with a range of real numbers, and different types of garbage have different μ and λ.

[0097] μ and λ can be measured experimentally. Specifically, for a type of garbage, the laboratory randomly puts various types of garbage of this type into garbage bags and measures their true weights, then randomly puts them into a garbage can to obtain the first image and the second image, and uses the above image analysis device to obtain the predicted density, predicted volume and predicted weight of the garbage bag, wherein the true weight of the garbage bag is equivalent to the predicted weight after compensation by the above formula. After repeated experiments to obtain multiple sets of data, based on the multiple sets of data, nonlinear fitting is used to solve μ and λ in the above formula to obtain the optimal values ​​of μ and λ.

[0098] In addition, in order to be able to identify whether the garbage in the garbage bag newly put in by the user has been reasonably classified, the image analysis device also includes:

[0099] a fourth image recognition module, for identifying the type of garbage in a newly-added garbage bag based on the region of interest of the first image;

[0100] The judging module is used to judge whether the garbage types in the newly-thrown garbage bag only include the garbage types that the current garbage bin is used to contain. If so, it is judged that the garbage classification is correct; if not, it is judged that the garbage classification is wrong.

[0101] Specifically, the second image recognition module, the third image recognition module and the fourth image recognition module are all deep learning models with adapted model parameters obtained through a pre-training process, and more preferably, the deep learning model is a CNN model. The pre-training process of the CNN model adopts existing technology.

[0102] In the training data set of the CNN model corresponding to the second image recognition module, the images used for training may be a plurality of sampling images taken by a micro camera after a garbage bag is filled with garbage to different fullnesses and then placed in a garbage can at different angles. The annotation information of the sampling image is the fullness of the garbage bag.

[0103] Similarly, in the training data set of the CNN model corresponding to the third image recognition module, the images used for training can be a plurality of sampling images taken at different angles in a trash can, in which the garbage of the corresponding garbage type is pre-filled into a garbage bag, and the garbage in the garbage bag (different garbage of the same garbage type, such as plastic garbage and metal garbage in recyclable garbage) is changed to have different densities, and the labeled information of the sampling images is the density of the garbage bag. The density of the garbage bag can be obtained by weighing it in advance, and then obtaining its volume by the water displacement method, and then dividing the weight by the volume to obtain the density of the garbage bag.

[0104] Similarly, in the training data set of the CNN model corresponding to the fourth image recognition module, the images used for training may be multiple sampling images of garbage of random types put into a garbage bag in advance, placed in a trash can based on different angles, and taken by a micro camera. The annotation information of the sampling images is all the garbage types in the garbage bag.

[0105] Embodiment 3

[0106] Based on the metering system provided in the first and second embodiments, the data platform can better manage the garbage collection site based on the collected data, such as identifying fluctuations in the accuracy of garbage classification and abnormal delivery volume, so as to facilitate the administrator to solve the problem in a targeted manner and improve the accuracy of garbage classification and recycling. In addition, the data platform can also provide real-time monitoring and alarm functions to help managers optimize the garbage management process.

[0107] Specifically, the data platform includes:

[0108] The data preprocessing module is used to record the total amount of new garbage at the garbage recycling site based on the predicted volume and predicted weight of the newly-thrown garbage bag predicted by the image analysis device each time a new garbage bag is thrown in.

[0109] The sequence processing module is used to obtain the total amount of garbage in all garbage bins at the garbage collection site, and to count the total amount of garbage at the garbage collection site after each preset sampling period since the last emptying, so as to form a time series of the total amount of garbage;

[0110] A time prediction model, used to predict a future time point when the total amount of garbage at the garbage site reaches a preset value based on the time series;

[0111] The scheduling module is used to determine the time to schedule the garbage truck to go to the garbage collection site based on the future time point.

[0112] Specifically, the time prediction model may be: an LSTM model obtained through a pre-training process and having adapted model parameters; or an ARIMA model obtained through a pre-training process and having suitable model parameters.

[0113] In a specific implementation, taking the ARIMA model as a time prediction model as an example, this embodiment provides a method for performing data analysis on a data platform, which is as follows:

[0114] Use ARIMA (AutoRegressive Integrated Moving Average) to predict the amount of garbage put out. The ARIMA model is a time series forecasting method that is particularly suitable for data with trends and seasonality.

[0115] The specific steps are as follows:

[0116] S1. Historical data cleaning and preprocessing

[0117] The format of the historical data is: the time from when a trash bin is emptied to when the capacity of the trash bin reaches 10%, 20%, ... 100%. These ten time periods form a time series. (Here it can also be based on the garbage capacity of the entire site and the time corresponding to reaching 0%, 20%, ... 100% of the site capacity)

[0118] Noise removal: First, clean the historical data to remove incomplete, duplicate or obviously erroneous data records.

[0119] Standardization: Convert data in different formats into a unified standard format to facilitate subsequent analysis.

[0120] Filling missing values: For some possible missing data, reasonable filling or estimation can be performed based on historical data or other relevant data.

[0121] S2. Determine model parameters

[0122] (1) Stationarity test: Use ADF (Augmented Dickey-Fuller) and other tests to check whether the time series is stationary. If not, you may need to perform a difference operation (d parameter) until the series becomes stationary.

[0123] (2) ACF and PACF plots: Plot the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots to help determine the order of AR(p) and MA(q). Generally, a tailing ACF plot and a truncated PACF plot indicate that the AR model is suitable; conversely, a tailing PACF plot and a truncated ACF plot indicate that the MA model is suitable.

[0124] (3) Selecting optimal parameters: You can use information criteria such as AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) to find the best (p, d, q) combination through grid search or other optimization algorithms.

[0125] S3. Model fitting and verification

[0126] Train Model: Train an ARIMA model using the selected (p,d,q) parameters.

[0127] Residual analysis: Check whether the model residuals are white noise, that is, there is no obvious pattern or trend. If there is significant autocorrelation in the residuals, the model parameters may need to be adjusted.

[0128] Cross-validation: The model is cross-validated by rolling forecast or hold-out method to evaluate its predictive performance.

[0129] S4. Prediction and Anomaly Detection

[0130] The forecasts are generated using the validated ARIMA model described above.

[0131] For example, when the garbage collected by a garbage station reaches 90% of the total capacity, it is necessary to consider sending a garbage truck to collect the garbage. The time from the last time the garbage bin was emptied to the time when the capacity of the garbage bin reaches 10%, 20%, ... 60% is obtained to form a time series, and the time required for the garbage volume to reach 70%, 80%, and 90% is predicted. The time from the time it takes to reach 90% is subtracted from the time the garbage station is emptied to the current time point, which is the time from the current time to when the garbage reaches 90% of the total capacity. If this time is within the preset range (for example, 4 hours), you can consider sending a garbage truck to collect the garbage.

[0132] In another specific implementation, taking the LSTM model as a time prediction model as an example, this embodiment provides a method for performing data analysis on a data platform.

[0133] The historical data format used for training the LSTM model is the total amount of garbage at a garbage collection site after a preset time period since it was cleared, forming a historical time series about the total amount of garbage. Preferably, the preset time period can be half an hour to two hours.

[0134] The LSTM model is trained using the above historical time series to obtain a trained LSTM model.

[0135] When it is used to predict the total amount of garbage at the current garbage site, the time series of the total amount of garbage at the current garbage site obtained by the sequence processing module is input into the trained LSTM model, and it can predict the predicted total amount of garbage sequence after every preset time period after the current time point.

[0136] The first future time point in the predicted total garbage volume sequence that exceeds 90% of the total capacity of the garbage site is used as a warning point. If the difference between this warning point and the current time point is less than a preset range (e.g., 4 hours), it is possible to consider sending a garbage truck to recycle the garbage.

[0137] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0139] It should be noted that in the claims, any reference numerals placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by the same hardware. The use of the words first, second, third, etc., is for convenience of expression only and does not indicate any order. These words may be understood as part of the component name.

[0140] In addition, it should be noted that, in the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0141] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments after knowing the basic creative concept. Therefore, the claims should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0142] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include these modifications and variations.

Claims

1. A garbage collection and metering system based on RFID technology, characterized in that: include: Electronic tags and card readers; The electronic tag is set on the garbage bag used by the user, and the electronic tag pre-stores label information about the capacity information of the garbage bag; The card reader is arranged on a trash can at a trash recycling site. When a user puts a trash bag into the trash can, the card reader is used to read the tag information stored in the electronic tag on the trash bag. The data platform is used to count the amount of garbage at the garbage recycling site and perform data analysis based on the capacity information in the label information corresponding to all garbage bags.

2. The metering system according to claim 1, characterized in that: The electronic tag is a passive electronic tag, and the card reader is a radio frequency card reader. The radio frequency card reader activates the passive electronic tag by transmitting a first radio frequency signal, so that the passive electronic tag transmits a second radio frequency signal containing tag information.

3. The metering system according to claim 2, characterized in that: The card reader comprises: A controller module, configured to generate an initial first radio frequency signal; A radio frequency circuit, used for modulating an initial first radio frequency signal; An antenna, used for transmitting the modulated first radio frequency signal; as well as, When the card reader is used to receive the second radio frequency signal, The antenna is further used to receive a second radio frequency signal; The radio frequency circuit is further used to demodulate the second radio frequency signal; The controller module is also used to receive the demodulated second radio frequency signal.

4. The metering system according to claim 1, characterized in that: The label information includes: the volume capacity of the garbage bag and the type of garbage it is used to contain.

5. The metering system according to claim 4, characterized in that: The metering system also includes: The image acquisition device is arranged on the top of the trash can and is used to obtain a first image in the trash can when the card reader reads the electronic tag of a newly-added trash bag; An image analysis device is used to identify a newly thrown garbage bag in the first image based on the first image; and, based on the image of the newly thrown garbage bag, identify the fullness of the newly thrown garbage bag; and predict the predicted volume of the newly thrown garbage bag based on the fullness of the newly thrown garbage bag and the garbage type in the label information.

6. The metering system according to claim 5, characterized in that The image analysis device comprises: A first image recognition module is used to perform a subtraction between the first image and a second image of the trash can obtained when the trash bag was last put in, and to use a region where the difference is greater than a first preset value as a region of interest corresponding to the newly put in trash bag in the first image; The second image recognition module is used to predict the fullness of a newly-thrown garbage bag in the region of interest; The volume prediction module is used to determine the predicted volume of the newly-thrown garbage bag based on the volume capacity and fullness of the newly-thrown garbage bag.

7. The metering system according to claim 6, characterized in that The image analysis device also includes: An image processing module, used for performing a difference operation on the first image and the second image to obtain a difference image, and assigning zero to the pixel values ​​of the region of interest in the difference image to obtain a feature image; A third image recognition module is used to predict the predicted density of the newly-thrown garbage bag based on the feature image and the garbage type of the newly-thrown garbage bag; The weight prediction module is used to determine the predicted weight of the newly-thrown garbage bag based on the predicted density and predicted volume of the newly-thrown garbage bag.

8. The metering system according to claim 7, characterized in that: The image analysis device also includes: a fourth image recognition module, for identifying the type of garbage in a newly-added garbage bag based on the region of interest of the first image; A judgment module is used to judge whether the garbage types in the newly-thrown garbage bag only include the garbage types currently contained in the garbage bin, and if so, to determine whether the garbage classification is correct; Among them, the second image recognition module, the third image recognition module and the fourth image recognition module are respectively: deep learning models with suitable model parameters obtained through a pre-training process.

9. The metering system according to any one of claims 1 to 8, characterized in that: The data platform includes: The sequence processing module is used to obtain the total amount of garbage in all garbage bins at the garbage collection site, and to count the total amount of garbage at the garbage collection site after each preset sampling period since the last emptying, so as to form a time series of the total amount of garbage; A time prediction model, used to predict a future time point when the total amount of garbage at the garbage collection site reaches a preset value based on the time series; A scheduling module, used for determining the time to schedule the garbage truck to go to the garbage collection site based on the future time point; The time prediction model is a machine learning model with suitable model parameters obtained through a pre-training process.

10. The metering system according to claim 9, characterized in that The data platform also includes: The data preprocessing module is used to record the total amount of new garbage at the garbage recycling site based on the predicted volume and predicted weight of the newly-thrown garbage bag predicted by the image analysis device each time a new garbage bag is thrown in.

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