Garbage collection classification management method and system driven by mobile internet

Abstract

The invention provides a garbage collection classification management method and system driven by a mobile internet. The garbage collection classification management method comprises the steps that an internet terminal dynamically adjusts garbage classification rules according to received environment data; the internet terminal determines a garbage classification result according to the received garbage data and environmental data in combination with the dynamically adjusted garbage classification rule, and sends the garbage classification result to the user mobile device for display; each type of garbage throwing is monitored and recorded; the internet terminal controls the clearing and transporting vehicle to clear and transport garbage according to the priority of the garbage in the intelligent garbage can, and carries out self-adaptive planning of a clearing and transporting path of the clearing and transporting vehicle according to the comprehensive condition of the priority of the garbage in the clearing and transporting vehicle; and the internet terminal controls the cleaning and transporting vehicle to transport the garbage to the garbage station according to the planned path. The system comprises modules corresponding to the steps of the method.

Description

Technical Field

[0001] The present invention provides a mobile Internet-driven garbage collection and classification management method and system, belonging to the technical field of Internet garbage classification management. Background Art

[0002] With the acceleration of urbanization and the continuous growth of the population, the amount of waste generated is growing rapidly, and waste disposal has become a major challenge for sustainable urban development. Waste sorting, as a key prerequisite for waste disposal, is crucial for improving resource recovery and reducing environmental pollution. Traditional waste recycling and classification management methods have many shortcomings. Regarding waste sorting rules, fixed, universal standards are often used, lacking adaptability to diverse environmental conditions (such as regional differences, seasonal changes, and special events). For example, at some tourist attractions, the increase in visitors during holidays means that the types and quantities of waste generated vary significantly from normal days. Fixed sorting rules cannot be adjusted in a timely manner to accommodate these changes, resulting in inaccurate waste sorting and low resource recovery efficiency. Traditional methods often rely on manual judgment and simple labeling to determine and display waste sorting results, lacking real-time and accuracy. Residents are prone to misclassification when disposing of their waste due to a lack of clear and accurate classification guidance. Furthermore, it is difficult for residents to promptly understand whether their waste has been correctly sorted and the subsequent disposal of their waste. Traditional management methods for monitoring and recording waste disposal often rely on manual inspections and simple registration, which is inefficient and prone to omissions and errors. This makes it difficult to accurately grasp information such as the source, type, and quantity of garbage, which is not conducive to the effective supervision and management of garbage recycling and sorting. In the garbage collection process, traditional collection methods lack scientific consideration of garbage priorities and usually adopt fixed collection routes and times, without considering the actual filling of garbage in the garbage bins and the differences in garbage types. For example, if some perishable garbage is not collected and transported in a timely manner, it will produce odors and breed bacteria, affecting the environment and the quality of life of residents; and if some recyclable garbage is stored for a long time, it may lead to waste of resources. In addition, fixed collection routes may cause vehicles to run empty or be overloaded, increasing transportation costs and energy consumption. Summary of the Invention

[0003] The present invention provides a mobile Internet-driven garbage collection and classification management method and system to solve the technical problems existing in the above-mentioned prior art. The technical solutions adopted are as follows:

[0004] A mobile internet-driven garbage collection and classification management method, the garbage collection and classification management method comprising:

[0005] Internet terminals dynamically adjust garbage classification rules based on received environmental data;

[0006] The Internet terminal determines a garbage classification result based on the received garbage data and environmental data in combination with the dynamically adjusted garbage classification rules, and sends the garbage classification result to the user's mobile device for display;

[0007] Internet terminals monitor and record each type of garbage disposal;

[0008] The Internet terminal controls the garbage collection truck according to the garbage priority in the smart garbage bin, and adaptively plans the collection route of the garbage collection truck based on the comprehensive situation of the garbage priority in the garbage collection truck;

[0009] The Internet terminal controls the garbage collection truck to transport the garbage to the garbage station according to the planned route.

[0010] Furthermore, the Internet terminal dynamically adjusts the garbage classification rules based on the received environmental data, including:

[0011] The Internet terminal receives the environmental data uploaded by the smart trash can in real time; wherein the environmental data includes the ambient temperature and the ambient humidity;

[0012] After receiving each piece of garbage, the priority level 1 and 2 smart trash cans will upload the corresponding corruption warning coefficient of the garbage to the Internet terminal;

[0013] The Internet terminal dynamically adjusts the corruption coefficient value corresponding to each priority level one and level two smart trash can based on the corruption warning coefficient corresponding to the existing garbage in the priority level one and level two smart trash cans combined with environmental data, and obtains the adjusted corruption coefficient value corresponding to the priority level one and level two smart trash cans.

[0014] Furthermore, the Internet terminal dynamically adjusts the corruption coefficient value corresponding to each priority level 1 and level 2 smart trash can according to the corruption warning coefficient corresponding to the existing garbage in the priority level 1 and level 2 smart trash cans in combination with the environmental data, including:

[0015] The Internet terminal retrieves the uncollected waste from the first and second priority smart trash bins;

[0016] Retrieve the corruption warning coefficient of each uncollected priority level 1 and level 2 smart trash can corresponding to the associated garbage;

[0017] The corruption coefficient values of the first- and second-level smart trash cans are dynamically adjusted by using the corruption warning coefficient of the associated garbage corresponding to each first- and second-level smart trash can that has not been cleared, combined with the coefficient dynamic adjustment model.

[0018] Furthermore, the Internet terminal determines a garbage classification result based on the received garbage data and environmental data in combination with the dynamically adjusted garbage classification rules, and sends the garbage classification result to the user's mobile device for display, including:

[0019] The Internet terminal receives the garbage data uploaded by the user's mobile device in real time; wherein the garbage data includes a garbage image of each garbage;

[0020] The current environmental data is collected in real time through the smart trash can and sent to the Internet terminal in real time; wherein the environmental data includes the ambient temperature and humidity;

[0021] The Internet terminal automatically classifies garbage based on environmental data and garbage data combined with current garbage classification rules, and sends the classification results to the user's mobile device for display.

[0022] Furthermore, the Internet terminal automatically classifies the garbage based on the environmental data and garbage data in combination with the current garbage classification rules, and sends the classification results to the user's mobile device for display, including:

[0023] The Internet terminal retrieves image data corresponding to each piece of garbage from the garbage data, identifies the image data, and obtains the garbage category corresponding to the image data;

[0024] After determining the garbage category, the Internet terminal retrieves the current environmental data sent by the smart trash can;

[0025] Use the garbage category and current environmental data corresponding to each garbage to obtain the corruption warning coefficient corresponding to each garbage;

[0026] The priority of the smart trash can corresponding to each garbage classification is determined according to the corruption warning coefficient corresponding to each garbage classification, and the location and priority information of the smart trash can corresponding to the priority of each garbage classification is sent to the user's mobile device for display.

[0027] Furthermore, the priority of the smart trash bin corresponding to each garbage classification is determined according to the corruption warning coefficient corresponding to each garbage, including:

[0028] Compare the corruption warning coefficient corresponding to the garbage with the current corruption coefficient value corresponding to each smart trash can;

[0029] When the corruption warning coefficient corresponding to the garbage is not lower than the corruption coefficient value of the smart garbage bin with a priority of level one, the garbage with a corruption warning coefficient not lower than the corruption coefficient value of the smart garbage bin with a priority of level one is determined to be level one garbage;

[0030] When the corruption warning coefficient corresponding to the garbage is lower than the corruption coefficient value of the smart garbage bin with the first priority, but not lower than the corruption coefficient value of the smart garbage bin with the second priority, the garbage is determined to be second-level garbage;

[0031] When the corruption warning coefficient corresponding to the garbage is lower than the corruption coefficient value of the smart garbage bin with a second priority, the garbage with a corruption warning coefficient lower than the corruption coefficient value of the smart garbage bin with a second priority is determined to be third-level garbage.

[0032] Furthermore, the Internet terminal monitors and records each type of garbage disposal, including:

[0033] The Internet terminal determines the target trash bin;

[0034] The garbage placement is monitored and recorded by detecting the weight change of the target garbage bin and determining the similarity between the image data of the garbage placed and the image data uploaded by the user's mobile device.

[0035] Furthermore, adaptive planning of the removal route of the removal truck is performed based on the comprehensive priority of the garbage in the removal truck, including:

[0036] Comparing the corruption coefficient value of the smart trash can with a preset coefficient threshold;

[0037] When the corruption coefficient value of the smart trash can reaches or exceeds a preset coefficient threshold, it is determined that the smart trash can needs to be cleaned;

[0038] When each garbage collection truck completes garbage loading according to the garbage loading volume, it retrieves the corruption coefficient values of all cleaned smart garbage bins corresponding to the garbage currently loaded by the garbage collection truck;

[0039] Obtaining a cleaning priority coefficient using the corruption coefficient values of all cleaned smart trash cans corresponding to the currently loaded garbage;

[0040] Retrieve the existing path in the database of the Internet terminal;

[0041] Obtaining a garbage transportation impact coefficient by combining the historical average vehicle speed of the current route of the existing routes in the database with a removal priority coefficient;

[0042] The path corresponding to the minimum value of the garbage transportation impact coefficient is used as the current collection path.

[0043] A mobile Internet-driven garbage collection and classification management system, comprising:

[0044] A classification rule dynamic adjustment module is used by Internet terminals to dynamically adjust garbage classification rules based on received environmental data;

[0045] A classification processing module is used for the Internet terminal to determine a garbage classification result based on the received garbage data and environmental data in combination with the dynamically adjusted garbage classification rules, and to send the garbage classification result to the user's mobile device for display;

[0046] The delivery monitoring module is used to monitor and record the delivery of each type of garbage;

[0047] The path planning module is used by the Internet terminal to control the garbage collection truck to collect garbage according to the garbage priority in the smart garbage bin, and to adaptively plan the collection path of the garbage collection truck based on the comprehensive situation of the garbage priority in the collection truck;

[0048] The garbage collection truck transport control module is used by the Internet terminal to control the garbage collection truck to transport garbage to the garbage station according to the planned route.

[0049] Beneficial effects of the present invention:

[0050] This invention proposes a mobile internet-driven waste collection and classification management method and system that dynamically adjusts classification rules based on environmental data, making them more relevant to real-world scenarios and reducing classification errors caused by inappropriate rules. Classification results are determined by combining waste data with the adjusted rules, and feedback is provided to users, helping them improve classification accuracy. This overall improvement in waste classification precision lays a solid foundation for subsequent resource recovery and processing. Waste collection is fully monitored and recorded, allowing managers to view detailed information at any time, effectively overseeing waste sorting. When classification issues or waste handling anomalies arise, records can be traced back to the specific collection process and responsible individuals, facilitating timely resolution and enhancing the transparency and traceability of waste sorting management. Collection and transportation are arranged based on waste priority, prioritizing high-priority waste, preventing the deterioration of perishable waste and other environmental impacts, and improving the timeliness of waste disposal. Adaptive route planning reduces empty runs and detours, reducing transportation costs and energy consumption, improving waste collection efficiency, and enabling more efficient allocation of collection resources. Users receive timely feedback on waste sorting results, understanding correct classification methods, and increasing environmental awareness and participation. At the same time, clear classification guidance and timely feedback enhance the user experience and encourage users to develop good waste sorting habits. The entire management process leverages internet terminals and smart devices to achieve automatic data collection, analysis, and processing, as well as intelligent planning of removal routes. This breaks away from the limitations of traditional manual management, promotes the development of intelligent and refined waste recycling and classification management, and improves the level of urban environmental management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flow chart of the method of the present invention;

[0052] Figure 2This is a system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] The embodiment of the present invention proposes a mobile Internet driven garbage collection and classification management method, such as Figure 1 As shown, the garbage collection and classification management method includes:

[0055] Internet terminals dynamically adjust garbage classification rules based on received environmental data;

[0056] The Internet terminal determines a garbage classification result based on the received garbage data and environmental data in combination with the dynamically adjusted garbage classification rules, and sends the garbage classification result to the user's mobile device for display;

[0057] Internet terminals monitor and record each type of garbage disposal;

[0058] The Internet terminal controls the garbage collection truck according to the garbage priority in the smart garbage bin, and adaptively plans the collection route of the garbage collection truck based on the comprehensive situation of the garbage priority in the garbage collection truck;

[0059] The Internet terminal controls the garbage collection truck to transport the garbage to the garbage station according to the planned route.

[0060] The working principle of the above technical solution is as follows: The internet terminal receives environmental data in real time and dynamically modifies the existing waste classification rules to ensure that the classification rules are consistent with current environmental conditions. The internet terminal receives garbage data and environmental data and inputs them into the dynamically adjusted waste classification rule model. Through model calculation and analysis, the garbage classification category is determined, thereby determining the garbage classification results. Subsequently, using mobile internet communication technology, the garbage classification results are sent to the user's mobile device in intuitive formats such as images, text, and voice. Users can view them in real time to confirm whether their garbage has been placed accurately. Various sensors (such as cameras, weight sensors, and RFID sensors) are deployed at garbage placement points, such as smart trash cans, to monitor each type of garbage placement in real time. Cameras identify the appearance of garbage to assist in classification, weight sensors record the weight of the garbage placed, and RFID sensors identify garbage container information. Monitoring data is transmitted in real time to the internet terminal, which records information such as the time of placement, garbage type, and the user who placed the garbage (by linking it to the user's identity information), forming a comprehensive garbage placement database for subsequent traceability, management, and analysis. The internet terminal determines garbage priority based on factors such as the type, fill level, and storage time of the garbage in the smart trash can. For example, perishable waste, which spoils and odors easily over time, has a higher priority; recyclable waste, when storage space is sufficient, has a relatively lower priority. Based on the garbage priority of each bin, a garbage truck is dispatched for garbage collection. Furthermore, taking into account the priority of the garbage already loaded in the truck, a path planning method is used to adaptively plan the truck's route, taking into account factors such as traffic conditions, distance, and road restrictions, generating an optimal route. The internet terminal transmits the planned route instructions to the truck via wireless communication technology, controlling the truck to follow the planned route and transport the garbage to the corresponding garbage station. During the transportation process, the terminal can monitor the truck's location and driving status in real time to ensure the smooth completion of the garbage transportation task.

[0061] The above technical solution achieves the following: Classification rules are dynamically adjusted based on environmental data, making them more relevant to real-world scenarios and reducing classification errors caused by inappropriate rules. Classification results are determined by combining garbage data with the adjusted rules, and feedback is provided to users, helping them improve classification accuracy. This overall improvement in the precision of waste classification lays a solid foundation for subsequent resource recovery and processing. Waste disposal is fully monitored and recorded, allowing managers to view detailed information at any time, effectively overseeing waste sorting. When classification issues or waste disposal anomalies arise, records can be traced back to the specific disposal process and responsible individuals, facilitating timely resolution and enhancing the transparency and traceability of waste sorting management. Collection and removal are arranged based on waste priority, prioritizing high-priority waste, preventing the deterioration of perishable waste and other environmental impacts, and improving the timeliness of waste disposal. Adaptive route planning reduces empty runs and detours, reducing transportation costs and energy consumption, improving waste collection efficiency, and ensuring a more efficient allocation of collection resources. Users receive timely feedback on waste sorting results, understanding correct classification methods, and increasing environmental awareness and participation. At the same time, clear classification guidance and timely feedback enhance the user experience and encourage users to develop good waste sorting habits. The entire management process leverages internet terminals and smart devices to achieve automatic data collection, analysis, and processing, as well as intelligent planning of removal routes. This breaks away from the limitations of traditional manual management, promotes the development of intelligent and refined waste recycling and classification management, and improves the level of urban environmental management.

[0062] In one embodiment of the present invention, an Internet terminal dynamically adjusts garbage classification rules based on received environmental data, including:

[0063] The Internet terminal receives the environmental data uploaded by the smart trash can in real time; wherein the environmental data includes the ambient temperature and the ambient humidity;

[0064] After receiving each piece of garbage, the priority level 1 and 2 smart trash cans will upload the corresponding corruption warning coefficient of the garbage to the Internet terminal;

[0065] The Internet terminal dynamically adjusts the corruption coefficient value corresponding to each priority level one and level two smart trash can based on the corruption warning coefficient corresponding to the existing garbage in the priority level one and level two smart trash cans combined with environmental data, and obtains the adjusted corruption coefficient value corresponding to the priority level one and level two smart trash cans.

[0066] The working principle of the above technical solution is as follows: an internet terminal establishes a real-time communication connection with a smart trash can. The smart trash can's built-in sensors collect real-time environmental data, including information such as ambient temperature and humidity, and upload this data to the internet terminal. For priority level 1 and 2 smart trash cans, each time a piece of trash is received, a corresponding corruption warning coefficient is calculated based on factors such as the type and composition of the trash and uploaded to the internet terminal. This coefficient is used to preliminarily assess the degree of corruption that the trash may experience in the current environment. After receiving the corruption warning coefficients and environmental data corresponding to the existing trash in the priority level 1 and 2 smart trash cans, the internet terminal uses a specific algorithm or model, taking into account the ambient temperature and humidity, to dynamically adjust the corruption coefficient value for each trash can.

[0067] The above technical solution achieves this by dynamically adjusting the spoilage coefficient of each trash bin, taking into account environmental data and the spoilage early warning coefficient of each piece of trash. This allows for a more accurate assessment of the spoilage risk of the trash in the current environment. This helps promptly identify trash that may produce odors or harbor bacteria, allowing appropriate measures to be taken. Based on the adjusted spoilage coefficient, waste sorting rules can be further optimized. For example, trash bins with higher spoilage coefficients may require more frequent collection or special treatment of the trash within. This makes waste sorting rules more flexible and scientific, improving the overall efficiency of waste recycling and classification management. Promptly monitoring waste spoilage and taking appropriate measures helps reduce environmental pollution caused by odor and bacteria, improves the quality of the surrounding environment, and provides residents with a healthier and more comfortable living environment. By dynamically adjusting the spoilage coefficient of trash in smart trash bins, refined trash bin management is achieved. Removal resources can be rationally allocated based on the actual conditions of each bin, improving collection efficiency and reducing operating costs. The large amount of data generated by this technical solution, such as environmental data, spoilage early warning coefficients, and adjusted spoilage coefficients, provides rich data support for waste recycling and classification management. Managers can analyze and make decisions based on these data, continuously optimize management strategies and methods, and promote the development of waste recycling and classification management in a more intelligent and scientific direction.

[0068] In one embodiment of the present invention, an Internet terminal dynamically adjusts the corruption coefficient value corresponding to each priority level 1 and 2 smart trash can based on the corruption warning coefficient corresponding to the existing garbage in the priority level 1 and 2 smart trash cans in combination with environmental data, including:

[0069] The Internet terminal retrieves the uncollected waste from the first and second priority smart trash bins;

[0070] Retrieve the corruption warning coefficient of each uncollected priority level 1 and level 2 smart trash can corresponding to the associated garbage;

[0071] The corruption coefficient values of the first- and second-level smart trash cans are dynamically adjusted by using the corruption warning coefficient of the associated garbage corresponding to each first- and second-level smart trash can that has not been cleared, combined with the coefficient dynamic adjustment model.

[0072] The structure of the coefficient dynamic adjustment model is as follows:

[0073]

[0074] Where E(t+Δt) represents the corruption warning coefficient of the smart trash can at time t+Δt; E(t) represents the corruption warning coefficient of the smart trash can at time t; s represents the first weight coefficient; k represents the second weight coefficient, and the first weight coefficient is greater than the second weight coefficient, and s+k=1; n represents the number of types of garbage in the smart trash can; F(t) i It represents the corruption warning coefficient of the i-th type of garbage at time t; x represents the environmental deterioration sensitivity coefficient, and the value range of the environmental deterioration sensitivity coefficient is 0.3-0.6; J represents the garbage weight correlation coefficient; A(t) represents the acceleration of environmental deterioration, which is calculated by the temperature and humidity change rate: max() is used in this formula to ensure that the corruption coefficient value only increases and does not decrease, avoiding misjudgment of priority due to short-term environmental improvement.

[0075] A(t)=s·P t +k·P h

[0076] Among them, P t Indicates the temperature change rate; P h Indicates the rate of change of humidity.

[0077] At the same time, the garbage weight correlation coefficient is obtained by the following formula:

[0078]

[0079] Among them, m represents the number of garbage in the smart trash can; F i F represents the corruption warning coefficient corresponding to the i-th garbage; c Indicates the preset reference value of the corruption warning coefficient; T zi T represents the storage time of the i-th garbage in the smart trash can; c Indicates the preset duration reference value; M i Indicates the weight corresponding to the i-th garbage; M c Indicates the preset weight reference value.

[0080] The working principle of the above technical solution is as follows: the internet terminal selects those priority-one and priority-two smart trash bins that have not yet been emptied. For each selected priority-one and priority-two smart trash bin that has not yet been emptied, the internet terminal obtains the spoilage warning coefficient for each piece of trash associated with it. This spoilage warning coefficient is preliminarily calculated at the time of trash delivery based on factors such as the type and composition of the trash, and reflects the potential for spoilage in the initial state of the trash. The internet terminal inputs the obtained spoilage warning coefficient for each associated trash bin in the priority-one and priority-two smart trash bin into a dynamic coefficient adjustment model. This model combines previously received environmental data (such as ambient temperature and humidity) to consider the impact of environmental factors on the waste spoilage process. For example, high temperature and high humidity can accelerate waste spoilage. The model weights or modifies the initial spoilage warning coefficient based on these factors, dynamically adjusting the spoilage coefficient for each priority-one and priority-two smart trash bin to more accurately reflect the actual current spoilage status of the trash within the bin.

[0081] The above technical solution achieves this by combining environmental data with the waste corruption warning coefficient and utilizing a dynamic coefficient adjustment model to more accurately reflect the actual corruption status of uncollected primary and secondary smart trash bins. This helps promptly identify bins with high levels of waste corruption, allowing targeted treatment measures to be implemented to prevent problems such as odor and bacterial growth caused by waste corruption, which impact the surrounding environment and residents' lives. Accurate corruption coefficient values provide a crucial basis for waste collection decisions. Based on the adjusted corruption coefficient, internet terminals can prioritize waste bins with high corruption coefficients for collection, improving collection efficiency, rationally allocating collection resources, avoiding unnecessary collection work, and reducing operating costs. Dynamic adjustment of the corruption coefficient makes waste sorting management more refined and scientific. It can optimize waste sorting rules and processing processes based on the actual corruption status of waste in the bins, further improving waste sorting accuracy and waste treatment effectiveness. This technical solution fully leverages data and models to achieve intelligent monitoring and dynamic adjustment of waste corruption. By continuously accumulating and analyzing relevant data, we can continuously optimize the coefficient dynamic adjustment model, improve the level of intelligent management, and promote the development of waste recycling and classification management in a more efficient and environmentally friendly direction.

[0082] On the other hand, the above-mentioned coefficient dynamic adjustment model comprehensively considers multiple factors such as garbage type, garbage corruption warning coefficient, environmental deterioration sensitivity coefficient, garbage weight correlation coefficient and environmental deterioration acceleration. The comprehensive consideration of multiple factors can more comprehensively and accurately portray the corruption status of garbage in the trash can. Compared with the judgment of a single factor, it greatly improves the accuracy of the dynamic adjustment of the corruption coefficient value. The environmental deterioration acceleration A(t) is calculated by the temperature and humidity change rate, and is associated with the garbage corruption warning coefficient through the environmental deterioration sensitivity coefficient x. Temperature and humidity are important environmental factors that affect garbage corruption. Accurately calculating the environmental deterioration acceleration and incorporating it into the model enables the model to accurately reflect the impact of environmental changes on garbage corruption, so that the adjustment of the corruption coefficient value is consistent with the garbage corruption process in the actual environment, thereby improving accuracy. The calculation formula of the garbage weight correlation coefficient J incorporates the garbage storage time T zi , weight M i , corruption warning coefficient F i and other garbage attribute parameters. Garbage of different weights and storage times has different degrees of corruption. Calculating the correlation coefficient based on these factors can accurately reflect the contribution of individual differences in garbage to the overall corruption coefficient, making the adjusted corruption coefficient more accurate. At the same time, the model uses time as a variable, and uses E(t) and E(t+Δt) to represent the corruption warning coefficients of the smart trash can at different times, and performs iterative calculations based on the time interval Δt. As time goes on, the corruption coefficient value is continuously recalculated according to the various parameters at the new time, which can timely capture the dynamic changes in the garbage corruption state over time and respond to the garbage corruption process in real time. The environmental deterioration acceleration A(t) is calculated in real time based on the temperature and humidity change rate. Once the temperature and humidity change, A(t) changes immediately, and then affects the corruption coefficient value E(t+Δt) through the model. The garbage weight correlation coefficient J involves the garbage storage time T zi Parameters such as T zi Continuous changes will cause J to change in real time. If the garbage storage time increases, T zi becomes larger, J is adjusted accordingly, and the model updates the corruption coefficient value in a timely manner based on this, which can quickly perceive the changes in the state of garbage in the trash can and ensure the timeliness of the response to the garbage corruption situation.

[0083] In one embodiment of the present invention, an Internet terminal determines a garbage classification result based on received garbage data and environmental data in combination with dynamically adjusted garbage classification rules, and sends the garbage classification result to a user's mobile device for display, including:

[0084] The Internet terminal receives the garbage data uploaded by the user's mobile device in real time; wherein the garbage data includes a garbage image of each garbage;

[0085] The current environmental data is collected in real time through the smart trash can and sent to the Internet terminal in real time; wherein the environmental data includes the ambient temperature and humidity;

[0086] The Internet terminal automatically classifies garbage based on environmental data and garbage data combined with current garbage classification rules, and sends the classification results to the user's mobile device for display.

[0087] The working principle of the above technical solution is as follows: users upload an image of each piece of trash as trash data via a mobile device (such as a mobile phone). Users can use the device's camera function to send a clear image of the trash to an internet terminal. The smart trash can is equipped with sensors that monitor ambient temperature and humidity in real time. These sensors continuously collect ambient temperature and humidity information and transmit it in real time to the internet terminal. After receiving the trash image and environmental data, the internet terminal processes it in conjunction with the current dynamically adjusted trash classification rules. For the trash image, the terminal may use image recognition technology, such as convolutional neural networks (CNNs) within deep learning algorithms, to extract and analyze features of the trash in the image. At the same time, it considers the potential impact of environmental data (ambient temperature and humidity) on trash classification. For example, certain trash that easily deteriorates under specific temperature and humidity conditions may require special classification. Based on the recognition results of the trash image and the influence of the environmental data, the internet terminal automatically classifies the trash according to the current trash classification rules and determines the category to which the trash belongs. The internet terminal then sends the automatic classification results to the user's mobile device via network communication technologies (such as Wi-Fi, mobile data networks, etc.). Users can view the trash classification results on their mobile device to understand how their trash should be correctly classified.

[0088] The above technical solution achieves the following benefits: Using image recognition technology to analyze garbage images enables more objective and accurate identification of garbage types, reducing classification errors caused by inaccurate user judgment. It also considers the impact of environmental data on garbage classification, making classification more scientific and rational. For example, in hot and humid environments, the classification and processing of perishable garbage can be more timely, preventing environmental factors from causing garbage to deteriorate, produce odors, or breed bacteria. Users simply need to take a picture of the garbage with their mobile phone and upload it to quickly obtain classification results, making the operation simple and convenient. This real-time feedback allows users to promptly understand whether their classification is correct, enhancing their enthusiasm and initiative in garbage sorting. It provides users with an intuitive and easy-to-understand guidance on garbage sorting, helping them better master garbage sorting knowledge and develop good garbage sorting habits. The automatic classification function of internet terminals reduces the workload of manual sorting and improves garbage sorting efficiency. Furthermore, by analyzing large amounts of garbage and environmental data, garbage sorting rules can be further optimized to better reflect actual conditions. This enables real-time monitoring and management of garbage sorting, enabling relevant departments to promptly understand the overall status of garbage sorting and implement targeted measures for improvement and optimization. By providing accurate classification results and convenient classification guidance, it helps to raise public awareness of the importance of waste sorting and promote environmental awareness throughout society. More people can correctly sort their waste, which is beneficial to resource recycling and environmental protection, and promotes sustainable development.

[0089] In one embodiment of the present invention, an Internet terminal automatically classifies garbage based on environmental data and garbage data in combination with current garbage classification rules, and sends the classification results to a user's mobile device for display, including:

[0090] The Internet terminal retrieves image data corresponding to each piece of garbage from the garbage data, identifies the image data, and obtains the garbage category corresponding to the image data;

[0091] After determining the garbage category, the Internet terminal retrieves the current environmental data sent by the smart trash can;

[0092] Use the garbage category and current environmental data corresponding to each garbage to obtain the corruption warning coefficient corresponding to each garbage;

[0093] The corruption warning coefficient corresponding to each garbage is obtained by the following formula:

[0094]

[0095] Where F(t) irepresents the corruption warning coefficient of the i-th type of garbage at time t. The larger the value, the higher the corruption risk, which requires priority treatment. C0 represents the preset baseline corruption coefficient of the i-th type of garbage, which can be determined by experiment (a certain type of wet kitchen waste is 1.5, a certain type of dry kitchen waste is 1.1, and a certain type of plastic waste is 0.6). α represents the temperature influence weight coefficient, and its value range is 1.2-2.3. T represents the ambient temperature at time t. T ref represents the corruption acceleration reference temperature, which is set to the critical temperature of local microbial activity and obtained through experiments. For example, it is 27°C in a place in the north and 25°C in a place in the south. β represents the humidity deviation penalty coefficient, which controls the increase in corruption risk when the humidity deviates from the optimal value. The value range of the humidity deviation penalty coefficient is 0.01-0.04. opt Indicates the optimal decomposition humidity of the i-th type of garbage, which varies significantly among different garbage (e.g., wet kitchen garbage = 70%, plastic = no effect, so it is set to 0);

[0096] The priority of the smart trash can corresponding to each garbage classification is determined according to the corruption warning coefficient corresponding to each garbage classification, and the location and priority information of the smart trash can corresponding to the priority of each garbage classification is sent to the user's mobile device for display, so as to guide the user to classify and place the garbage; wherein, the priority of the smart trash can includes level one, level two and level three, specifically, the garbage with level one has the greatest corruption intensity and the fastest corruption speed, such as wet kitchen waste; the garbage with level two has average corruption intensity and average corruption speed, such as dry kitchen waste; the garbage with level three has the lowest corruption intensity and the lowest corruption speed, such as packaging bags, plastic, glass, etc., and there are multiple smart trash cans with level one, level two and level three priorities within the garbage placement range.

[0097] The working principle of the above technical solution is as follows: the internet terminal extracts the image data of each piece of trash from the garbage data uploaded by the user's mobile device. Using image recognition technology, such as convolutional neural network algorithms, it analyzes and identifies the features of the trash in the image, thereby determining the garbage category corresponding to the image data. After determining the garbage category, the internet terminal receives the current environmental data uploaded by the smart trash can, which primarily includes ambient temperature and humidity information. Based on the corresponding corruption warning coefficient of each piece of trash, the priority of the smart trash can corresponding to each garbage category is determined. Garbage is divided into primary, secondary, and tertiary levels. Priority 1 garbage has the highest corruption intensity and fastest corruption rate, followed by priority 2 garbage, and the lowest by priority 3 garbage. The location and priority information of the smart trash can corresponding to each garbage category are then displayed to the user's mobile device to guide the user in the correct garbage sorting and placement.

[0098] At the same time, the exponential function is used in the above technical solution Describing temperature effects:

[0099] When T>T ref , the spoilage rate increases exponentially with every 1°C increase in temperature (in line with the law of microbial activity);

[0100] When T <T ref , lower temperatures will inhibit corruption.

[0101] And, by 1+β·(HH opt ) 2 Characterize the influence of humidity and set the humidity secondary penalty term:

[0102] Humidity deviates from the optimal value H opt When the risk of corruption increases (whether it is too high or too low), the risk of corruption increases;

[0103] For garbage that is not sensitive to humidity (such as plastic), set H opt =0, the humidity term degenerates to 1+β·H 2 , avoid interference.

[0104] The above technical solution achieves the following: Using image recognition technology to sort garbage effectively reduces classification errors caused by user misjudgment and improves garbage sorting accuracy. Furthermore, by comprehensively factoring in environmental data to calculate the corruption warning coefficient, the system further optimizes the scientific nature of the classification and ensures that garbage is placed correctly. By calculating the corruption warning coefficient and prioritizing trash bins, garbage with a high risk of corruption is prioritized, preventing problems such as prolonged storage leading to spoilage, odor, and bacterial growth, thereby improving overall garbage disposal efficiency. Users receive clear guidance on garbage sorting on their mobile devices, including the location and priority of corresponding trash bins, making it easier and more accurate to sort and place their garbage. This intuitive guidance helps enhance users' environmental awareness and fosters good garbage sorting habits. Determining trash bin priorities based on the corruption warning coefficient ensures a more efficient allocation of recycling resources. This technical solution generates a large amount of garbage data, environmental data, and corruption warning coefficient information, which can inform decision-making by waste recycling management departments. Analysis of this data can further optimize garbage sorting rules, trash bin layout, and collection plans, achieving refined and intelligent waste recycling classification management.

[0105] At the same time, the inherent properties of waste are taken into account: by presetting a baseline spoilage coefficient, C0, for category i, different baseline values are assigned to different types of waste (e.g., 1.5 for wet kitchen waste, 1.1 for dry kitchen waste, and 0.6 for certain types of plastic waste), reflecting the differences in the inherent spoilage characteristics of the waste. This serves as the basis for calculating the spoilage warning coefficient, fundamentally differentiating the spoilage risks of different types of waste and laying the foundation for accuracy. The temperature impact weighting coefficient α works in conjunction with the ambient temperature T and the spoilage acceleration reference temperature Tref. α ranges from 1.2 to 2.3, dynamically reflecting the impact of temperature on waste spoilage based on temperature fluctuations in different regions and seasons. For example, when the ambient temperature T approaches or exceeds the spoilage acceleration reference temperature Tref (e.g., the critical temperature for local microbial activity), waste spoilage accelerates. This formula accurately quantifies this temperature-induced increase in spoilage risk, ensuring that the spoilage warning coefficient more closely reflects the actual spoilage risk. The humidity deviation penalty coefficient β and the optimal spoilage humidity Hopt for category i jointly measure the effect of humidity on waste spoilage. The value range of β is 0.01-0.04, which controls the increase in corruption risk when humidity deviates from the optimal value. Different types of garbage have different optimal corruption humidity levels (e.g., 70% for wet kitchen waste and 0% for plastic). When the actual humidity deviates from Hopt, the change in corruption risk caused by humidity changes can be accurately calculated, allowing the corruption warning coefficient to accurately reflect the corruption of garbage under the influence of humidity. This formula combines multiple factors such as the garbage's own baseline corruption coefficient, temperature, and humidity, comprehensively considering the key factors affecting garbage corruption. Rather than viewing any one factor in isolation, these factors are organically combined through mathematical formulas, so that the calculated corruption warning coefficient F(t)i can comprehensively and accurately reflect the corruption risk of garbage in the current environment, providing reliable and accurate data support for subsequent garbage sorting and placement and smart trash can priority determination.

[0106] In one embodiment of the present invention, the priority of the smart trash bin corresponding to each garbage classification is determined based on the corruption warning coefficient corresponding to each garbage, including:

[0107] Compare the corruption warning coefficient corresponding to the garbage with the current corruption coefficient value corresponding to each smart trash can;

[0108] When the corruption warning coefficient corresponding to the garbage is not lower than the corruption coefficient value of the smart garbage bin with a priority of level one, the garbage with a corruption warning coefficient not lower than the corruption coefficient value of the smart garbage bin with a priority of level one is determined to be level one garbage;

[0109] When the corruption warning coefficient corresponding to the garbage is lower than the corruption coefficient value of the smart garbage bin with the first priority, but not lower than the corruption coefficient value of the smart garbage bin with the second priority, the garbage is determined to be second-level garbage;

[0110] When the corruption warning coefficient corresponding to the garbage is lower than the corruption coefficient value of the smart garbage bin with a second priority, the garbage with a corruption warning coefficient lower than the corruption coefficient value of the smart garbage bin with a second priority is determined to be third-level garbage.

[0111] The working principle of this technical solution is to set a corruption warning coefficient for each piece of trash, and each smart trash can has a corresponding corruption coefficient value. These two coefficients are the basic data of the entire solution and are used for subsequent comparison and judgment.

[0112] The garbage's corruption warning coefficient is then compared with the corruption coefficient values of the smart trash bins of different priorities. According to the set rules, if the garbage's corruption warning coefficient is not lower than the corruption coefficient value of the smart trash bin with a first-priority level, then the garbage is determined to be first-level garbage, which means it needs to be placed in the first-level smart trash bin first. If the garbage's corruption warning coefficient is lower than the corruption coefficient value of the first-level smart trash bin, but not lower than the corruption coefficient value of the second-level smart trash bin, then the garbage is determined to be second-level garbage and should be placed in the second-level smart trash bin. If the garbage's corruption warning coefficient is lower than the corruption coefficient value of the second-level smart trash bin, then the garbage is determined to be third-level garbage and will be placed in the third-level smart trash bin. Through such comparison and judgment, the priority of the corresponding smart trash bin when the garbage is sorted and placed is determined according to the garbage's corruption level (reflected by the corruption warning coefficient).

[0113] The above technical solution achieves the following: By quantifying the degree of waste spoilage (corruption early warning coefficient) and comparing it with the corruption coefficient values of smart trash cans, waste can be more scientifically allocated to smart trash cans of different priorities. This helps improve the accuracy and efficiency of waste sorting and ensures more orderly waste disposal. Garbage with a faster decay rate (higher corruption early warning coefficient) is prioritized for placement in the corresponding smart trash cans. This allows waste handlers to prioritize this waste, reducing odor and bacterial growth caused by corruption, and improving the efficiency of the entire waste disposal system. Promptly handling high-risk waste effectively reduces the risk of environmental pollution during storage, such as reducing leachate production and harmful gas emissions, thereby protecting the environment and residents' quality of life. By comparing smart trash cans and correlation coefficients, this technical solution enables intelligent decision-making for waste sorting and placement, reducing manual intervention and improving the automation and accuracy of management, in line with the development trend of intelligent urban management.

[0114] In one embodiment of the present invention, the Internet terminal monitors and records each type of garbage disposal, including:

[0115] The Internet terminal determines the target trash bin;

[0116] The garbage placement is monitored and recorded by detecting the weight change of the target garbage bin and determining the similarity between the image data of the garbage placed and the image data uploaded by the user's mobile device.

[0117] Specifically, the steps for the Internet terminal to monitor and record each type of garbage disposal are as follows:

[0118] The smart trash bin corresponding to the priority of each trash category identified by the user's mobile device is used as the target trash bin;

[0119] Associate the user's mobile device with the target trash bin corresponding to each priority level and monitor the location of the user's mobile device in real time;

[0120] When the user moves the mobile device close to the target trash can, the weight change of the target trash can is determined by the built-in weight sensor of the target trash can;

[0121] When the weight of the target trash can changes, the built-in camera of the target trash can takes a picture of the trash that causes the weight change to obtain trash image data;

[0122] Comparing the junk image data with image data uploaded by the user's mobile device for similarity;

[0123] When it is determined that the similarity value between the garbage image data and the image data uploaded by the user's mobile device meets the preset similarity threshold, it is determined that the user has completed the disposal of the garbage, and the corruption warning coefficient corresponding to the completed garbage is associated with the target garbage bin where it is disposed.

[0124] The working principle of the above technical solution is as follows: The internet terminal determines the target trash can based on the priority level of each trash category identified by the user's mobile device. This serves as the basis for the entire monitoring and recording process and clarifies the specific trash can for subsequent operations. The user's mobile device is associated with the corresponding target trash can and its location is monitored in real time. This ensures that the subsequent monitoring process is triggered promptly when the user approaches the target trash can, ensuring that relevant operations are only performed when the user is near the correct trash can, thereby improving the accuracy and targeting of monitoring. When the user's mobile device approaches the target trash can, the weight sensor built into the target trash can is used to determine the weight change of the trash can. Weight change is an important indicator for determining whether trash has been placed, as the weight of the trash can only change when an object is placed in it, providing a preliminary indication of trash placement. Once a weight change is detected in the target trash can, the trash can's built-in camera takes a photo of the trash that caused the weight change, capturing trash image data. This image data is then compared with the image data uploaded by the user's mobile device for similarity. This step confirms whether the discarded garbage matches the garbage information previously identified and uploaded by the user on their mobile device. This accuracy is further verified through image similarity. When the similarity between the garbage image data and the image data uploaded by the user's mobile device meets a preset similarity threshold, the user is deemed to have completed the garbage disposal. The corresponding corruption warning coefficient for the discarded garbage is then associated with the target trash can. This not only records the user's successful garbage disposal but also links the garbage corruption warning coefficient to the specific trash can, providing data support for subsequent garbage management based on corruption levels.

[0125] The above technical solution achieves the following: By combining trash bin weight change detection with image data similarity determination, it can accurately monitor and record users' waste placement behavior for each type of waste, effectively avoiding misjudgments and erroneous recordings, and improving the accuracy of waste placement monitoring. Comparing trash image data uploaded by users' mobile devices with images captured by the trash bins helps ensure that users place their waste in the correct bins according to classification requirements, promoting the effective implementation of waste sorting policies and improving waste sorting accuracy. The entire process, leveraging internet terminals, sensors, and cameras, automates and intelligently monitors and records waste placement, reducing manual intervention, improving management efficiency, and lowering management costs. It also provides more scientific and accurate data support for urban waste management, helping to optimize waste disposal processes and resource allocation. This monitoring and recording method allows users to more clearly understand that their waste placement behavior is being monitored and recorded, thereby enhancing their environmental awareness and sense of responsibility, encouraging them to more consciously comply with waste sorting regulations, and playing a positive role in promoting good environmental habits throughout society.

[0126] In one embodiment of the present invention, adaptive planning of a removal route of a removal vehicle is performed based on the comprehensive priority of garbage in the removal vehicle, including:

[0127] Comparing the corruption coefficient value of the smart trash can with a preset coefficient threshold;

[0128] When the corruption coefficient value of the smart trash can reaches or exceeds a preset coefficient threshold, it is determined that the smart trash can needs to be cleaned;

[0129] When each garbage collection truck completes garbage loading according to the garbage loading volume, it retrieves the corruption coefficient values of all cleaned smart garbage bins corresponding to the garbage currently loaded by the garbage collection truck;

[0130] Obtaining a cleaning priority coefficient using the corruption coefficient values of all cleaned smart trash cans corresponding to the currently loaded garbage;

[0131] Retrieve the existing path in the database of the Internet terminal;

[0132] Obtaining a garbage transportation impact coefficient by combining the historical average vehicle speed of the current route of the existing routes in the database with a removal priority coefficient;

[0133] The path corresponding to the minimum value of the garbage transportation impact coefficient is used as the current collection path.

[0134] The removal priority coefficient is obtained by the following formula:

[0135]

[0136] Among them, Q represents the cleaning priority coefficient; N represents the number of all cleaned smart trash cans; E peaki represents the peak value of the corruption coefficient of the i-th smart trash can before the cleaning instruction is generated; y represents the time delay penalty coefficient, and the value range of the time delay penalty coefficient is 0.01-0.03; Δt b represents the time interval from the first time the corruption coefficient value of the smart trash bin exceeds the threshold to the time it is cleared; r represents the sudden corruption penalty weight, and the sudden corruption penalty weight range is 1.3-2.1; E fi represents the peak value of the corruption coefficient of the i-th smart trash can at the time of cleaning execution; E y Indicates the preset corruption coefficient peak threshold;

[0137] In addition, the garbage transportation impact coefficient is obtained by the following formula:

[0138]

[0139] Among them, I represents the impact coefficient of garbage transportation; D represents the path length corresponding to the path; Q represents the priority coefficient of garbage collection; V represents the historical average speed of the path; C r Indicates the current remaining garbage loading capacity or volume of the garbage collection truck; P v Indicates the historical average vehicle speed change rate (percentage) corresponding to each path.

[0140] The working principle of the above technical solution is to compare the corruption coefficient of a smart trash bin with a preset coefficient threshold. When the corruption coefficient of a smart trash bin reaches or exceeds the preset coefficient threshold, the garbage in that bin is determined to be highly corrupt and requires removal, thereby clarifying the target of the removal task. After each collection truck completes loading according to the garbage load, the corruption coefficient values of all cleaned smart trash bins corresponding to the garbage currently loaded by the truck are retrieved. These corruption coefficients are used to calculate a collection priority coefficient, which comprehensively reflects the overall corruption level of the garbage in that load and is used to determine the collection priority of the garbage in that truck. The existing route information in the internet terminal database is first retrieved. Then, the garbage transportation impact coefficient is calculated by combining the historical average vehicle speed of the current route with the previously calculated collection priority coefficient. This coefficient takes into account the route's speed and the garbage collection priority, comprehensively assessing the impact of different routes on the current collection task. The route with the lowest garbage transportation impact coefficient is selected as the current collection route, thereby selecting the route with the highest efficiency and lowest impact on the current garbage collection task, thus achieving adaptive collection route planning.

[0141] The above technical solution prioritizes collection based on the trash can's corruption coefficient, prioritizing the removal of highly corrupted trash, thus avoiding the problems associated with excessive corruption. Furthermore, by incorporating historical average vehicle speeds along the route, the collection route is planned, enabling trucks to complete their tasks more quickly and improving overall collection efficiency. By scientifically planning collection routes, trucks reduce time wasted and unnecessary mileage during transportation, lowering operating costs such as fuel consumption and vehicle wear and tear, and improving resource utilization. This solution leverages the corruption coefficient of intelligent trash cans, database-based route information, and the comprehensive calculation of various coefficients to achieve adaptive collection route planning, reducing manual intervention, enhancing the intelligence of waste collection management, and making waste collection more scientific and accurate. Timely removal of highly corrupted trash helps reduce odor, bacterial growth, and environmental pollution caused by corruption, effectively improving urban environmental quality and creating a healthier and more comfortable living environment for residents. By comparing the corruption coefficient of each smart trash bin against a preset threshold, the system determines whether it requires collection. This allows accurate identification of bins with a level of garbage corruption requiring treatment, avoiding misjudgments and missed detections, and enabling precise targeting of collection efforts to bins in need. The collection priority coefficient formula comprehensively considers factors such as the peak corruption coefficient of the smart bin, time delay, time delay penalty coefficient, and sudden corruption penalty weight. For example, the peak corruption coefficient reflects the severity of garbage corruption, while the time delay penalty reflects the impact of prolonged garbage collection. This provides a comprehensive and rational assessment of the collection priority of each bin, providing an accurate basis for subsequent route planning. The waste transport impact coefficient formula incorporates route length, collection priority coefficient, historical average vehicle speed, vehicle speed change rate, and remaining loading capacity. This comprehensive analysis accurately assesses the impact of different routes on the current collection task, identifying the optimal route that best suits the current collection vehicle and garbage situation, and improving route planning accuracy. Based on accurate collection priority coefficients, garbage with high levels of corruption and requiring prompt disposal is prioritized for collection. This reduces the problems caused by excessive corruption, improves overall waste disposal efficiency, and prevents odor and pollution from spoilage that impacts the surrounding environment. The optimal route is determined by the waste transportation impact coefficient, minimizing time wasted by trucks during transportation, enabling trucks to complete their tasks more quickly and shortening the time it takes to complete a single collection trip, thereby improving collection efficiency per unit time. Accurate collection demand determination and priority calculation optimize the allocation of truck resources, avoiding unnecessary collection of low-corruption bins that don't require immediate processing. This allows trucks to focus on critical tasks and improves the efficiency of resources such as trucks. Routing based on the waste transportation impact coefficient reduces the need for trucks to travel on congested roads or unsuitable routes, reducing fuel consumption and vehicle wear, and improving overall resource utilization.

[0142] The embodiment of the present invention proposes a mobile Internet-driven garbage collection and classification management system, such as Figure 2 As shown, the garbage recycling classification management system includes:

[0143] A classification rule dynamic adjustment module is used by Internet terminals to dynamically adjust garbage classification rules based on received environmental data;

[0144] A classification processing module is used for the Internet terminal to determine a garbage classification result based on the received garbage data and environmental data in combination with the dynamically adjusted garbage classification rules, and to send the garbage classification result to the user's mobile device for display;

[0145] The delivery monitoring module is used to monitor and record the delivery of each type of garbage;

[0146] The path planning module is used by the Internet terminal to control the garbage collection truck to collect garbage according to the garbage priority in the smart garbage bin, and to adaptively plan the collection path of the garbage collection truck based on the comprehensive situation of the garbage priority in the collection truck;

[0147] The garbage collection truck transport control module is used by the Internet terminal to control the garbage collection truck to transport garbage to the garbage station according to the planned route.

[0148] The working principle of the above technical solution is as follows: The internet terminal receives environmental data in real time and dynamically modifies the existing waste classification rules to ensure that the classification rules are consistent with current environmental conditions. The internet terminal receives garbage data and environmental data and inputs them into the dynamically adjusted waste classification rule model. Through model calculation and analysis, the garbage classification category is determined, thereby determining the garbage classification results. Subsequently, using mobile internet communication technology, the garbage classification results are sent to the user's mobile device in intuitive formats such as images, text, and voice. Users can view them in real time to confirm whether their garbage has been placed accurately. Various sensors (such as cameras, weight sensors, and RFID sensors) are deployed at garbage placement points, such as smart trash cans, to monitor each type of garbage placement in real time. Cameras identify the appearance of garbage to assist in classification, weight sensors record the weight of the garbage placed, and RFID sensors identify garbage container information. Monitoring data is transmitted in real time to the internet terminal, which records information such as the time of placement, garbage type, and the user who placed the garbage (by linking it to the user's identity information), forming a comprehensive garbage placement database for subsequent traceability, management, and analysis. The internet terminal determines garbage priority based on factors such as the type, fill level, and storage time of the garbage in the smart trash can. For example, perishable waste, which spoils and odors easily over time, has a higher priority; recyclable waste, when storage space is sufficient, has a relatively lower priority. Based on the garbage priority of each bin, a garbage truck is dispatched for garbage collection. Furthermore, taking into account the priority of the garbage already loaded in the truck, a path planning method is used to adaptively plan the truck's route, taking into account factors such as traffic conditions, distance, and road restrictions, generating an optimal route. The internet terminal transmits the planned route instructions to the truck via wireless communication technology, controlling the truck to follow the planned route and transport the garbage to the corresponding garbage station. During the transportation process, the terminal can monitor the truck's location and driving status in real time to ensure the smooth completion of the garbage transportation task.

[0149] The above technical solution achieves the following: Classification rules are dynamically adjusted based on environmental data, making them more relevant to real-world scenarios and reducing classification errors caused by inappropriate rules. Classification results are determined by combining garbage data with the adjusted rules, and feedback is provided to users, helping them improve classification accuracy. This overall improvement in the precision of waste classification lays a solid foundation for subsequent resource recovery and processing. Waste disposal is fully monitored and recorded, allowing managers to view detailed information at any time, effectively overseeing waste sorting. When classification issues or waste disposal anomalies arise, records can be traced back to the specific disposal process and responsible individuals, facilitating timely resolution and enhancing the transparency and traceability of waste sorting management. Collection and removal are arranged based on waste priority, prioritizing high-priority waste, preventing the deterioration of perishable waste and other environmental impacts, and improving the timeliness of waste disposal. Adaptive route planning reduces empty runs and detours, reducing transportation costs and energy consumption, improving waste collection efficiency, and ensuring a more efficient allocation of collection resources. Users receive timely feedback on waste sorting results, understanding correct classification methods, and increasing environmental awareness and participation. At the same time, clear classification guidance and timely feedback enhance the user experience and encourage users to develop good waste sorting habits. The entire management process leverages internet terminals and smart devices to achieve automatic data collection, analysis, and processing, as well as intelligent planning of removal routes. This breaks away from the limitations of traditional manual management, promotes the development of intelligent and refined waste recycling and classification management, and improves the level of urban environmental management.

[0150] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A mobile Internet-driven garbage collection and classification management method, characterized in that: The garbage collection and classification management method includes: Internet terminals dynamically adjust garbage classification rules based on received environmental data; The Internet terminal determines a garbage classification result based on the received garbage data and environmental data in combination with the dynamically adjusted garbage classification rules, and sends the garbage classification result to the user's mobile device for display; Internet terminals monitor and record each type of garbage disposal; The Internet terminal controls the garbage collection truck according to the garbage priority in the smart garbage bin, and adaptively plans the collection route of the garbage collection truck based on the comprehensive situation of the garbage priority in the garbage collection truck; The Internet terminal controls the garbage collection truck to transport the garbage to the garbage station according to the planned route.

2. The mobile Internet-driven garbage collection and classification management method according to claim 1, characterized in that: The Internet terminal dynamically adjusts the garbage classification rules based on the received environmental data, including: The Internet terminal receives the environmental data uploaded by the smart trash can in real time; wherein the environmental data includes the ambient temperature and the ambient humidity; After receiving each piece of garbage, the priority level 1 and 2 smart trash cans will upload the corresponding corruption warning coefficient of the garbage to the Internet terminal; The Internet terminal dynamically adjusts the corruption coefficient value corresponding to each priority level one and level two smart trash can based on the corruption warning coefficient corresponding to the existing garbage in the priority level one and level two smart trash cans combined with environmental data, and obtains the adjusted corruption coefficient value corresponding to the priority level one and level two smart trash cans.

3. The mobile Internet-driven garbage collection and classification management method according to claim 2, characterized in that: The Internet terminal dynamically adjusts the corruption coefficient value corresponding to each priority level 1 and level 2 smart trash can based on the corruption warning coefficient corresponding to the existing garbage in the priority level 1 and level 2 smart trash cans and the environmental data, including: The Internet terminal retrieves the uncollected waste from the first and second priority smart trash bins; Retrieve the corruption warning coefficient of each uncollected priority level 1 and level 2 smart trash can corresponding to the associated garbage; The corruption coefficient values of the first- and second-level smart trash cans are dynamically adjusted by using the corruption warning coefficient of the associated garbage corresponding to each first- and second-level smart trash can that has not been cleared, combined with the coefficient dynamic adjustment model.

4. The mobile Internet-driven garbage collection and classification management method according to claim 1, characterized in that: The Internet terminal determines a garbage classification result based on the received garbage data and environmental data in combination with the dynamically adjusted garbage classification rules, and sends the garbage classification result to the user's mobile device for display, including: The Internet terminal receives the garbage data uploaded by the user's mobile device in real time; wherein the garbage data includes a garbage image of each garbage; The current environmental data is collected in real time through the smart trash can and sent to the Internet terminal in real time; wherein the environmental data includes the ambient temperature and humidity; The Internet terminal automatically classifies garbage based on environmental data and garbage data combined with current garbage classification rules, and sends the classification results to the user's mobile device for display.

5. The mobile Internet-driven garbage collection and classification management method according to claim 2, characterized in that: The Internet terminal automatically classifies garbage based on environmental data and garbage data combined with current garbage classification rules, and sends the classification results to the user's mobile device for display, including: The Internet terminal retrieves image data corresponding to each piece of garbage from the garbage data, identifies the image data, and obtains the garbage category corresponding to the image data; After determining the garbage category, the Internet terminal retrieves the current environmental data sent by the smart trash can; Use the garbage category and current environmental data corresponding to each garbage to obtain the corruption warning coefficient corresponding to each garbage; The priority of the smart trash can corresponding to each garbage classification is determined according to the corruption warning coefficient corresponding to each garbage classification, and the location and priority information of the smart trash can corresponding to the priority of each garbage classification is sent to the user's mobile device for display.

6. The mobile Internet-driven garbage collection and classification management method according to claim 5, characterized in that: The priority of the smart trash bins corresponding to each type of garbage is determined based on the corruption warning coefficient corresponding to each type of garbage, including: Compare the corruption warning coefficient corresponding to the garbage with the current corruption coefficient value corresponding to each smart trash can; When the corruption warning coefficient corresponding to the garbage is not lower than the corruption coefficient value of the smart garbage bin with a priority of level one, the garbage with a corruption warning coefficient not lower than the corruption coefficient value of the smart garbage bin with a priority of level one is determined to be level one garbage; When the corruption warning coefficient corresponding to the garbage is lower than the corruption coefficient value of the smart garbage bin with the first priority, but not lower than the corruption coefficient value of the smart garbage bin with the second priority, the garbage is determined to be second-level garbage; When the corruption warning coefficient corresponding to the garbage is lower than the corruption coefficient value of the smart garbage bin with a second priority, the garbage with a corruption warning coefficient lower than the corruption coefficient value of the smart garbage bin with a second priority is determined to be third-level garbage.

7. The mobile Internet-driven garbage collection and classification management method according to claim 1, characterized in that: Internet terminals monitor and record each type of garbage disposal, including: The Internet terminal determines the target trash bin; The garbage placement is monitored and recorded by detecting the weight change of the target garbage bin and determining the similarity between the image data of the garbage placed and the image data uploaded by the user's mobile device.

8. The mobile Internet-driven garbage collection and classification management method according to claim 1, characterized in that: Adaptive planning of the truck's transportation path is performed based on the overall priority of the garbage in the truck, including: Comparing the corruption coefficient value of the smart trash can with a preset coefficient threshold; When the corruption coefficient value of the smart trash can reaches or exceeds a preset coefficient threshold, it is determined that the smart trash can needs to be cleaned; When each garbage collection truck completes garbage loading according to the garbage loading volume, it retrieves the corruption coefficient values of all cleaned smart garbage bins corresponding to the garbage currently loaded by the garbage collection truck; Obtaining a cleaning priority coefficient using the corruption coefficient values of all cleaned smart trash cans corresponding to the currently loaded garbage; Retrieve the existing path in the database of the Internet terminal; Obtaining a garbage transportation impact coefficient by combining the historical average vehicle speed of the current route of the existing routes in the database with a removal priority coefficient; The path corresponding to the minimum value of the garbage transportation impact coefficient is used as the current collection path.

9. A mobile Internet-driven garbage collection and classification management system, characterized in that: The waste recycling classification management system includes: A classification rule dynamic adjustment module is used by Internet terminals to dynamically adjust garbage classification rules based on received environmental data; A classification processing module is used for the Internet terminal to determine a garbage classification result based on the received garbage data and environmental data combined with the dynamically adjusted garbage classification rules, and send the garbage classification result to the user's mobile device for display; The delivery monitoring module is used to monitor and record the delivery of each type of garbage; The path planning module is used by the Internet terminal to control the garbage collection truck to collect garbage according to the garbage priority in the smart garbage bin, and to adaptively plan the collection path of the garbage collection truck based on the comprehensive situation of the garbage priority in the collection truck; The garbage collection truck transport control module is used by the Internet terminal to control the garbage collection truck to transport garbage to the garbage station according to the planned route.

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