Garbage collection and transportation management method and garbage collection and transportation management system

Abstract

The invention discloses a garbage collection and transportation management method and a garbage collection and transportation management system, and belongs to the technical field of intelligent city management, and the method comprises the steps: carrying out the real-time monitoring of a plurality of garbage cans at a garbage collection point through a pre-arranged sensing unit, and obtaining the monitoring information of a plurality of garbage cans; according to the multiple pieces of garbage can monitoring information, overflow detection is conducted, and multiple overflow evaluation coefficients are obtained; calculating the number of overflowed garbage cans according to the plurality of overflowed evaluation coefficients; if the number of the overflowed garbage cans is larger than or equal to the overflowed garbage can number threshold value, a garbage collection and transportation instruction is generated; and based on the garbage collection and transportation instruction, the multiple pieces of garbage can monitoring information are sent to a garbage collection and transportation management center, and a garbage collection and transportation management scheme is generated. The technical problems that in the prior art, a garbage collection and transportation mode depends on manual monitoring or single sensor monitoring, so that garbage can state monitoring is not accurate, and collection and transportation scheduling efficiency is low are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent city management, and in particular to a garbage collection and transportation management method and a garbage collection and transportation management system. Background Art

[0002] With the acceleration of urbanization, the amount of urban domestic waste is increasing year by year, and waste collection and transportation management has become an important part of urban management. However, traditional waste collection and transportation methods mainly rely on fixed-time collection and transportation plans or manual inspections. Not only is the collection and transportation frequency not flexible enough and difficult to adapt to the volatility of waste production, it is also easy to cause problems such as overflowing garbage bins or waste of collection and transportation resources. This extensive management model is inefficient and cannot meet the needs of modern cities for efficient and intelligent waste management.

[0003] The existing garbage collection and transportation system usually relies on manual inspections or single sensor detection to determine the status of the garbage bin, which has the defects of incomplete data collection and low monitoring accuracy. In addition, the scheduling decision mainly relies on manual experience and lacks scientific data support, which is prone to problems such as uneven scheduling or delayed transportation, resulting in frequent garbage overflow and pollution of the environment or empty garbage trucks wasting resources. Therefore, there is an urgent need for a garbage collection and transportation management method based on multi-dimensional data collection and intelligent evaluation to monitor the status of the garbage bin in real time and generate an efficient and reasonable garbage collection and transportation plan. Summary of the invention

[0004] The present application provides a garbage collection and transportation management method and a garbage collection and transportation management system, aiming to solve the technical problem in the prior art that the garbage collection and transportation mode relies on manual or single sensor monitoring, resulting in inaccurate garbage bin status monitoring and low collection and transportation scheduling efficiency.

[0005] In view of the above problems, the present application provides a garbage collection and transportation management method.

[0006] The first aspect disclosed in the present application provides a garbage collection and transportation management method, which includes real-time monitoring of multiple garbage bins at a garbage collection point through pre-deployed sensor units to obtain monitoring information of multiple garbage bins; performing overflow detection based on the monitoring information of the multiple garbage bins to obtain multiple overflow assessment coefficients; calculating the number of overflowing garbage bins based on the multiple overflow assessment coefficients; generating a garbage collection and transportation instruction if the number of overflowing garbage bins is greater than or equal to a threshold value of the number of overflowing garbage bins; and sending the monitoring information of the multiple garbage bins to a garbage collection and transportation management center based on the garbage collection and transportation instruction to generate a garbage collection and transportation management plan.

[0007] Another aspect disclosed in the present application provides a garbage collection and transportation management system, which includes a real-time monitoring module for real-time monitoring of multiple garbage bins at a garbage collection point through pre-deployed sensor units to obtain monitoring information of multiple garbage bins; an overflow detection module for performing overflow detection based on the monitoring information of the multiple garbage bins to obtain multiple overflow assessment coefficients; a quantity calculation module for calculating the number of overflowing garbage bins based on the multiple overflow assessment coefficients; an instruction generation module for generating a garbage collection and transportation instruction if the number of overflowing garbage bins is greater than or equal to a threshold value of the number of overflowing garbage bins; and a plan generation module for sending the monitoring information of the multiple garbage bins to a garbage collection and transportation management center based on the garbage collection and transportation instruction to generate a garbage collection and transportation management plan.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The invention adopts a technical solution of real-time monitoring of multiple garbage bins at garbage collection points through pre-deployed sensor units, generating garbage collection and transportation instructions according to the overflow assessment coefficient and sending them to the garbage collection and transportation management center to generate a collection and transportation management plan. This solves the technical problem that the garbage collection and transportation mode in the prior art relies on manual or single sensor monitoring, resulting in inaccurate garbage bin status monitoring and low collection and transportation scheduling efficiency, and achieves the technical effect of improving the accuracy of garbage bin status monitoring, optimizing garbage collection and transportation scheduling efficiency and reducing resource waste.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flowchart of a garbage collection and transportation management method is provided for an embodiment of the present application;

[0012] Figure 2 A structural schematic diagram of a garbage collection and transportation management system is provided for an embodiment of the present application.

[0013] Explanation of the accompanying drawings: real-time monitoring module 11, overflow detection module 12, quantity calculation module 13, instruction generation module 14, solution generation module 15. DETAILED DESCRIPTION

[0014] The overall idea of ​​the technical solution provided by this application is as follows:

[0015] The embodiment of the present application provides a method for garbage collection and transportation management. By pre-deploying sensor units to monitor the status of garbage bins in real time, collecting multi-dimensional data and performing overflow detection, a garbage collection and transportation instruction is generated. When the number of overflowing garbage bins reaches a set threshold, the system sends the monitoring information to the garbage collection and transportation management center, and generates a garbage collection and transportation management plan that includes classification management and scheduling optimization.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0017] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a garbage collection and transportation management method, the method comprising:

[0018] Step S100: Real-time monitoring of multiple garbage bins at a garbage collection point is performed through pre-deployed sensor units to obtain monitoring information of multiple garbage bins.

[0019] Specifically, a garbage collection point refers to a specific location in a city where multiple garbage bins are placed, such as residential areas, shopping malls, office buildings, etc. Garbage bin monitoring information refers to a multi-dimensional data set about the status of the garbage bin obtained through a sensor unit.

[0020] Sensor units are installed on multiple trash cans at the garbage collection point. Each sensor unit collects the status information of the trash can in real time through the built-in sensor. Specifically, the position sensor records the location of the trash can and uploads it to the background system; the weight sensor measures the real-time weight of the garbage in the can through the weighing principle. For example, the current weight of a trash can is 50kg, which is close to its full load state; the temperature and humidity sensor measures the environment inside the trash can. For example, if the current temperature is 35℃ and the humidity is 70%, it indicates that odor may be generated; the video monitoring equipment captures the image of the trash can at a fixed time interval and uploads it to the server through the wireless transmission module. These sensors together form a multi-dimensional monitoring network to form comprehensive trash can monitoring information.

[0021] In order to achieve efficient monitoring, wireless transmission technology (such as NB-IoT, LoRa or 4G network) is combined to upload data to the garbage collection and transportation management center in real time. The management center summarizes the monitoring information of the garbage bin through the data receiving module for subsequent analysis and processing.

[0022] Through real-time monitoring by the sensor unit, the status information of the trash can can be accurately and comprehensively obtained, providing data support for the garbage management system. This step not only avoids the inefficiency of manual inspections, but also improves the accuracy of garbage can overflow judgment through multi-dimensional data, reduces the pollution of garbage overflow to the environment, and lays the foundation for subsequent garbage collection and transportation scheduling, significantly improving the efficiency and intelligence level of garbage management.

[0023] Step S200: performing overflow detection according to the monitoring information of the plurality of trash cans to obtain a plurality of overflow assessment coefficients.

[0024] Specifically, overflow detection is used to determine whether the trash can is close to being full or overflowing. The overflow assessment coefficient is used to quantify the assessment value of the trash can status, usually a number ranging from 0 to 1 (or other custom ranges). The closer the value is to 1, the closer the trash can is to overflowing.

[0025] First, the system receives the trash bin monitoring information uploaded by the sensor unit, including weight, image, temperature and humidity data. Based on this data, features related to the overflow state are extracted. For example, the reading of the weight sensor is used to quantify the filling degree of the trash in the bin; the image captured by the video surveillance device is processed to detect the height ratio of the trash in the trash bin. At the same time, the reading of the temperature and humidity sensor can be used to assist in judging the odor that may be generated by the garbage accumulation for too long.

[0026] After data preprocessing, the system inputs key features into a pre-trained overflow assessment model, such as an image classification model based on a convolutional neural network (CNN). The model is trained to output an overflow assessment coefficient based on the image of the trash can, and combines the weight and other data to calculate a comprehensive assessment value.

[0027] Through overflow detection of garbage bin monitoring information, multiple overflow assessment coefficients can be obtained quickly and accurately, providing a scientific basis for priority judgment and task allocation of garbage collection and transportation. This step combines multi-dimensional data to avoid the misjudgment problem that may arise from relying solely on a single data source, greatly improving the accuracy of garbage status assessment.

[0028] Step S300: Calculate the number of overflowing trash cans according to the multiple overflow assessment coefficients.

[0029] Specifically, first, the system obtains the overflow assessment coefficients of all garbage bins from the monitoring results. These assessment coefficients are calculated by the overflow assessment model in the previous step. For example, a garbage collection point contains 5 garbage bins, and their overflow assessment coefficients are 0.6, 0.85, 0.75, 0.9, and 0.65 respectively.

[0030] Next, set an overflow assessment threshold to distinguish whether the trash can is overflowing. The threshold can be flexibly set according to the specific scenario, such as the default setting of 0.8. When the overflow assessment coefficient of a trash can is greater than or equal to the threshold, the system marks the trash can as an overflowing trash can. For example, in the above example, the trash cans with assessment coefficients of 0.85 and 0.9 are marked as overflowing trash cans, and the rest of the trash cans have not reached the overflow state.

[0031] Finally, count all the bins marked as overflowing to get the total number of overflowing bins. The system can use a simple traversal algorithm to compare the evaluation coefficients one by one, or use matrix operations to quickly filter out overflowing bins. For example, use Python's NumPy tool to use conditional filtering statements to filter out all overflowing bins and quickly calculate the number.

[0032] By calculating the overflow assessment coefficient and determining the threshold, the number of overflowing garbage bins can be accurately counted. This method has the characteristics of clear logic and efficient calculation. Combined with the automation system, it can provide real-time overflow status distribution information of garbage collection points, providing a scientific basis for garbage removal and dispatching.

[0033] Step S400: If the number of overflowing trash cans is greater than or equal to the threshold number of overflowing trash cans, a garbage collection and transportation instruction is generated.

[0034] Specifically, the number of overflowing trash cans is obtained from the previous link and compared with the preset threshold of the number of overflowing trash cans. The system finds through logical judgment that the number of overflowing trash cans has reached or exceeded the threshold, triggering the conditions for generating garbage collection and transportation instructions.

[0035] In the process of generating instructions, the system will integrate relevant information about overflowing trash cans, such as location, overflow degree, type of trash can, etc. This information can be directly called through the data collected by the sensor unit. In addition, the instruction generation tool can use the task management module to automatically send instructions to the garbage collection and transportation management center or the corresponding garbage collection and transportation vehicles. In order to improve reliability, the system can design multi-level judgment logic. For example, when the number of overflowing trash cans in the area where a trash can is located reaches a threshold, the system will further check whether there is a risk of garbage overflow, whether it is during the peak collection period, etc. Only when the additional conditions are met can the garbage collection and transportation instructions be finally confirmed and sent.

[0036] By comparing the number of overflowing trash cans with the preset threshold, the system can accurately identify the scene that needs to be cleared and automatically generate garbage collection and transportation instructions when the conditions are met. This method has the advantages of clear logic and efficient operation, and can significantly shorten the response time from trash can overflow detection to garbage collection task arrangement.

[0037] Step S500: Based on the garbage collection and transportation instruction, the monitoring information of the multiple garbage bins is sent to a garbage collection and transportation management center to generate a garbage collection and transportation management plan.

[0038] Specifically, after generating the garbage collection and transportation instructions, the system sends the relevant garbage bin monitoring information to the garbage collection and transportation management center. The garbage collection and transportation management center is a management system or platform responsible for the scheduling and optimization of garbage collection and transportation tasks, integrating monitoring information to generate specific collection and transportation management plans. Garbage bin monitoring information usually includes location data (latitude and longitude or area description), overflow degree (assessment coefficient or status identification), garbage type (kitchen waste, recyclables, etc.) and real-time images. The management center integrates this data through the receiving module and starts the garbage collection and transportation management model to generate a plan.

[0039] The garbage collection and transportation management model usually consists of two parts: the garbage classification management model and the garbage collection and transportation scheduling model. The garbage classification management model analyzes the classification information of the garbage bins (such as the overflowing status of the kitchen waste bins) and outputs specific classification and transportation tasks. For example, "give priority to the transportation of kitchen waste bins to ensure accurate classification". The garbage collection and transportation scheduling model comprehensively considers the location of the garbage bins, the number of overflowing garbage bins, the location of the transportation vehicles and the vehicle capacity, etc., to generate the optimal collection and transportation route and task allocation plan. For example, "Vehicle No. 05 departs from the base, clears the kitchen waste bins in community A, and then goes to shopping mall B to complete the transportation task of recyclable garbage."

[0040] During the plan generation process, GIS (Geographic Information System) technology can be used to optimize the collection and transportation routes, and real-time traffic data and garbage bin location data can be used to reduce collection and transportation time; at the same time, the scheduling model based on AI algorithms can automatically balance the task load to ensure collection and transportation efficiency. The final generated garbage collection and transportation management plan will be automatically assigned to the terminal equipment of the collection and transportation vehicle for the driver to perform the task.

[0041] By integrating garbage bin monitoring information with garbage collection and transportation instructions, sending them to the garbage collection and transportation management center and generating a garbage collection and transportation management plan, the scientificity and efficiency of garbage collection and transportation can be significantly improved.

[0042] Furthermore, overflow detection is performed based on the multiple trash can monitoring information to obtain multiple overflow assessment coefficients, including: extracting multiple trash can monitoring images based on the multiple trash can monitoring information; building an overflow assessment model based on a convolutional neural network; inputting the multiple trash can monitoring images into the overflow assessment model to generate the multiple overflow assessment coefficients.

[0043] Specifically, trash can monitoring images refer to trash can images collected by video surveillance equipment, which reflect information such as the filling status and type of trash in the trash can. Convolutional neural network (CNN) is a deep learning algorithm commonly used in image processing, which can automatically extract image features for classification or detection tasks. The overflow assessment model is a specific model trained based on convolutional neural network, which is used to analyze trash can images and generate overflow assessment coefficients.

[0044] First, video surveillance equipment is required to collect monitoring images of trash cans in real time to form an image data set. For example, 10 trash cans in a community upload images every 10 minutes, and the images show the current accumulation status of the trash in the cans. The system preprocesses the collected monitoring images, including denoising, graying, scaling and other operations to ensure that the image quality is suitable for model analysis. Next, a convolutional neural network (CNN) is used to build an overflow assessment model. The specific method includes: collecting a large number of trash can image sample sets, annotating the sample images, recording the actual overflow degree label corresponding to each image, and using supervised learning methods to train the CNN model. CNN extracts image features through multiple convolutional layers, such as key features such as the height, position, and shape of the trash inside the trash can, and finally outputs the overflow assessment value. When the overflow assessment model is built, the collected trash can monitoring images are input into the model, and the model generates an overflow assessment coefficient through feature extraction and classification operations.

[0045] By extracting garbage bin monitoring images and performing overflow detection based on convolutional neural networks, a high-precision quantitative assessment of the overflow status of garbage bins can be achieved, achieving more comprehensive and accurate garbage status monitoring.

[0046] Furthermore, based on the convolutional neural network, an overflow assessment model is constructed, including: obtaining a trash can monitoring image sample set and an overflow assessment sample set; dividing the trash can monitoring image sample set and the overflow assessment sample set according to a predetermined ratio to obtain an overflow assessment training set and an overflow assessment test set; performing supervised training on the convolutional neural network according to the overflow assessment training set to generate an overflow assessment initial model; performing test optimization on the overflow assessment initial model according to the overflow assessment test set to generate the overflow assessment model.

[0047] Specifically, the overflow assessment sample set refers to the label data set corresponding to the garbage bin monitoring image sample set, which annotates the overflow degree of each image, usually expressed as a number or classification value. The training set is a sample data set used for convolutional neural network (CNN) model training. The model improves its prediction ability by learning the relationship between the features and labels of the training set. The test set is a sample data set used to evaluate the performance of the trained model, and is optimized by verifying the accuracy and generalization ability of the model.

[0048] First, a large number of monitoring image samples of trash cans are collected, and each image is labeled with an overflow degree label to form an overflow assessment sample set. For example, an empty trash can image is labeled as 0.1, an image of trash close to the trash can mouth is labeled as 0.85, and an image of an overflowing trash can is labeled as 1.0. These sample data need to be preprocessed, including image scaling, grayscale, data enhancement (such as flipping, rotation), etc., to ensure data quality and diversity.

[0049] Subsequently, the sample data set is divided into a training set and a test set according to a certain ratio (such as 7:3). The training set is used for model learning, and the test set is used for model performance evaluation. In the specific implementation, a convolutional neural network (CNN) can be built with the help of a deep learning framework (such as TensorFlow or PyTorch). CNN extracts key features of the trash can image through the convolution layer, such as the stacking height, location distribution, and overflow of the garbage.

[0050] During the training process, the images in the training set and their corresponding labels are input into the CNN model, and the supervised learning method is used to optimize the parameters of the model. For example, the cross entropy loss function is used to measure the error between the model prediction value and the true value, and the model weight is adjusted through the back propagation algorithm to gradually improve the evaluation accuracy. After multiple rounds of training, the model generates an initial overflow evaluation model.

[0051] Next, the test set is input into the initial model for evaluation to verify the model's generalization ability for unseen samples. If the test result accuracy is low, the model structure or training parameters need to be further adjusted. For example, the number of convolutional layers or the learning rate can be modified. Finally, after test optimization, a stable and reliable overflow evaluation model is generated, which can be used for real-time garbage bin overflow status detection.

[0052] Through the process of building an overflow assessment model based on a convolutional neural network, key features can be efficiently extracted from trash can monitoring images and accurate overflow assessment coefficients can be generated, thereby improving the accuracy and robustness of the assessment.

[0053] Furthermore, the number of overflowing trash cans is calculated based on the multiple overflow assessment coefficients, including: determining whether the multiple overflow assessment coefficients are greater than or equal to an overflow assessment threshold; recording the trash cans corresponding to the overflow assessment coefficients that are greater than or equal to the overflow assessment threshold as overflowing trash cans; and counting the number of the overflowing trash cans to generate the number of overflowing trash cans.

[0054] Specifically, the overflow assessment coefficients of multiple trash bins output by the overflow assessment model are received, and these coefficients represent the current state of each trash bin. For example, the overflow assessment coefficients of trash bins at a certain garbage collection point are 0.7, 0.85, 0.6, 0.9 and 0.8 respectively, and the data is obtained through the sensor unit and the assessment model.

[0055] In order to accurately determine whether a trash bin is overflowing, a full overflow assessment threshold needs to be preset as a distinction standard. For each trash bin's assessment coefficient, the system compares it with the threshold one by one. If a trash bin's full overflow assessment coefficient is greater than or equal to the threshold, the trash bin is marked as an overflowing trash bin.

[0056] The trash cans that meet the threshold conditions are recorded one by one, and the total number is counted. For example, in the above example, 3 of the 5 trash cans are marked as overflowing trash cans, so the number of overflowing trash cans is 3. This statistical data will be used for subsequent garbage removal task priority determination and task generation.

[0057] Through threshold judgment and statistics based on the overflow assessment coefficient, the number of overflowing garbage bins can be determined quickly and efficiently, providing a scientific basis for garbage collection and transportation scheduling.

[0058] Furthermore, based on the garbage collection and transportation instructions, the monitoring information of the multiple garbage bins is sent to a garbage collection and transportation management center to generate a garbage collection and transportation management plan, including: based on the garbage collection and transportation instructions, activating the garbage collection and transportation management model of the garbage collection and transportation management center, wherein the garbage collection and transportation management model includes a garbage classification management model and a garbage collection and transportation scheduling model; inputting the monitoring information of the multiple garbage bins into the garbage classification management model to obtain a garbage classification management plan; based on the monitoring information of the multiple garbage bins, according to the garbage collection and transportation scheduling model, obtaining a garbage collection and transportation scheduling plan; outputting the garbage classification management plan and the garbage collection and transportation scheduling plan as the garbage collection and transportation management plan.

[0059] Specifically, the garbage collection and transportation management model is the core functional module of the garbage collection and transportation management center, which is used to process garbage collection and transportation instructions and generate garbage collection and transportation management plans, mainly including garbage classification management model and garbage collection and transportation scheduling model. The garbage classification management model is an analysis model specifically for garbage classification. It generates garbage classification management plans based on garbage bin monitoring information to ensure that garbage collection and transportation meet classification requirements. The garbage collection and transportation scheduling model is a model that optimizes the collection and transportation routes and vehicle scheduling based on the garbage bin status and collection and transportation resource allocation, and is used to generate garbage collection and transportation scheduling plans.

[0060] After the collection and transportation instructions are sent to the garbage collection and transportation management center, the garbage collection and transportation management model is activated. The model includes two parts: the classification management model and the scheduling model. Specifically, the garbage classification management model is based on the monitoring information and classification rules of the garbage bin, and a classification priority determination model is established through a machine learning algorithm. First, multidimensional data is collected, including the overflow assessment coefficient of the garbage bin, the type of garbage (such as kitchen waste, recyclable garbage), the location and the historical transportation records, etc., to form a sample data set. Secondly, the samples are trained using classification algorithms such as decision trees and random forests to learn the mapping relationship between the overflow status and transportation requirements of different types of garbage. The model also introduces classification priority rules, such as giving priority to the transportation of kitchen waste bins to avoid the spread of odor. Finally, the model optimizes the classification accuracy through the validation set, forming a classification management tool that can be applied in real time, and realizing the dynamic adjustment and priority sorting of garbage collection and transportation classification. The garbage collection and transportation scheduling model is constructed through a path optimization algorithm based on the geographical location of the garbage bin, the status of the transportation vehicle and real-time traffic data. First, data such as the location of the garbage bin, overflow status, load and location of the garbage collection vehicle are collected to form dynamic input variables; then, a classic path planning algorithm (such as Dijkstra or A* algorithm) is used in combination with real-time traffic information to generate the shortest path plan. At the same time, a multi-objective optimization algorithm (such as particle swarm algorithm or genetic algorithm) is introduced to minimize the collection time, fuel consumption and task load unevenness, and optimize vehicle scheduling and route planning. During the training phase, the model continuously optimizes parameters through historical collection data, and dynamically adjusts them in actual operation to ensure the maximization of collection efficiency and resource utilization.

[0061] First, the garbage bin monitoring information is input into the garbage classification management model to analyze the overflow status of different types of garbage bins and generate a garbage classification management plan. Then, based on the geographical location information of the garbage bins, the type of garbage, the vehicle status and real-time traffic information, the monitoring information is input into the garbage collection and transportation scheduling model. The scheduling model generates the optimal collection and transportation path and task allocation through the optimization algorithm. Finally, the classification management plan and the scheduling plan are integrated to form a complete garbage collection and transportation management plan, which is output to the management terminal or vehicle equipment to guide the actual collection and transportation tasks.

[0062] By inputting garbage bin monitoring information into the garbage collection and transportation management model and generating garbage classification management and scheduling plans, this method can achieve accurate classification and efficient scheduling of garbage collection and transportation tasks.

[0063] Furthermore, based on the monitoring information of the multiple garbage bins and according to the garbage collection and transportation scheduling model, a garbage collection and transportation scheduling plan is obtained, including: performing garbage capacity statistics based on the monitoring information of the multiple garbage bins to obtain the capacity of garbage to be collected and transported; performing location identification based on the monitoring information of the multiple garbage bins to determine the location of the garbage to be collected and transported; collecting information on the multiple garbage collection and transportation vehicles of the garbage collection and transportation management center to obtain a collection and transportation vehicle information set; inputting the capacity of garbage to be collected and transported, the location of garbage to be collected and transported and the collection and transportation vehicle information set into the garbage collection and transportation scheduling model to generate the garbage collection and transportation scheduling plan.

[0064] Specifically, garbage capacity statistics is to calculate the total capacity of garbage to be collected based on garbage bin monitoring information, which is used to measure the scale of collection tasks and vehicle arrangement needs. Location identification is to determine the geographical location of the garbage bin to be collected based on garbage bin monitoring information, usually using GPS or sensor geographic data to assist in positioning. The collection and transportation vehicle information set contains real-time status information of all collection and transportation vehicles, including vehicle location, load, collection and transportation task status, and fuel status.

[0065] First, the garbage capacity is counted through the garbage bin monitoring information. The system integrates the weight of multiple garbage bins, overflow assessment coefficients and other information to calculate the total capacity of the garbage to be collected. Next, the system identifies the location of the garbage bin to be collected, and obtains the specific geographical coordinates of the garbage bin through GPS data or predefined information of the garbage bin installation location. Subsequently, the system collects information about the garbage collection and transportation management center's collection and transportation vehicles to form a collection and transportation vehicle information set, including each vehicle's current location, load capacity, task status and fuel volume. The capacity of garbage to be collected, the location of the garbage bin and the vehicle information are input into the garbage collection and transportation scheduling model. The scheduling model combines the path planning algorithm (such as the Dijkstra algorithm) and the multi-objective optimization algorithm (such as the genetic algorithm) to generate the optimal collection and transportation plan. For example, according to the current location of vehicle number 01 and the location of garbage bin number 001, a shortest collection and transportation path is planned, while balancing the vehicle load and collection and transportation efficiency. Finally, the garbage collection and transportation scheduling plan is output, including vehicle task allocation, driving path and estimated completion time.

[0066] This step ensures comprehensive analysis and reasonable scheduling of garbage collection tasks through garbage capacity statistics, location identification, and vehicle information collection. The garbage collection and transportation scheduling model maximizes the efficiency of garbage collection and transportation through path optimization and resource balancing, avoiding the problem of empty or overloaded vehicles. The final scheduling plan is combined with real-time traffic information to further reduce collection time and fuel costs.

[0067] Furthermore, the sensing unit includes multiple sensors, including position sensors, temperature and humidity sensors, weight sensors and video monitoring equipment.

[0068] Specifically, the location sensor is used to obtain the geographic location data of the trash can in order to accurately locate the area where the trash can is located. The temperature and humidity sensor is used to monitor the temperature and humidity inside the trash can to determine the length of time the garbage has been accumulated and the possible odor or fire hazards. The weight sensor measures the current weight of the garbage in the trash can by weighing it to assess the filling level of the trash can. The video surveillance equipment collects image information inside the trash can in real time to help identify the height of garbage accumulation and overflow.

[0069] A sensor unit is installed on each trash can, including a position sensor, a temperature and humidity sensor, a weight sensor, and a video monitoring device. The sensor unit realizes real-time data collection and transmission through the Internet of Things technology. The position sensor obtains the geographical location information of the trash can, and the weight sensor measures the current weight of the garbage in the can. For example, if a trash can weighs 45kg, it shows that it is close to full. The temperature and humidity sensor monitors the internal environment of the trash can, indicating that kitchen waste may be accumulated in the trash can. The video monitoring equipment captures real-time images of the trash can every 10 minutes and analyzes the height of the garbage accumulation.

[0070] These data are transmitted to the management system via wireless networks for real-time analysis and processing. For example, by combining weight and video surveillance data, the system determines that a trash can is close to overflowing, and the data from the temperature and humidity sensors indicates that the can may produce odor, thereby increasing the priority of the trash can's removal. The combination of sensor units enables multi-dimensional monitoring of the trash can status, improving the comprehensiveness and accuracy of monitoring.

[0071] Through the coordinated monitoring of multiple sensors, the sensing unit can comprehensively collect multi-dimensional status information of the trash can, effectively improving the accuracy and reliability of garbage status detection. Position sensors ensure accurate positioning of the trash can, temperature and humidity sensors provide environmental risk monitoring, weight sensors quantitatively evaluate the filling status, and video monitoring equipment further supplements image information.

[0072] In summary, the garbage collection and transportation management method provided in the embodiment of the present application has the following technical effects:

[0073] 1. By monitoring the garbage bins in real time and generating garbage collection and transportation instructions based on the overflow status, dynamic and precise management of garbage collection and transportation is achieved. This method can effectively reduce environmental pollution caused by overflowing garbage bins, improve the response efficiency of garbage collection and transportation, and optimize the utilization rate of transportation resources.

[0074] 2. The introduction of convolutional neural networks to detect overflow in garbage bin monitoring images significantly improves the accuracy and robustness of garbage bin status assessment. Compared with traditional methods, this technology can capture more garbage accumulation features, avoid misjudgment, and provide more reliable data support for garbage collection and transportation decisions.

[0075] 3. Combining garbage capacity statistics, location identification and vehicle status information, the garbage collection and transportation scheduling model can dynamically generate the optimal collection and transportation plan. This plan significantly improves the response speed and path efficiency of the collection and transportation task, while achieving multi-objective optimization, reducing collection and transportation costs and improving resource utilization.

[0076] Embodiment 2, based on the same inventive concept as the garbage collection and transportation management method in the above embodiment, Figure 2 As shown, the embodiment of the present application provides a garbage collection and transportation management system, which includes:

[0077] A real-time monitoring module 11 is used to perform real-time monitoring of multiple garbage bins at a garbage collection point through pre-deployed sensor units to obtain monitoring information of multiple garbage bins; an overflow detection module 12 is used to perform overflow detection based on the monitoring information of the multiple garbage bins to obtain multiple overflow assessment coefficients; a quantity calculation module 13 is used to calculate the number of overflowing garbage bins based on the multiple overflow assessment coefficients; an instruction generation module 14 is used to generate a garbage collection and transportation instruction if the number of overflowing garbage bins is greater than or equal to a threshold value of the number of overflowing garbage bins; a plan generation module 15 is used to send the monitoring information of the multiple garbage bins to a garbage collection and transportation management center based on the garbage collection and transportation instruction to generate a garbage collection and transportation management plan.

[0078] Furthermore, the overflow detection module 12 is also used to perform the following steps: extract multiple garbage bin monitoring images based on the multiple garbage bin monitoring information; build an overflow assessment model based on a convolutional neural network; input the multiple garbage bin monitoring images into the overflow assessment model to generate the multiple overflow assessment coefficients.

[0079] Furthermore, the overflow detection module 12 is also used to perform the following steps: obtain a trash can monitoring image sample set and an overflow assessment sample set; divide the trash can monitoring image sample set and the overflow assessment sample set according to a predetermined ratio to obtain an overflow assessment training set and an overflow assessment test set; perform supervised training on the convolutional neural network according to the overflow assessment training set to generate an overflow assessment initial model; perform test optimization on the overflow assessment initial model according to the overflow assessment test set to generate the overflow assessment model.

[0080] Furthermore, the quantity calculation module 13 is also used to perform the following steps: determine whether the multiple overflow assessment coefficients are greater than or equal to the overflow assessment threshold; record the trash cans corresponding to the overflow assessment coefficients that are greater than or equal to the overflow assessment threshold as overflow trash cans; count the number of the overflow trash cans to generate the number of overflow trash cans.

[0081] Furthermore, the solution generation module 15 is also used to perform the following steps: based on the garbage collection and transportation instructions, activate the garbage collection and transportation management model of the garbage collection and transportation management center, wherein the garbage collection and transportation management model includes a garbage classification management model and a garbage collection and transportation scheduling model; input the monitoring information of the multiple garbage bins into the garbage classification management model to obtain a garbage classification management solution; based on the monitoring information of the multiple garbage bins, obtain a garbage collection and transportation scheduling solution according to the garbage collection and transportation scheduling model; output the garbage classification management solution and the garbage collection and transportation scheduling solution as the garbage collection and transportation management solution.

[0082] Furthermore, the solution generation module 15 is also used to perform the following steps: perform garbage capacity statistics based on the monitoring information of the multiple garbage bins to obtain the capacity of garbage to be collected and transported; perform location identification based on the monitoring information of the multiple garbage bins to determine the location of the garbage to be collected and transported; collect information from the multiple garbage collection and transportation vehicles of the garbage collection and transportation management center to obtain a collection and transportation vehicle information set; input the capacity of garbage to be collected and transported, the location of garbage to be collected and transported, and the collection and transportation vehicle information set into the garbage collection and transportation scheduling model to generate the garbage collection and transportation scheduling solution.

[0083] Furthermore, the sensing unit includes multiple sensors, including position sensors, temperature and humidity sensors, weight sensors and video monitoring equipment.

[0084] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0085] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A garbage collection and transportation management method, characterized in that: The method comprises: The multiple garbage bins at the garbage collection point are monitored in real time by using pre-deployed sensor units to obtain monitoring information of the multiple garbage bins; Perform overflow detection according to the monitoring information of the plurality of trash cans to obtain a plurality of overflow assessment coefficients; Calculating the number of overflowing trash cans according to the plurality of overflowing assessment coefficients; If the number of overflowing garbage bins is greater than or equal to the overflowing garbage bin number threshold, a garbage collection and transportation instruction is generated; Based on the garbage collection and transportation instructions, the monitoring information of the multiple garbage bins is sent to a garbage collection and transportation management center to generate a garbage collection and transportation management plan.

2. The method according to claim 1, characterized in that Perform overflow detection according to the monitoring information of the plurality of trash cans to obtain a plurality of overflow assessment coefficients, including: Extracting a plurality of garbage bin monitoring images according to the plurality of garbage bin monitoring information; Build an overflow assessment model based on convolutional neural networks; The plurality of garbage bin monitoring images are input into the overflow assessment model to generate the plurality of overflow assessment coefficients.

3. The method according to claim 2, characterized in that Based on the convolutional neural network, an overflow assessment model is built, including: Obtain a garbage bin monitoring image sample set and an overflow assessment sample set; Dividing the garbage bin monitoring image sample set and the overflow assessment sample set according to a predetermined ratio to obtain an overflow assessment training set and an overflow assessment test set; Performing supervised training on the convolutional neural network according to the overflow assessment training set to generate an overflow assessment initial model; The overflow assessment initial model is tested and optimized according to the overflow assessment test set to generate the overflow assessment model.

4. The method according to claim 1, characterized in that Calculating the number of overflowing trash cans according to the plurality of overflowing assessment coefficients includes: Determining whether the plurality of overflow assessment coefficients are greater than or equal to an overflow assessment threshold; Recording a trash bin corresponding to an overflow assessment coefficient that is greater than or equal to the overflow assessment threshold as an overflow trash bin; The number of the overflowing trash cans is counted to generate the number of the overflowing trash cans.

5. The method according to claim 1, characterized in that Based on the garbage collection and transportation instruction, the monitoring information of the plurality of garbage bins is sent to a garbage collection and transportation management center to generate a garbage collection and transportation management plan, including: Based on the garbage collection and transportation instruction, activating the garbage collection and transportation management model of the garbage collection and transportation management center, wherein the garbage collection and transportation management model includes a garbage classification management model and a garbage collection and transportation scheduling model; Inputting the monitoring information of the plurality of garbage bins into the garbage classification management model to obtain a garbage classification management plan; Based on the monitoring information of the plurality of garbage bins, and according to the garbage collection and transportation scheduling model, a garbage collection and transportation scheduling plan is obtained; The garbage classification management plan and the garbage collection and transportation scheduling plan are output as the garbage collection and transportation management plan.

6. The method according to claim 5, characterized in that Based on the monitoring information of the plurality of garbage bins and according to the garbage collection and transportation scheduling model, a garbage collection and transportation scheduling plan is obtained, including: Perform garbage capacity statistics based on the monitoring information of the multiple garbage bins to obtain the capacity of the garbage to be collected and transported; Performing location identification based on the monitoring information of the multiple garbage bins to determine the location of the garbage to be collected and transported; Collecting information on multiple garbage collection and transportation vehicles in the garbage collection and transportation management center to obtain a collection and transportation vehicle information set; The capacity of the garbage to be collected and transported, the location of the garbage to be collected and transported, and the collection and transportation vehicle information set are input into the garbage collection and transportation scheduling model to generate the garbage collection and transportation scheduling plan.

7. The method according to claim 1, characterized in that The sensing unit includes multiple sensors, including position sensors, temperature and humidity sensors, weight sensors and video monitoring equipment.

8. Garbage collection and transportation management system, characterized in that: The system for executing the garbage collection and transportation management method according to any one of claims 1 to 7 comprises: A real-time monitoring module is used to monitor multiple garbage bins at the garbage collection point in real time through pre-deployed sensor units to obtain monitoring information of multiple garbage bins; An overflow detection module, used to perform overflow detection according to the monitoring information of the plurality of trash cans, and obtain a plurality of overflow assessment coefficients; A quantity calculation module, used for calculating the quantity of overflowing trash cans according to the plurality of overflow assessment coefficients; An instruction generation module, configured to generate a garbage collection and transportation instruction if the number of overflowing garbage bins is greater than or equal to a threshold number of overflowing garbage bins; A solution generation module is used to send the monitoring information of the multiple garbage bins to the garbage collection and transportation management center based on the garbage collection and transportation instructions, and generate a garbage collection and transportation management solution.

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