Urban management garbage classification transportation system

Abstract

The invention relates to the technical field of garbage classification, and discloses an urban management garbage classification transportation system which comprises an intelligent classification module, a data collection and analysis module, a transportation scheduling module and a monitoring and management module. The intelligent classification module is used for automatically classifying the garbage by using image recognition and machine learning technologies; the data collection and analysis module is used for collecting data of each garbage throwing point, including garbage type, weight and throwing frequency; the transportation scheduling module is used for dynamically adjusting a garbage transportation route and a time table according to real-time data and prediction analysis; and the monitoring and management module is used for monitoring the whole process of garbage classification and transportation. The method has the advantage of improving the accuracy and the treatment efficiency of garbage classification.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage classification, and in particular to a garbage classification and transportation system for urban management. Background Art

[0002] Urban garbage has many hazards such as occupying land, polluting air and water, and endangering human health, which seriously affects our daily life. The classification and treatment of domestic garbage is an important part of implementing the sustainable development strategy and a development requirement for building an environmentally friendly society. The classification and treatment of garbage can classify and recycle garbage with different characteristics. While improving the efficiency of garbage treatment, it can also beautify the city, protect the environment, and maximize resource utilization, bringing a win-win situation for society, the environment, and the economy.

[0003] Domestic waste is the most generated waste in daily life. The difficulty of classification lies in the large amount and complex composition, which increases the workload and difficulty of treatment. Waste classification is a complex chain, including classification, collection, transportation, treatment and other links. If any of these links has problems, the entire classification system may fail. Therefore, it is necessary to design an efficient, intelligent, closed-loop management garbage classification and transportation system. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the deficiencies in the prior art, the present invention provides a city management garbage classification and transportation system, which has the advantages of improving the accuracy and processing efficiency of garbage classification and solves the problems in the above-mentioned background technology.

[0006] (II) Technical solution

[0007] In order to achieve the above-mentioned purpose of improving the accuracy and processing efficiency of garbage classification, the present invention provides the following technical solutions: a city management garbage classification and transportation system, including an intelligent classification module, a data collection and analysis module, a transportation scheduling module and a monitoring and management module;

[0008] The intelligent classification module is used to automatically classify garbage using image recognition and machine learning technology;

[0009] Preferably, the intelligent classification module further comprises:

[0010] By integrating image recognition and machine learning technologies, efficient and automatic classification of garbage can be achieved. Equipped with high-resolution cameras and sensors, images of garbage are captured in real time. After the image is captured, deep learning algorithms are used to analyze the image data. By identifying shape, color, and material, the garbage is automatically classified into multiple categories such as recyclables, kitchen waste, and hazardous waste.

[0011] Preferably, the analyzing the image data using a deep learning algorithm further comprises:

[0012] Collect a large amount of labeled garbage image data, including different types of garbage samples, pre-process the images, including resizing, normalizing pixel values, and enhancing image quality, select convolutional neural networks, design network structures, including the number of layers, activation functions, and pooling layers, train the model using the prepared training data, optimize network parameters through the back-propagation algorithm, minimize the error between the predicted results and the true labels, set aside a portion of the data as validation sets and test sets, evaluate the classification accuracy and other performance indicators of the model, adjust the model structure or hyperparameters based on the results, and apply the model to real-time garbage classification after training is completed

[0013] The data collection and analysis module is used to collect data from each garbage placement point, including garbage type, weight and placement frequency;

[0014] Preferably, the data collection and analysis module further comprises:

[0015] By integrating high-precision sensors and IoT technology, data from each garbage collection point is collected in real time, including garbage type, weight, and frequency of collection. Equipped with image recognition equipment and combined with weight sensors to record the actual weight of each collection, the system will regularly count the collection frequency of each collection point to form a complete data set. Through data mining and visualization tools, managers can identify the peak period and category distribution of garbage generation and evaluate the effectiveness of garbage classification.

[0016] The transportation scheduling module is used to dynamically adjust the garbage transportation routes and schedules based on real-time data and predictive analysis;

[0017] Preferably, the transport scheduling module further comprises:

[0018] Through real-time data collection and intelligent predictive analysis, the routes and schedules of garbage transportation are optimized. IoT devices and GPS technology are used to monitor the amount, type and loading status of garbage at each garbage drop-off point in real time, while collecting vehicle location information and driving data. Based on this information, the system applies algorithms to perform data analysis, evaluate current transportation needs and traffic conditions, and dynamically adjust transportation routes. The module also considers historical data and trend analysis to predict future garbage generation and transportation needs, and plan scheduling strategies in advance.

[0019] The monitoring and management module is used to monitor the entire process of garbage classification and transportation.

[0020] Preferably, the monitoring and management module further includes:

[0021] By integrating a variety of monitoring technologies and management systems, comprehensive monitoring of the entire process of garbage classification and transportation is achieved. High-resolution cameras and sensors are configured to capture garbage placement and classification in real time. The module is also equipped with a GPS tracking function to monitor the location information and driving status of transport vehicles in real time, and record the time, loading volume and unloading status of each transportation link. All data is centrally processed through the cloud platform to generate a visual monitoring panel for managers to view and analyze in real time. The module has an early warning mechanism. When abnormal situations such as classification errors or transportation delays occur, the system will automatically send an alarm.

[0022] A method for transporting urban waste by classification, comprising the following steps:

[0023] S1: Set up smart trash cans in various areas of the city, equipped with intelligent classification modules, supporting image recognition and machine learning;

[0024] S2: Using the data collection and analysis module, real-time monitoring of data at each garbage disposal point, including garbage type, weight and frequency of disposal;

[0025] S3: Dynamically adjust the routes and schedules of waste transportation based on real-time data and analysis results;

[0026] S4: Real-time monitoring of the entire process of garbage classification and transportation.

[0027] (III) Beneficial effects

[0028] Compared with the prior art, the present invention provides a city management garbage classification and transportation system, which has the following beneficial effects:

[0029] 1. Through the image recognition and machine learning technology of the intelligent classification module, the accuracy of garbage classification is significantly improved, the classification error rate is reduced, and the effective recycling of recyclable resources is promoted.

[0030] 2. The data collection and analysis module provides real-time data to help managers optimize transportation routes and times, reduce transportation costs and resource waste, and improve vehicle utilization.

[0031] 3. The monitoring and management module realizes full monitoring of the classification and transportation process, detects problems in time and takes measures to ensure efficient and safe garbage management. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Example 1

[0035] The present invention provides a technical solution: a city management garbage classification and transportation system, including an intelligent classification module, a data collection and analysis module, a transportation scheduling module and a monitoring and management module;

[0036] The intelligent classification module is used to automatically classify garbage using image recognition and machine learning technology;

[0037] By integrating image recognition and machine learning technologies, efficient and automatic classification of garbage can be achieved. The module is equipped with high-resolution cameras and sensors that can capture images of garbage in real time. After the image is captured, a deep learning algorithm is used to analyze the image data, and the garbage is automatically classified into multiple categories such as recyclables, kitchen waste, and hazardous waste by identifying features such as shape, color, and material. In order to improve the accuracy of classification, the system will continuously receive user feedback and new data, conduct online learning and model updates, and adapt to changes in garbage placement. At the same time, the module can also be connected to smart trash cans to achieve real-time feedback of classification information and guide users to place garbage correctly. The data analysis part will summarize the classification results, provide important decision-making support for subsequent garbage disposal and resource recycling, and promote the intelligence and efficiency of urban garbage management.

[0038] The data collection and analysis module is used to collect data from each garbage disposal point, including garbage type, weight and frequency of disposal;

[0039] The module is equipped with image recognition equipment, which can automatically identify the type of garbage placed, and combine with weight sensors to record the actual weight of each placement. The system will regularly count the frequency of placement at each placement point to form a complete data set. These data are uploaded to the cloud database in real time for centralized management and in-depth analysis. Through data mining and visualization tools, managers can identify the peak period and category distribution of garbage generation, evaluate the effectiveness of garbage classification, provide a basis for transportation scheduling and resource allocation, and ultimately improve the overall efficiency and accuracy of urban garbage management.

[0040] The transportation scheduling module is used to dynamically adjust garbage transportation routes and schedules based on real-time data and predictive analysis;

[0041] Using IoT devices and GPS technology, the system monitors the amount, type and loading status of garbage at each garbage drop-off point in real time, while collecting the location information and driving data of vehicles. Based on this information, the system applies advanced algorithms such as the shortest path algorithm and genetic algorithm to analyze data, evaluate current transportation needs and traffic conditions, and dynamically adjust transportation routes to avoid congestion and delays. The module also considers historical data and trend analysis to predict future garbage generation and transportation needs, and plan scheduling strategies in advance. Through this intelligent scheduling, the system can efficiently utilize transportation resources, reduce vehicle idleness, optimize operating time, ensure that garbage is delivered to the processing center in a timely and safe manner, and ultimately improve the overall efficiency and responsiveness of urban garbage management.

[0042] The monitoring and management module is used to monitor the entire process of garbage classification and transportation.

[0043] The module is equipped with high-resolution cameras and sensors to capture the placement and classification of garbage in real time to ensure the accuracy of classification. The module is also equipped with GPS tracking function to monitor the location information and driving status of transport vehicles in real time, and record the time, loading volume and unloading conditions of each transport link. All data is centrally processed through the cloud platform to generate a visual monitoring panel for managers to view and analyze in real time. At the same time, the module is equipped with an early warning mechanism. When abnormal situations such as classification errors or transportation delays occur, the system will automatically send an alarm to facilitate timely intervention and processing. Through these functions, the monitoring and management module not only improves the compliance of garbage classification and the efficiency of transportation, but also provides reliable data support for decision-making, promoting the intelligence and transparency of urban garbage management.

[0044] The specific implementation of the above-mentioned transportation scheduling module includes:

[0045] 1. Data Input

[0046] Real-time data includes: garbage generation, from sensors or historical data; traffic conditions, real-time traffic data; weather conditions, weather conditions that affect transportation.

[0047] The forecast data includes: garbage generation trends, forecast analysis based on historical data; transportation demand forecast, based on population changes, holidays and other factors.

[0048] 2. Data processing

[0049] Collect and integrate real-time and forecast data to form a complete set of input information; use machine learning or statistical models to analyze historical data to predict future waste generation and transportation needs.

[0050] 3. Route Optimization

[0051] Apply shortest path algorithms, such as Dijkstra algorithm, A* algorithm and genetic algorithm, to calculate the optimal transportation route; dynamically adjust the transportation route according to real-time data, such as traffic congestion and accidents, to ensure transportation efficiency.

[0052] 4. Schedule Adjustment

[0053] Through time window analysis, the optimal departure time and transportation frequency are determined to ensure timely collection of garbage; automatic scheduling rules are set to automatically generate a new schedule when real-time data reaches a certain threshold.

[0054] Recalculate the transportation schedule based on the current real-time data and forecast data. The steps are as follows:

[0055] 1) Collect current data:

[0056] Current waste generation: 600 kg (exceeding the threshold)

[0057] Current transportation status: A transportation route is congested and the delay is expected to be 20 minutes.

[0058] 2) Determine new transportation needs:

[0059] As the amount of waste generated exceeded the threshold, it was decided to increase the frequency of transportation in the area.

[0060] Assume that the original schedule was to have a train depart every hour, and the new demand is for a train to depart every 30 minutes.

[0061] 3) Recalculate the timetable:

[0062] Current time: 08:00

[0063] Original schedule: 08:00 09:00

[0066] New Schedule: 08:00 08:30 09:00 09:30

[0071] 4) Update the transportation plan:

[0072] Notify transportation personnel and related systems of the new schedule to ensure that trains depart on the new schedule.

[0073] 5. System feedback

[0074] After implementation, data such as transportation efficiency and time consumption are collected to evaluate the results; algorithms and scheduling rules are adjusted based on feedback results to achieve continuous optimization of the system.

[0075] Optimizing the use of transport resources, reducing vehicle idleness and optimizing operating time can be achieved through the following steps:

[0076] 1. Data Analysis

[0077] Relevant data needs to be collected and analyzed, including:

[0078] Vehicle usage data: each vehicle’s driving routes, mileage, and operating time.

[0079] Demand forecast data: Forecasts of waste generation and transportation demand in different regions.

[0080] Traffic conditions: real-time traffic information, including congestion, road conditions, etc.

[0081] 2. Vehicle dispatch optimization

[0082] Dynamic Scheduling: Dynamically adjust transportation tasks based on real-time demand and forecast data to ensure vehicles work in high-demand areas.

[0083] Route Optimization: Use algorithms to calculate the best route to reduce driving time and mileage.

[0084] Assuming there are 3 vehicles and 5 garbage collection points, use the following steps to optimize the scheduling:

[0085] Current status:

[0086] Vehicle A is at collection point 1 and its estimated completion time is 09:00.

[0087] Vehicle B is at collection point 2 with an estimated completion time of 09:15.

[0088] Vehicle C is at collection point 3 and has an estimated completion time of 09:30.

[0089] Demand forecast: The amount of garbage generated at collection point 4 is large and needs to be handled urgently.

[0090] Scheduling optimization: Based on the prediction, it is decided to re-schedule vehicle B to collection point 4 instead of returning to the parking lot; the optimal path for vehicle B to travel to collection point 4 is the congested route reduced by traffic information.

[0091] A method for transporting urban waste by classification, comprising the following steps:

[0092] S1: Set up smart trash cans in various areas of the city, equipped with intelligent classification modules, supporting image recognition and machine learning;

[0093] S2: Using the data collection and analysis module, real-time monitoring of data at each garbage disposal point, including garbage type, weight and frequency of disposal;

[0094] S3: Dynamically adjust the routes and schedules of waste transportation based on real-time data and analysis results;

[0095] S4: Real-time monitoring of the entire process of garbage classification and transportation.

[0096] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0097] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A city management garbage classification and transportation system, characterized in that: It includes intelligent classification module, data collection and analysis module, transportation scheduling module and monitoring and management module; The intelligent classification module is used to automatically classify garbage using image recognition and machine learning technology; The data collection and analysis module is used to collect data from each garbage placement point, including garbage type, weight and placement frequency; The transportation scheduling module is used to dynamically adjust the garbage transportation routes and schedules based on real-time data and predictive analysis; The monitoring and management module is used to monitor the entire process of garbage classification and transportation.

2. The urban management garbage classification and transportation system according to claim 1 is characterized in that: The intelligent classification module further comprises: By integrating image recognition and machine learning technologies, efficient and automatic classification of garbage can be achieved. Equipped with high-resolution cameras and sensors, images of garbage are captured in real time. After the image is captured, deep learning algorithms are used to analyze the image data. By identifying shape, color, and material, the garbage is automatically classified into multiple categories such as recyclables, kitchen waste, and hazardous waste.

3. The urban management garbage classification and transportation system according to claim 2 is characterized in that: The use of a deep learning algorithm to analyze image data further includes: Collect a large amount of labeled garbage image data, including different types of garbage samples, preprocess the images, including adjusting the image size, normalizing pixel values, and enhancing image quality, select convolutional neural networks, design the network structure, including the number of layers, activation functions, and pooling layers, train the model using the prepared training data, optimize the network parameters through the back propagation algorithm, minimize the error between the predicted results and the true labels, set aside a portion of the data as a validation set and a test set, evaluate the classification accuracy and other performance indicators of the model, adjust the model structure or hyperparameters based on the results, and after training, apply the model to real-time garbage classification.

4. The urban management garbage classification and transportation system according to claim 1 is characterized in that: The data collection and analysis module further includes: By integrating high-precision sensors and Internet of Things technology, data from each garbage disposal point is collected in real time, including garbage type, weight and frequency of disposal. Equipped with image recognition equipment and combined with weight sensors, the actual weight of each disposal is recorded. The system will regularly count the frequency of disposal at each disposal point to form a complete data set. Through data mining and visualization tools, managers can identify the peak period and category distribution of garbage generation and evaluate the effectiveness of garbage classification.

5. The urban management garbage classification and transportation system according to claim 1 is characterized in that: The transport scheduling module further includes optimizing the routes and schedules of garbage transportation through real-time data collection and intelligent prediction analysis, using IoT devices and GPS technology to monitor the amount, type and loading status of garbage at each garbage drop-off point in real time, and collecting vehicle location information and driving data. Based on this information, the system applies algorithms to perform data analysis, evaluate current transportation needs and traffic conditions, and dynamically adjust transportation routes. The module also considers historical data and trend analysis to predict future garbage generation and transportation needs, and plan scheduling strategies in advance.

6. The urban management garbage classification and transportation system according to claim 1 is characterized in that: The monitoring and management module further includes integrating multiple monitoring technologies and management systems to achieve comprehensive monitoring of the entire process of garbage classification and transportation, configuring high-resolution cameras and sensors to capture garbage placement and classification in real time. The module is also equipped with a GPS tracking function to monitor the location information and driving status of transportation vehicles in real time, and record the time, loading volume and unloading status of each transportation link. All data is centrally processed through the cloud platform to generate a visual monitoring panel for management personnel to view and analyze in real time. The module has an early warning mechanism. When abnormal situations such as classification errors or transportation delays occur, the system will automatically send an alarm.

7. A method for classified transportation of urban management waste, applied to an urban management waste classified transportation system as claimed in claims 1 to 5, comprising the following steps: S1: Set up smart trash cans in various areas of the city, equipped with intelligent classification modules, supporting image recognition and machine learning; S2: Using the data collection and analysis module, real-time monitoring of data at each garbage disposal point, including garbage type, weight and frequency of disposal; S3: Dynamically adjust the routes and schedules of waste transportation based on real-time data and analysis results; S4: Real-time monitoring of the entire process of garbage classification and transportation.

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