CN116228212A - Garbage recycling method, intelligent garbage can and garbage recycling system - Google Patents
Garbage recycling method, intelligent garbage can and garbage recycling system Download PDFInfo
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Abstract
The invention provides a garbage recycling method, an intelligent garbage can and a garbage recycling system, wherein the method comprises the steps of starting up a self-check when a user is detected to be close to the garbage, and acquiring description information of garbage to be delivered of the user; uploading the description information to an edge server, so that the edge server can identify the type of garbage to be delivered according to the description information; receiving an identification result issued by the edge server, and prompting a user to deliver the garbage to be delivered into a garbage can of a corresponding type through voice according to the identification result; and monitoring the filling data of the garbage can and uploading the filling data to the edge server, so that the edge server predicts the filling degree of the garbage can according to the filling data of the garbage can and calculates a scheduling scheme of the garbage can according to the filling degree of the garbage can, and the garbage can receives and schedules according to the scheduling scheme. The invention effectively solves the problem of garbage overflow of the garbage can.
Description
Technical Field
The invention relates to the technical field of garbage monitoring and garbage recycling, in particular to a garbage recycling method, an intelligent garbage can and a garbage recycling system.
Background
At present, the problem of garbage collection management in cities is a hot spot problem, and the first-line cities are subjected to garbage classification, so that the starting point and the purpose of the garbage classification are realized to solve the problem of garbage. However, the traditional fixed-point recycling mode through the dustbin does not meet the requirement of the era, and the traditional artificial garbage recycling scheme faces the problem that the state of the dustbin cannot be obtained in time, so that many garbage can not be recycled in time and the utilization rate of many garbage cans is low. In order to efficiently manage urban garbage, manpower, material resources and financial resources can be reduced, and some garbage recycling schemes are provided. For example, chinese patent application CN 109284867a discloses a garbage collection path optimizing system based on the internet of things, by acquiring basic information, acquiring a garbage collection path, acquiring garbage state information, calculating the number and specific gravity of garbage cans, selecting an optimizing path, resetting and returning the garbage state information. However, it has the following disadvantages: the problem of garbage overflow of the garbage can cannot be solved in time; garbage recycling vehicle can meet the condition that the dustbin is on the lane or other inconvenient garbage recycling vehicle business turn over causes the garbage recycling difficulty when retrieving rubbish, needs artificial removal to the garbage bin this moment, and garbage recycling efficiency of garbage truck is discounted greatly.
Therefore, in order to solve the above problems, there is a need for a garbage recycling scheme to achieve efficient and intelligent recycling of garbage.
Disclosure of Invention
Therefore, the embodiment of the invention provides a garbage recycling method, an intelligent garbage can and a garbage recycling system, which are used for solving the problems that the garbage overflow of a garbage can cannot be timely solved and the garbage recycling difficulty is caused by the fact that the garbage can is on a small road or other conditions inconvenient for the garbage recycling vehicle to enter and exit when the garbage recycling vehicle is used for recycling the garbage.
In order to solve the above problems, an embodiment of the present invention provides a garbage collection method, including:
s1: when the approach of the user is detected, starting up self-checking, and acquiring description information of the garbage to be delivered of the user;
s2: uploading the description information to an edge server, so that the edge server can identify the type of garbage to be delivered according to the description information;
s3: receiving an identification result issued by the edge server, and prompting a user to deliver the garbage to be delivered into a garbage can of a corresponding type through voice according to the identification result;
s4: monitoring filling data of the garbage can and uploading the filling data to an edge server, so that the edge server predicts the filling degree of the garbage can according to the filling data of the garbage can and calculates a scheduling scheme of the garbage can according to the filling degree of the garbage can, and the garbage can receives and schedules according to the scheduling scheme;
the scheduling scheme for calculating the garbage can according to the filling degree of the garbage can comprises the following steps:
when the filling degree of the garbage can is larger than a set first threshold value, the edge server sends a recycling instruction to the garbage can with the filling degree larger than the set first threshold value, searches for a nearby idle garbage can, instructs the idle garbage can to move to a position where the garbage can with the filling degree larger than the set first threshold value is located for temporary filling, and the idle garbage can is a garbage can with the filling degree smaller than the set second threshold value.
Preferably, the method for predicting the filling degree of the garbage can by the edge server according to the filling data of the garbage can specifically comprises the following steps:
data acquisition and preprocessing: collecting historical filling condition data of the garbage can, converting time series data into a format required by Prophet, and carrying out necessary missing value filling and abnormal value processing; the data is subjected to preprocessing operation, so that the data is more standard and easier to process, and the preprocessing operation comprises cleaning, normalization and smoothing;
splitting the time sequence: splitting the time series data into three parts of trend, seasonal and holiday, and carrying out logarithmic transformation to eliminate nonlinearity of the trend;
fitting a model: modeling trends, seasonality and holidays by using a Bayesian method, performing parameter optimization and model fitting, and selecting proper evaluation indexes to evaluate the performance and accuracy of the model, wherein the evaluation indexes comprise mean square error and average absolute error;
model prediction: and predicting the filling degree of the garbage can in a future period of time by using a trained time sequence model.
Preferably, the scheduling scheme further includes: when the garbage bin with the filling degree larger than the first threshold value is located at an inconvenient garbage recycling vehicle, the edge server sends an instruction to instruct the garbage bin with the filling degree larger than the first threshold value to be recycled to a proper position.
The embodiment of the invention also provides an intelligent dustbin, which comprises:
the detection module is used for detecting the starting self-check when the user approaches to the detection module and acquiring the description information of the garbage to be delivered of the user;
the identification module is used for uploading the description information to an edge server so that the edge server can identify the type of garbage to be delivered according to the description information;
the classification module is used for receiving the identification result issued by the edge server and prompting a user to deliver the garbage to be delivered into the garbage can of the corresponding type through voice according to the identification result;
the scheduling module is used for monitoring the filling data of the garbage can and uploading the filling data to the edge server so that the edge server predicts the filling degree of the garbage can according to the filling data of the garbage can and calculates a scheduling scheme of the garbage can according to the filling degree of the garbage can, and the garbage can receives and schedules according to the scheduling scheme;
the scheduling scheme for calculating the garbage can according to the filling degree of the garbage can comprises the following steps:
when the filling degree of the garbage can is larger than a set first threshold value, the edge server sends a recycling instruction to the garbage can with the filling degree larger than the set first threshold value, searches for a nearby idle garbage can, instructs the idle garbage can to move to a position where the garbage can with the filling degree larger than the set first threshold value is located for temporary filling, and the idle garbage can is a garbage can with the filling degree smaller than the set second threshold value.
Preferably, the intelligent dustbin is equipped with the garbage bin, the garbage bin includes AGV dolly and garbage bin body, and the garbage bin body sets up on the AGV dolly, the AGV dolly includes main control chip, GPS module and motor drive module, keeps away the barrier module.
The embodiment of the invention also provides a garbage recycling system, which comprises the intelligent garbage can;
the user end is used for delivering garbage to the intelligent dustbin;
the edge server is used for receiving the description information of the garbage to be delivered and the filling degree of the garbage can, which are uploaded by the intelligent garbage can, identifying the type of the garbage to be delivered according to the description information, issuing an identification result to the intelligent garbage can, predicting the filling degree of the garbage can according to the filling data of the garbage can, calculating a scheduling scheme of the garbage can according to the filling degree of the garbage can, and sending the scheduling scheme to the garbage can.
Preferably, the method further comprises: the cloud server is used for receiving the data information and the scheduling scheme of the intelligent dustbin in the edge server and sending a scheduling instruction of the man-machine interaction end to the edge server;
the man-machine interaction end is used for acquiring the data information of the intelligent dustbin and the scheduling scheme of the dustbin in the cloud server, and issuing scheduling instructions to the cloud server according to the acquired data information of the intelligent dustbin and the scheduling scheme of the dustbin.
Preferably, the cloud server is further used for processing the data information of the received intelligent dustbin, generating a heat point diagram of the use frequency of all the intelligent dustbin within a period of time, and the man-machine interaction end rearranges all the intelligent dustbin according to the heat point diagram to maximize the use rate of the intelligent dustbin.
Preferably, the edge server is further configured to, when the filling degree of the dustbin is greater than a set first threshold, perform path planning according to data information of a position where the dustbin is located, where the filling degree is greater than the set first threshold, by using an ant colony algorithm, and send the obtained planned path to the cloud server.
Preferably, the man-machine interaction end is further used for acquiring a planned path in the cloud server and commanding the garbage collection vehicle to collect garbage according to the planned path.
From the above technical scheme, the invention has the following advantages:
the embodiment of the invention provides a garbage recycling method, an intelligent garbage can and a garbage recycling system, wherein the garbage recycling method, the intelligent garbage can and the garbage recycling system are characterized in that the type of garbage to be delivered is identified by an edge server according to description information of garbage to be delivered, so that the garbage is classified, filling data of the garbage can are monitored in real time, and the garbage can with the filling degree larger than a set first threshold value is selected to temporarily fill the garbage can which is idle nearby through a particle swarm algorithm, so that the problem that the existing garbage can cannot be timely solved when overflows is solved, and meanwhile, when the garbage can is inconvenient to recycle in the position of the garbage can, the garbage can move directionally to reach a designated place to wait for the garbage recycling vehicle. In addition, the distribution of the intelligent dustbin can be regularly adjusted through the hot spot diagrams of the use frequencies of the dustbin at different positions, so that the distribution of the intelligent dustbin is more reasonable. The method is based on the internet of things technology, and is more intelligent compared with the existing method.
Drawings
For a clearer description of embodiments of the invention or of solutions in the prior art, reference will be made to the accompanying drawings, which are intended to be used in the examples, for a clearer understanding of the characteristics and advantages of the invention, by way of illustration and not to be interpreted as limiting the invention in any way, and from which, without any inventive effort, a person skilled in the art can obtain other figures. Wherein:
FIG. 1 is a flow chart of a method of garbage collection according to an embodiment;
FIG. 2 is a schematic diagram of a waste reclamation system provided in accordance with an embodiment;
FIG. 3 is a thermal diagram of the frequency of use of the intelligent dustbin;
fig. 4 is a schematic diagram of a planned path calculated by using an ant colony algorithm;
FIG. 5 is a schematic view of the trash can balance and the position of each trash can;
FIG. 6 is a schematic view of a recycling path of a trash can;
fig. 7 is a schematic view of the working state of the trash can.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides a garbage collection method, which includes:
s1: when the approach of the user is detected, starting up self-checking, and acquiring description information of the garbage to be delivered of the user;
s2: uploading the description information to an edge server, so that the edge server can identify the type of garbage to be delivered according to the description information;
s3: receiving an identification result issued by the edge server, and prompting a user to deliver the garbage to be delivered into a garbage can of a corresponding type through voice according to the identification result;
s4: monitoring filling data of the garbage can and uploading the filling data to an edge server, so that the edge server predicts the filling degree of the garbage can according to the filling data of the garbage can and calculates a scheduling scheme of the garbage can according to the filling degree of the garbage can, and the garbage can receives and schedules according to the scheduling scheme;
the scheduling scheme for calculating the garbage can according to the filling degree of the garbage can comprises the following steps:
when the filling degree of the garbage can is larger than a set first threshold value, the edge server sends a recycling instruction to the garbage can with the filling degree larger than the set first threshold value, searches for a nearby idle garbage can, instructs the idle garbage can to move to a position where the garbage can with the filling degree larger than the set first threshold value is located for temporary filling, and the idle garbage can is a garbage can with the filling degree smaller than the set second threshold value.
The invention provides a garbage recycling method, which is characterized in that the type of garbage to be delivered is identified by an edge server according to description information of the garbage to be delivered by acquiring the description information of a user, so that the garbage is classified, filling data of the garbage can are monitored in real time, and the garbage can with the filling degree larger than a set first threshold value is selected to be temporarily repaired by a particle swarm algorithm for the garbage can with idle vicinity, so that the problem that the existing garbage can cannot be timely solved when overflowed is solved, and meanwhile, when the garbage can is inconvenient to recycle by a garbage recycling vehicle in the position, the garbage can move directionally to reach a designated place to wait for the garbage recycling vehicle. In addition, the distribution of the intelligent dustbin can be regularly adjusted through the hot spot diagrams of the use frequencies of the dustbin at different positions, so that the distribution of the intelligent dustbin is more reasonable. The method is based on the internet of things technology, and is more intelligent compared with the existing method.
Further, when the user approaches, the intelligent dustbin detects the user, the intelligent dustbin starts up self-checking (is in a dormant state when not working, if the intelligent dustbin breaks down, the intelligent dustbin lights a red light and records the time at the moment, and the intelligent dustbin can still be opened through a pedal), and the intelligent dustbin acquires the description information of the user to-be-delivered garbage.
Further, the description information acquired by the intelligent garbage can is uploaded to an edge server, so that the edge server can identify the type of garbage to be delivered according to the description information. The edge server is deployed with a trained convolutional neural network model, and the trained convolutional neural network model is used for identifying and classifying garbage to be delivered according to the acquired data information. The specific training process of the trained convolutional neural network model comprises the steps of firstly, carrying out preprocessing operation on the convolutional neural network model by an edge server according to description information acquired by an intelligent garbage can, wherein the preprocessing operation comprises image enhancement, image segmentation, feature extraction and the like. The contrast and brightness of the image can be improved by image enhancement, so that the image is more suitable for subsequent processing; the garbage can and the garbage can be separated by image segmentation, so that the garbage can and the garbage can be conveniently processed later; the feature extraction can extract the features of the garbage can and garbage for subsequent classification and prediction. And inputting the preprocessed data information into a convolutional neural network for training, and finally obtaining a trained convolutional neural network model.
Specifically, we use a mode of transfer learning to import a pre-trained neural network model, freeze a feature extraction layer, perform fine tuning training, select four neural network models of SeNet154, se_ResNet50, se_ResNext101 and ResNext101_32x16d_WSL to perform a comparison experiment, and then perform tuning on the model with a good selection result. In order to accurately and objectively evaluate the migration learning of four pre-trained neural network models on the garbage picture data, a graph of the variation of Accurcy and Loss along with the iteration times on a training set and a testing set is adopted for comparison analysis. Finally we train to generate neural network models using the ResNext101_32x16d_wsl model.
Further, the intelligent dustbin receives the identification result issued by the edge server, and prompts a user to deliver the garbage to be delivered to the garbage bin of the corresponding type through voice according to the identification result.
Further, the intelligent dustbin monitors filling data of the dustbin in real time and uploads the filling data to the edge server, so that the edge server predicts the filling degree of the dustbin according to the filling data of the dustbin and calculates a scheduling scheme of the dustbin according to the filling degree of the dustbin.
Specifically, the edge server predicts the filling degree of the garbage can according to the filling data of the garbage can uploaded by the intelligent garbage can, and specifically comprises the following steps: data acquisition and preprocessing: collecting historical filling condition data of the garbage can, converting time series data into a format required by Prophet, and carrying out necessary missing value filling and abnormal value processing; the data is subjected to preprocessing operation, so that the data is more standard and easier to process, and the preprocessing operation comprises cleaning, normalization and smoothing; splitting the time sequence: splitting the time series data into three parts of trend, seasonal and holiday, and carrying out logarithmic transformation to eliminate nonlinearity of the trend; fitting a model: modeling trends, seasonality and holidays by using a Bayesian method, performing parameter optimization and model fitting, and selecting proper evaluation indexes to evaluate the performance and accuracy of the model, wherein the evaluation indexes comprise mean square error and average absolute error; model prediction: and predicting the filling degree of the garbage can in a future period of time by using a trained time sequence model.
Calculating according to the filling degree of the garbage can to obtain a scheduling scheme of the garbage can, wherein the scheduling scheme comprises the following steps: when the filling degree of the garbage can is larger than a set first threshold (70%), the edge server sends a recycling instruction to the garbage can with the filling degree larger than the set first threshold, and searches for a nearby idle garbage can by utilizing a particle swarm algorithm, and instructs the idle garbage can to move to a position where the garbage can with the filling degree larger than the set first threshold is located for temporary filling, wherein the idle garbage can is a garbage can with the filling degree smaller than a set second threshold (30%). The garbage bin receives and schedules according to a scheduling scheme.
When the garbage bin with the filling degree larger than the first threshold value is located at an inconvenient garbage recycling vehicle, the edge server sends an instruction to instruct the garbage bin with the filling degree larger than the first threshold value to be recycled to a proper position.
The embodiment of the invention also provides an intelligent dustbin, which comprises:
the detection module is used for detecting the starting self-check when the user approaches to the detection module and acquiring the description information of the garbage to be delivered of the user;
the identification module is used for uploading the description information to an edge server so that the edge server can identify the type of garbage to be delivered according to the description information;
the classification module is used for receiving the identification result issued by the edge server and prompting a user to deliver the garbage to be delivered into the garbage can of the corresponding type through voice according to the identification result;
the scheduling module is used for monitoring the filling data of the garbage can and uploading the filling data to the edge server so that the edge server predicts the filling degree of the garbage can according to the filling data of the garbage can and calculates a scheduling scheme of the garbage can according to the filling degree of the garbage can, and the garbage can receives and schedules according to the scheduling scheme;
the scheduling scheme for calculating the garbage can according to the filling degree of the garbage can comprises the following steps:
when the filling degree of the garbage can is larger than a set first threshold value, the edge server sends a recycling instruction to the garbage can with the filling degree larger than the set first threshold value, searches for a nearby idle garbage can, instructs the idle garbage can to move to a position where the garbage can with the filling degree larger than the set first threshold value is located for temporary filling, and the idle garbage can is a garbage can with the filling degree smaller than the set second threshold value.
Specifically, a plurality of garbage cans are arranged in the intelligent garbage can so as to recycle garbage of different types, the garbage can comprises an AGV trolley and a garbage can body, the garbage can body is arranged on the AGV trolley, and the AGV trolley comprises a main control chip, a GPS module, a motor driving module and an obstacle avoidance module, so that the AGV trolley has the capabilities of autonomous movement, obstacle avoidance and the like. The AGV trolley body is made of plastic, has certain use strength, is provided with 4 direct-current gear motors, and has good directivity, four-wheel drive and abundant power. The working current of each motor is about 80mA, the working voltage is 3-6V, and the fastest speed is 48m/min. In addition, the AGV is provided with an STM32 singlechip as a main control board, and an RTOS embedded system is carried, so that the operation efficiency of the AGV is improved in a multithreading mode. The main control part has the functions of: the Lora module is adopted for communication, so that the communication device has the characteristic of low power consumption, and compared with a 4G-iot module, the communication device can also remarkably reduce the cost. The automatic obstacle avoidance module uses an ultrasonic distance sensor, and when an obstacle incapable of crossing appears in front of the AGV, the automatic obstacle avoidance module can trigger the automatic obstacle avoidance function of the main control board. And after the positioning information is acquired by the GSM communication module carried by the AGV, the longitude and latitude information can be sent by the Lora module. The motor driving module comprises a 3S battery for a model airplane, the full voltage is about 12.5V, and an L298N motor driving plate is used for driving the AGV trolley to move. L298N is used as a main control chip, has the characteristics of strong driving capability, low heat productivity, strong anti-interference capability and the like, has the working voltage of up to 48V, and can conveniently adopt various types of lithium batteries for power supply; the instantaneous peak value of the output current reaches 3A, the continuous working current is 2A, the rated power is 25W, and the power consumption is within the allowable range, so that the motor of the AGV is driven. The inside adopts standard logic level control signal, has two enabling ends, and two ports independent operation do not interfere each other for AGV dolly control interference resistance reinforcing.
The solar panel is arranged at the top of the intelligent dustbin, and the storage battery connected with the bottom supplies power to the whole intelligent dustbin; when the electric quantity of the battery is insufficient, the electric quantity can be obtained from the street lamp through the cable. Install the interface that charges in the intelligent dustbin and can charge for the AGV dolly. The intelligent dustbin is equipped with a human body sensor, a camera module, a communication module, an ultrasonic ranging sensor and the like, and is equipped with a HarmonyOS system. The human body sensor is used for detecting a human body, and the camera module is embedded in the intelligent dustbin and arranged above the dustbin, so that the camera module can shoot real-time photos of the dustbin; the camera module device adopts an ESP32-CAM module, and the ESP32-CAM is a very small camera module based on an ESP32-S chip. Using the ESP32-CAM module, the image recognition system can be built without using any complex procedures and any additional components. The ESP32-CAM module is equipped with an ESP32-S chip, an OV2640 camera of ultra-small size and a MicroSD card slot. The MicroSD card slot may be used to store images taken from a camera or to store files. The ESP32-CAM module may be used in a wide variety of IoT applications. The communication module is used for communicating with the edge server, the ultrasonic ranging sensor is used for acquiring filling data of the garbage can, and the ultrasonic ranging sensor cannot accurately acquire the data of the garbage with uneven height. Firstly, using an edge detection technology to separate out garbage with too high and too low height, calculating the approximate filling degree of the garbage can through a vertical projection algorithm, and finally uploading the garbage can by combining data measured by an ultrasonic ranging sensor.
The intelligent dustbin is used for realizing the garbage recycling method, and in order to avoid redundancy, the description is omitted here.
As shown in fig. 2, the present invention provides a garbage recycling system, which includes the above-mentioned intelligent garbage can 200:
the user terminal 100 is configured to deliver garbage to the intelligent dustbin;
the edge server 300 is configured to receive the description information of the to-be-delivered garbage uploaded by the intelligent dustbin and the filling degree of the dustbin, identify the type of the to-be-delivered garbage according to the description information, send the identification result to the intelligent dustbin, predict the filling degree of the dustbin according to the filling data of the dustbin, calculate to obtain a scheduling scheme of the dustbin according to the filling degree of the dustbin, and send the scheduling scheme to the dustbin.
The cloud server 400 is configured to receive data information and a scheduling scheme of the intelligent dustbin in the edge server, and send a scheduling instruction of the man-machine interaction end to the edge server;
the man-machine interaction end 500 is configured to obtain data information of the intelligent dustbin and a scheduling scheme of the dustbin in the cloud server, and issue a scheduling instruction to the cloud server according to the obtained data information of the intelligent dustbin and the scheduling scheme of the dustbin.
Further, the cloud server is further used for processing the received data information of the intelligent dustbin, generating a heat point diagram of the use frequency of all the intelligent dustbin within a period of time, and the man-machine interaction end rearranging all the intelligent dustbin according to the heat point diagram to maximize the use rate of the intelligent dustbin. As shown in fig. 3, the frequency distribution diagram of the use of the intelligent dustbin is shown (the larger the black dots in the figure are, the higher the use frequency is).
Further, when the filling degree of the dustbin is greater than the set first threshold, the edge server is further configured to perform path planning according to data information of the position of the dustbin where the filling degree is greater than the set first threshold and using an ant colony algorithm, and send the obtained planned path to the cloud server.
Specifically, the path planning by using the ant colony algorithm specifically comprises the following steps:
(1) initializing a parameter
The parameters such as ant number m, pheromone factor alpha, heuristic function factor beta, pheromone volatilization factor rou, pheromone constant Q, maximum iteration number t and the like need to be initialized at the calculation place. Wherein the number m of ants is 1.5 times of the number of the garbage cans in the area; the pheromone constant Q takes a value of 50; the maximum iteration number t takes a value of 200; the value of the pheromone factor alpha is between [1,4 ]; the value range of the heuristic function factor beta is generally between [0,5 ]; the pheromone volatilization factor rou is generally in the range of 0.2 and 0.5.
(2) Construction of solution space
The ants are then placed at different departure points, and for each ant k (k belongs to 1 to m), the next city to be visited is calculated until each ant has visited all cities. Ants use roulette to select the next city to arrive at each step of the construction path. The probability of selecting each path is expressed as:
where i and j represent the start and end of each path, respectively, tao represents the pheromone concentration from i to j at time t, the value of yita is equal to the inverse of the path length d, and allowedk represents the set of unviewed nodes.
(3) Updating pheromones
Calculating the path length L of each ant passing by, and recording the historical optimal solution in the current iteration number, namely the shortest path; at the same time, the method comprises the steps of,
updating pheromone concentration of paths connected by each city
The expression for the pheromone update is:
τ ij (t+1)＝τ ij (t)*(1-ρ)+Aτ ij ，0＜ρ＜1
wherein, the liquid crystal display device comprises a liquid crystal display device,
that is: the pheromone content from city i to city j after the (t+1) th cycle is equal to the pheromone content from city i to city j after the (t) th cycle multiplied by the pheromone residual coefficient and added with the newly added pheromone content, wherein the newly added pheromone content can be expressed as the sum of the pheromones left by all ants on the path from city i to city j.
(4) Judging whether or not the termination condition is reached
The termination conditions of the ant colony algorithm are: and judging whether the maximum iteration times are reached.
The final planned path is obtained by the ant colony algorithm described above, as shown in fig. 4.
In addition, in the garbage collection system, the man-machine interaction end uses a WeChat applet developer tool to develop a ui interface. Finally, the residue of the garbage can and the positions of the garbage cans are monitored in real time by utilizing a small program at the man-machine interaction end, as shown in fig. 5. Fig. 6 shows a recycling path of the trash can, and fig. 7 shows an operating state of the trash can.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (10)
1. A method of recycling waste comprising:
s1: when the approach of the user is detected, starting up self-checking, and acquiring description information of the garbage to be delivered of the user;
s2: uploading the description information to an edge server, so that the edge server can identify the type of garbage to be delivered according to the description information;
s3: receiving an identification result issued by the edge server, and prompting a user to deliver the garbage to be delivered into a garbage can of a corresponding type through voice according to the identification result;
s4: monitoring filling data of the garbage can and uploading the filling data to an edge server, so that the edge server predicts the filling degree of the garbage can according to the filling data of the garbage can and calculates a scheduling scheme of the garbage can according to the filling degree of the garbage can, and the garbage can receives and schedules according to the scheduling scheme;
the scheduling scheme for calculating the garbage can according to the filling degree of the garbage can comprises the following steps:
when the filling degree of the garbage can is larger than a set first threshold value, the edge server sends a recycling instruction to the garbage can with the filling degree larger than the set first threshold value, searches for a nearby idle garbage can, instructs the idle garbage can to move to a position where the garbage can with the filling degree larger than the set first threshold value is located for temporary filling, and the idle garbage can is a garbage can with the filling degree smaller than the set second threshold value.
2. The garbage collection method according to claim 1, wherein the method for predicting the filling degree of the garbage can by the edge server according to the filling data of the garbage can specifically comprises:
data acquisition and preprocessing: collecting historical filling condition data of the garbage can, converting time series data into a format required by Prophet, and carrying out necessary missing value filling and abnormal value processing; the data is subjected to preprocessing operation, so that the data is more standard and easier to process, and the preprocessing operation comprises cleaning, normalization and smoothing;
splitting the time sequence: splitting the time series data into three parts of trend, seasonal and holiday, and carrying out logarithmic transformation to eliminate nonlinearity of the trend;
fitting a model: modeling trends, seasonality and holidays by using a Bayesian method, performing parameter optimization and model fitting, and selecting proper evaluation indexes to evaluate the performance and accuracy of the model, wherein the evaluation indexes comprise mean square error and average absolute error;
model prediction: and predicting the filling degree of the garbage can in a future period of time by using a trained time sequence model.
3. The garbage collection method according to claim 1, wherein the scheduling scheme further comprises: when the garbage bin with the filling degree larger than the first threshold value is located at an inconvenient garbage recycling vehicle, the edge server sends an instruction to instruct the garbage bin with the filling degree larger than the first threshold value to be recycled to a proper position.
4. An intelligent dustbin, characterized by comprising:
the detection module is used for detecting the starting self-check when the user approaches to the detection module and acquiring the description information of the garbage to be delivered of the user;
the identification module is used for uploading the description information to an edge server so that the edge server can identify the type of garbage to be delivered according to the description information;
the classification module is used for receiving the identification result issued by the edge server and prompting a user to deliver the garbage to be delivered into the garbage can of the corresponding type through voice according to the identification result;
the scheduling module is used for monitoring the filling data of the garbage can and uploading the filling data to the edge server so that the edge server predicts the filling degree of the garbage can according to the filling data of the garbage can and calculates a scheduling scheme of the garbage can according to the filling degree of the garbage can, and the garbage can receives and schedules according to the scheduling scheme;
the scheduling scheme for calculating the garbage can according to the filling degree of the garbage can comprises the following steps:
when the filling degree of the garbage can is larger than a set first threshold value, the edge server sends a recycling instruction to the garbage can with the filling degree larger than the set first threshold value, searches for a nearby idle garbage can, instructs the idle garbage can to move to a position where the garbage can with the filling degree larger than the set first threshold value is located for temporary filling, and the idle garbage can is a garbage can with the filling degree smaller than the set second threshold value.
5. The intelligent dustbin of claim 4, wherein the intelligent dustbin is provided with a dustbin, the dustbin comprises an AGV trolley and a dustbin body, the dustbin body is arranged on the AGV trolley, and the AGV trolley comprises a main control chip, a GPS module, a motor driving module and an obstacle avoidance module.
6. A waste recycling system comprising the intelligent dustbin of claim 5;
the user end is used for delivering garbage to the intelligent dustbin;
the edge server is used for receiving the description information of the garbage to be delivered and the filling degree of the garbage can, which are uploaded by the intelligent garbage can, identifying the type of the garbage to be delivered according to the description information, issuing an identification result to the intelligent garbage can, predicting the filling degree of the garbage can according to the filling data of the garbage can, calculating a scheduling scheme of the garbage can according to the filling degree of the garbage can, and sending the scheduling scheme to the garbage can.
7. The waste reclamation system as recited in claim 6, further comprising:
the cloud server is used for receiving the data information and the scheduling scheme of the intelligent dustbin in the edge server and sending a scheduling instruction of the man-machine interaction end to the edge server;
the man-machine interaction end is used for acquiring the data information of the intelligent dustbin and the scheduling scheme of the dustbin in the cloud server, and issuing scheduling instructions to the cloud server according to the acquired data information of the intelligent dustbin and the scheduling scheme of the dustbin.
8. The garbage collection system according to claim 7, wherein the cloud server is further configured to process the data information of the received intelligent garbage can, generate a heat map of the usage frequency of all intelligent garbage cans within a period of time, and the man-machine interaction end rearranges all intelligent garbage cans according to the heat map, so as to maximize the usage rate of the intelligent garbage cans.
9. The garbage collection system according to claim 6 or 7, wherein the edge server is further configured to, when the filling degree of the garbage can is greater than the set first threshold, perform path planning according to data information of a position where the garbage can with the filling degree greater than the set first threshold and using an ant colony algorithm, and send the obtained planned path to the cloud server.
10. The garbage collection system of claim 9, wherein the man-machine interaction end is further configured to obtain a planned path in the cloud server, and instruct the garbage collection vehicle to collect garbage according to the planned path.
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| CN117094534B (en) * | 2023-10-19 | 2024-01-23 | 浩博泰德(北京)科技有限公司 | Intelligent control method and system for Internet of things |
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