Solid waste full life cycle management method and system

Abstract

The invention relates to the technical field of solid waste monitoring and management, in particular to a full-life-cycle management method and system for solid waste. The method comprises the following steps: collecting waste source monitoring data based on a sensor, performing distributed storage management according to the waste source monitoring data, and constructing a solid waste real-time management database; according to the solid waste real-time management database, waste recovery types are predicted one by one, dynamic sorting control treatment is carried out, and therefore a dynamic solid waste sorting strategy is generated; obtaining a solid waste recovery processing log; carrying out urban garbage disposal recycle bin node identification on the solid waste recycling log, and constructing an urban recycle bin distribution map; and identifying positioning information of each waste source according to the waste source monitoring data, and making a dynamic solid waste transportation decision based on the distribution map of the urban recycle bin to generate a dynamic solid waste transportation strategy. According to the invention, the solid waste recovery efficiency and real-time monitoring management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of solid waste monitoring and management, and in particular to a full life cycle management method and system for solid waste. Background Art

[0002] As global environmental problems become increasingly severe, the management of solid waste has become an important technical issue of widespread concern. Traditional solid waste management methods mainly rely on manual sorting, manual recycling and simple treatment methods. These methods often have problems such as low treatment efficiency, low resource recovery rate, and high management costs, and are difficult to meet the requirements of modern society for waste treatment and resource recovery. Especially with the acceleration of urbanization and the improvement of industrialization, the types and quantities of solid waste have shown diversified and large-scale growth, and traditional treatment methods have been unable to cope with the increasingly complex waste management needs.

[0003] At the same time, waste classification, recycling, transportation and treatment are all difficult to manage. In the process of source monitoring, classification and sorting, transportation and scheduling of waste, traditional means are often unable to achieve real-time monitoring and dynamic scheduling, and due to the lag and inaccuracy of information, it often leads to waste of resources and low processing efficiency. Problems such as uneven distribution of waste recycling stations, unreasonable planning of transportation routes, and low sorting efficiency have further aggravated the difficulty and complexity of solid waste management.

[0004] Therefore, with the continuous development and application of information technology, the Internet of Things, big data, artificial intelligence and other technologies, there is an urgent need for an intelligent and automated method for the entire life cycle management of solid waste. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a full life cycle management method and system for solid waste to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a method for the full life cycle management of solid waste, comprising the following steps: Step S1: collecting waste source monitoring data based on sensors, performing distributed storage management based on the waste source monitoring data, and building a solid waste real-time management database; Step S2: predicting waste recycling types one by one according to the solid waste real-time management database, and performing dynamic sorting control processing, thereby generating a dynamic solid waste sorting strategy; Step S3: obtaining a solid waste recycling log; identifying the nodes of garbage disposal and recycling stations in the city based on the solid waste recycling log, and performing distribution positioning fitting to construct a distribution map of recycling stations in the city; Step S4: Identify the location information of each waste source based on the waste source monitoring data, and make dynamic solid waste transportation decisions based on the distribution map of recycling stations in the city to generate a dynamic solid waste transportation strategy; Step S5: making intelligent recycling processing decisions and intelligent recycling optimization according to the real-time solid waste management database to generate an intelligent recycling optimization strategy; Step S6: Based on the recycling feedback information, intelligent collaborative learning and global monitoring optimization are performed on the intelligent recycling optimization strategy, dynamic solid waste transportation strategy and dynamic solid waste sorting strategy to build a full-cycle monitoring optimization model.

[0007] The present invention uses sensors to monitor the data of the source of waste generation in real time, and can accurately understand the amount and type of waste at various locations and times. Distributed storage management improves the security and reliability of data and avoids the risk of single point failure. The establishment of a real-time database makes the collection, query and analysis of waste information more efficient, which helps to provide accurate data support for subsequent management and decision-making. The data in the real-time database can be used to accurately predict the type of waste recycling to avoid human sorting errors. By adopting dynamic sorting control technology, the sorting strategy can be adjusted in real time according to the type and quantity of waste to improve sorting efficiency and accuracy. The dynamic sorting strategy can adapt to the changes in waste in different regions and time, and improve the flexibility and sustainability of recycling. Through the recycling and processing log, the processing efficiency and status of each recycling station can be understood, which helps to optimize resource allocation. The identification and positioning of recycling station nodes in the city helps to accurately grasp the distribution of garbage treatment facilities. The distribution map enables managers to better layout and schedule garbage recycling stations, improve recycling efficiency and service coverage. Real-time monitoring data provides accurate positioning of the source of waste, making transportation decisions more scientific and efficient. Dynamic transportation decisions can be flexibly adjusted according to factors such as the quantity and type of waste and the capacity of the recycling station. The generated transportation strategy can effectively avoid empty and overload phenomena during transportation, reduce transportation costs, and optimize resource use. Through the real-time database, intelligent decisions can be made according to the type of waste and recycling efficiency to maximize the recycling value. Intelligent recycling optimization can formulate the most appropriate treatment plan according to different types of waste, improve treatment efficiency and recycling quality. The optimization strategy not only improves the recycling rate, but also reduces unnecessary costs and resource waste, and promotes sustainable development. The integration of recycling feedback information enables the system to continuously learn and optimize existing strategies and improve the adaptability of full life cycle management. Collaborative learning ensures that the strategies of recycling, sorting and transportation cooperate with each other, avoiding conflicts or inefficiencies in the process. The full-cycle monitoring optimization model can globally optimize the entire solid waste management process, ensure the optimal allocation of resources in each link, and improve the operating efficiency and environmental friendliness of the entire system.

[0008] In this specification, a full life cycle management system for solid waste is provided, which is used to implement the full life cycle management method for solid waste as described above, including: A distributed storage module is used to collect waste source monitoring data based on sensors, perform distributed storage management based on the waste source monitoring data, and build a solid waste real-time management database; Solid waste sorting module, used to predict waste recycling types one by one according to the solid waste real-time management database, and perform dynamic sorting control processing, so as to generate dynamic solid waste sorting strategy; The distribution positioning module is used to obtain the solid waste recycling log; identify the nodes of the garbage disposal and recycling stations in the city based on the solid waste recycling log, perform distribution positioning fitting, and build a distribution map of recycling stations in the city; Solid waste transportation module, which is used to identify the location information of each waste source based on the waste source monitoring data, and make dynamic solid waste transportation decisions based on the distribution map of recycling stations in the city to generate dynamic solid waste transportation strategies; The recycling optimization module is used to make intelligent recycling processing decisions and intelligent recycling optimization based on the real-time solid waste management database to generate intelligent recycling optimization strategies; The full-cycle monitoring module is used to conduct intelligent collaborative learning and global monitoring optimization of intelligent recycling optimization strategies, dynamic solid waste transportation strategies and dynamic solid waste sorting strategies based on recycling feedback information, and to build a full-cycle monitoring optimization model.

[0009] The present invention provides basic data support for subsequent recycling, sorting and other links by real-time monitoring of the generation and change trend of waste. Distributed storage can support the storage of massive data and provide efficient data access to ensure the reliability and real-time nature of data. Centralized management of waste data provides a basis for decision support, optimization strategy generation and system automation, and improves overall processing capabilities. Through accurate prediction of recycling types, automatic classification of waste is achieved, and the recycling rate and resource recovery efficiency are improved. Dynamic sorting can respond to changes in the source and type of waste in real time, and flexibly respond to the processing requirements of different wastes. The automated sorting system reduces manual operations, reduces human errors and work intensity, and improves work efficiency. Based on the spatial distribution optimization map of the recycling station, the location of the recycling station can be reasonably planned, the coverage rate can be improved, and the transportation distance can be reduced. The distribution map can reflect the load of the recycling station in real time, and help the scheduling system to perform intelligent matching and resource allocation. The location and operating status of the recycling station are clear at a glance, providing data support for subsequent recycling decisions and strategy formulation. Accurate waste source positioning and dynamic path adjustment can effectively reduce the empty load rate and transportation time during waste transportation. Optimized transportation routes reduce unnecessary driving distances, thereby reducing transportation costs and energy consumption. The dynamic transportation decision-making system can adjust transportation plans according to real-time traffic conditions to avoid transportation delays caused by emergencies. Intelligent decision-making can develop personalized recycling and treatment plans for different types of waste to ensure that each type of waste is treated most effectively. Real-time monitoring and optimization can reduce resource waste and improve the overall efficiency of the recycling process. By optimizing the recycling and treatment strategy, the continuous improvement of the entire recycling process is ensured, and the self-optimization ability of the system is enhanced. Through intelligent collaborative learning, the collaboration of each link is optimized to improve the efficiency and responsiveness of the entire system. The system can dynamically adjust and optimize according to real-time data to improve the overall effect of the waste treatment process. The full-cycle monitoring model continuously feeds back optimization suggestions, so that the system gradually has self-learning and self-adaptive capabilities, forming a long-term management mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of the steps of a full life cycle management method for solid waste according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0012] The present application example provides a full life cycle management method and system for solid waste. The execution subjects of the full life cycle management method and system for solid waste include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0013] See also Figures 1 to 4 The present invention provides a full life cycle management method for solid waste, and the full life cycle management method for solid waste comprises the following steps: Step S1: collecting waste source monitoring data based on sensors, performing distributed storage management based on the waste source monitoring data, and building a solid waste real-time management database; Step S2: predicting waste recycling types one by one according to the solid waste real-time management database, and performing dynamic sorting control processing, thereby generating a dynamic solid waste sorting strategy; Step S3: obtaining a solid waste recycling log; identifying the nodes of garbage disposal and recycling stations in the city based on the solid waste recycling log, and performing distribution positioning fitting to construct a distribution map of recycling stations in the city; Step S4: Identify the location information of each waste source based on the waste source monitoring data, and make dynamic solid waste transportation decisions based on the distribution map of recycling stations in the city to generate a dynamic solid waste transportation strategy; Step S5: making intelligent recycling processing decisions and intelligent recycling optimization according to the real-time solid waste management database to generate an intelligent recycling optimization strategy; Step S6: Based on the recycling feedback information, intelligent collaborative learning and global monitoring optimization are performed on the intelligent recycling optimization strategy, dynamic solid waste transportation strategy and dynamic solid waste sorting strategy to build a full-cycle monitoring optimization model.

[0014] The present invention uses sensors to monitor the data of the source of waste generation in real time, and can accurately understand the amount and type of waste at various locations and times. Distributed storage management improves the security and reliability of data and avoids the risk of single point failure. The establishment of a real-time database makes the collection, query and analysis of waste information more efficient, which helps to provide accurate data support for subsequent management and decision-making. The data in the real-time database can be used to accurately predict the type of waste recycling to avoid human sorting errors. By adopting dynamic sorting control technology, the sorting strategy can be adjusted in real time according to the type and quantity of waste to improve sorting efficiency and accuracy. The dynamic sorting strategy can adapt to the changes in waste in different regions and time, and improve the flexibility and sustainability of recycling. Through the recycling and processing log, the processing efficiency and status of each recycling station can be understood, which helps to optimize resource allocation. The identification and positioning of recycling station nodes in the city helps to accurately grasp the distribution of garbage treatment facilities. The distribution map enables managers to better layout and schedule garbage recycling stations, improve recycling efficiency and service coverage. Real-time monitoring data provides accurate positioning of the source of waste, making transportation decisions more scientific and efficient. Dynamic transportation decisions can be flexibly adjusted according to factors such as the quantity and type of waste and the capacity of the recycling station. The generated transportation strategy can effectively avoid empty and overload phenomena during transportation, reduce transportation costs, and optimize resource use. Through the real-time database, intelligent decisions can be made according to the type of waste and recycling efficiency to maximize the recycling value. Intelligent recycling optimization can formulate the most appropriate treatment plan according to different types of waste, improve treatment efficiency and recycling quality. The optimization strategy not only improves the recycling rate, but also reduces unnecessary costs and resource waste, and promotes sustainable development. The integration of recycling feedback information enables the system to continuously learn and optimize existing strategies and improve the adaptability of full life cycle management. Collaborative learning ensures that the strategies of recycling, sorting and transportation cooperate with each other, avoiding conflicts or inefficiencies in the process. The full-cycle monitoring optimization model can globally optimize the entire solid waste management process, ensure the optimal allocation of resources in each link, and improve the operating efficiency and environmental friendliness of the entire system.

[0015] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a full life cycle management method for solid waste of the present invention. In this example, the steps of the full life cycle management method for solid waste include: Step S1: collecting waste source monitoring data based on sensors, performing distributed storage management based on the waste source monitoring data, and building a solid waste real-time management database; In this embodiment, suitable sensors are selected according to actual needs, such as weighing sensors for monitoring the weight of waste, gas sensors for monitoring the release of harmful gases, and image sensors for identifying the types of waste. The selection of each sensor should be based on its accuracy, response time, and environmental adaptability. Set the data collection frequency of the sensor, such as once a minute, hour, or day. The frequency should take into account the actual fluctuations in waste generation to ensure the representativeness of the data. During the data collection process, the potential impact of environmental factors (such as temperature, humidity, air pressure, etc.) on waste generation should also be considered to ensure the comprehensiveness of the data. Remove outliers and missing values. For example, use statistical methods to identify abnormal data that exceeds the normal range and process them. Missing values ​​can be filled by mean interpolation or forward filling. Convert the collected data into a unified format to ensure that each data record has a clear timestamp, source ID, and related attributes (such as weight, composition, etc.). This not only helps with subsequent analysis, but also facilitates data storage and retrieval. Classify and manage waste. For example, waste can be divided into recyclables, hazardous materials, kitchen waste, etc., and data of different categories should be stored in different database tables for subsequent management and query. Choose a suitable database system according to your needs, such as a NoSQL database (such as MongoDB) or a distributed SQL database (such as CockroachDB). NoSQL databases are suitable for processing unstructured data, while distributed SQL databases have advantages in transaction processing and data consistency. Set up multiple data nodes in different geographical locations to ensure fast access and redundant backup of data. Each node should have a certain storage capacity and computing power to support the access of real-time data. Implement a data sharding strategy to divide a large amount of data into small pieces and distribute them to different nodes to improve storage efficiency. At the same time, adopt a data replication mechanism to ensure redundant storage of data between nodes and improve the fault tolerance of the system. Use a stream processing framework (such as Apache Kafka or Apache Flink) to process real-time data. Real-time processing allows the system to analyze data as it is generated, detect abnormal situations in time and take measures. Use data visualization tools (such as Tableau or Grafana) to visualize real-time monitoring data. Visual dashboards can intuitively reflect the generation trend, classification and other important indicators of waste, which is convenient for managers to make decisions. A real-time alarm mechanism is set up. When abnormal data is monitored (such as waste generation exceeding the set threshold), the system can issue an alarm in time to help managers respond quickly.

[0016] Step S2: predicting waste recycling types one by one according to the solid waste real-time management database, and performing dynamic sorting control processing, thereby generating a dynamic solid waste sorting strategy; In this embodiment, the extracted data is cleaned to remove duplicate and incomplete data records. Statistical methods (such as mean and standard deviation) are used to identify and process outliers to ensure data quality. Features that affect the type of waste recycling, such as physical properties (weight, volume), chemical composition (content of harmful substances), source location (residential area, commercial area), etc. of the waste are extracted. Correlation analysis is used to evaluate the relationship between features and recycling types to determine which features are most important for prediction. Waste inflow data is collected in real time through sensors and monitoring systems, including the type, quantity and time of inflowing waste. The monitoring frequency is set, such as recording once every minute, to ensure the real-time nature of the data. The sorting strategy is dynamically adjusted based on the real-time monitoring data and the output of the prediction model. For example, if the inflow of a certain type of waste is predicted to increase, the operation mode of the sorting equipment is adjusted to increase the sorting priority of this type of waste. The sorting efficiency is analyzed based on the real-time monitoring and prediction data, the bottleneck link is identified, and the sorting strategy is adjusted. For example, if the processing time of a certain link is too long, consider increasing manpower or improving equipment to improve efficiency. The generated dynamic sorting strategy is recorded in the database to ensure the traceability of information. Generate a strategy implementation report that details the strategy's background, objectives, and implementation plan to facilitate subsequent evaluation and adjustment.

[0017] Step S3: obtaining a solid waste recycling log; identifying the nodes of garbage disposal and recycling stations in the city based on the solid waste recycling log, and performing distribution positioning fitting to construct a distribution map of recycling stations in the city; In this embodiment, recycling logs are obtained from various garbage disposal recycling stations and management departments. These data can be records in a spreadsheet or database. Ensure the integrity of the information, including recycling date, recycling station ID, processing capacity, recycling volume, etc. Perform data cleaning on the collected logs to remove duplicate and incomplete records. Statistical analysis methods (such as mean and standard deviation) are used to identify and eliminate outliers to ensure the quality of the data. The cleaned data is organized into a structured format, including key fields such as recycling station ID, waste type, recycling quantity and timestamp for subsequent analysis and processing. According to the functions and service scope of the recycling station, node identification standards are formulated, including the type of recycling station (such as fixed station, mobile station), processing capacity and service radius, etc., in order to perform effective node identification. Geographic Information System (GIS) technology is used to extract the information of recycling station nodes from the recycling log. By analyzing the historical data of the recycling station, the spatial location of each recycling station and its service scope are identified. All identified garbage disposal recycling station nodes are marked in the GIS system to ensure that each node has a unique identifier and record its relevant information, such as service radius and processing capacity, to provide a basis for subsequent analysis. Import the identified recycling station nodes and their related attributes (such as service radius and processing capacity) into the GIS system in preparation for spatial analysis. Consider using actual geographic data (such as city maps and road networks) as a background to improve the accuracy of the analysis. Select appropriate spatial analysis methods, such as Kriging interpolation or heat map analysis, to fit the distribution and location of recycling stations. These methods can generate smoother and continuous spatial distribution maps based on the existing recycling station location data. Run the fitting model to generate a distribution map of recycling stations to show the spatial layout of recycling stations and their service coverage. Use GIS tools to generate visualization charts so that decision makers can intuitively understand the distribution of recycling stations. Organize the distribution and location fitting results of recycling stations into a report, which describes the spatial distribution characteristics of each recycling station in detail, including concentrated areas, service coverage, etc. Generate maps and charts to facilitate decision makers to understand the distribution characteristics. Evaluate the rationality of the generated recycling station distribution map and identify possible underserved areas or improper resource allocation. Based on the distribution situation, make optimization suggestions to improve the efficiency of solid waste management in the city.

[0018] Step S4: Identify the location information of each waste source based on the waste source monitoring data, and make dynamic solid waste transportation decisions based on the distribution map of recycling stations in the city to generate a dynamic solid waste transportation strategy; In this embodiment, source data is obtained from each waste monitoring system, which may come from sensors, monitoring equipment or management systems. Ensure the frequency and scope of data collection so as to fully cover all sources of waste generation. Clean the collected data and delete duplicate records and outliers. Use statistical analysis methods, such as box plots and Z-scores, to identify and process outliers to ensure data quality. Organize the cleaned data into a structured format, with key fields including source ID, location coordinates, waste type, generation volume and timestamp. Ensure that the data can support subsequent positioning analysis and decision making. According to the characteristics of the waste source, set positioning criteria, including the type of source (such as residential areas, commercial areas, industrial areas) and geographical location (urban areas, towns, etc.). Ensure that the identification criteria are targeted and practical. Use geographic information system (GIS) technology to identify the spatial location of each waste source based on the location information of the waste source monitoring data. Use spatial clustering analysis methods (such as K-means or DBSCAN) to classify sources with similar characteristics and identify the main waste generation areas. Organize the location information of the identified waste sources into a data set to ensure that each source has a unique identifier and its corresponding spatial coordinates. Verify the accuracy of the location information through on-site verification or comparison with other data sources. Integrate the location information of the waste sources with the distribution map of recycling stations in the city to ensure that the distance and transportation route from each source to the recycling station can be clearly identified. Analyze the service radius and capacity of the recycling stations to identify the most suitable recycling stations. Build a dynamic transportation decision model and set decision parameters such as transportation efficiency, cost and time. Select a suitable algorithm (such as the shortest path algorithm or multi-objective optimization) to ensure that the optimal transportation plan can be found under multiple constraints. Dynamically adjust transportation decisions based on real-time data (such as traffic conditions and weather changes). If a recycling station is congested or temporarily closed, the system can automatically re-plan the transportation route to ensure maximum transportation efficiency. According to the output of the transportation decision model, formulate specific solid waste transportation strategies, including transportation routes, vehicle scheduling and personnel arrangements. Ensure that the strategy can flexibly respond to fluctuations and emergencies in the amount of waste generated. Record the generated dynamic transportation strategy in the database to ensure the traceability of information. Establish a feedback mechanism to monitor the performance of the transportation process in real time so as to adjust the strategy in time. Collect key data during the transportation process (such as transportation time, cost, and recycling efficiency) to provide a basis for subsequent optimization. Regularly evaluate the implementation effect of dynamic transportation strategies, analyze transportation efficiency and recycling effects, and identify possible improvement points. Based on the evaluation results, continuously optimize the transportation strategy to improve the overall efficiency of solid waste management.

[0019] Step S5: making intelligent recycling processing decisions and intelligent recycling optimization according to the real-time solid waste management database to generate an intelligent recycling optimization strategy; In this embodiment, historical recycling data, waste types, recycling volume, processing capacity, facility conditions, transportation costs and time of each recycling station are obtained. Ensure the comprehensiveness of the data, covering different time periods and multiple types of waste. Clean the collected data to remove duplicate records and outliers. Use statistical analysis techniques, such as standard deviation method and IQR method, to identify and process abnormal data to improve the quality and credibility of the data. Convert the cleaned data into a structured format to ensure the existence of key fields, such as recycling station ID, waste type, processing records and resource usage, for subsequent analysis. Select appropriate decision support tools, such as multi-objective optimization models, decision trees or artificial intelligence algorithms (such as machine learning models) to achieve optimal decisions for recycling and processing. Ensure that the model can handle a variety of complex factors, such as the type, quantity and treatment method of waste. Set the input parameters of the decision model, including waste type, quantity, recycling station processing capacity, cost, time and environmental impact. Ensure the accuracy and timeliness of the input parameters to improve the effectiveness of the decision. If a machine learning algorithm is used, the model needs to be trained with historical data to verify the accuracy and reliability of the model. Evaluate the generalization ability of the model through methods such as cross-validation to ensure its effectiveness in practical applications. Run the decision model to generate specific treatment plans for each recycling station. These plans should include information such as treatment methods, estimated treatment time, resource allocation and cost estimation to ensure the operability of the plans. Evaluate the generated treatment plans and use key performance indicators (KPIs) such as recovery rate, treatment cost and treatment time to ensure that the plans can achieve the expected results. Collect feedback information on each plan and analyze its advantages and disadvantages for subsequent optimization. Record the generated treatment plans and their evaluation results in the database to ensure the traceability of information. Establish a feedback mechanism to monitor the performance of the treatment process in real time so that the plan can be adjusted in time. Set optimization goals based on the effect of recycling treatment, such as improving recycling efficiency, reducing treatment costs and improving resource utilization. Ensure that the optimization goals are quantifiable and achievable. Build an intelligent recycling optimization model based on recycling efficiency and cost data. Select a suitable optimization algorithm, such as genetic algorithm or linear programming, to perform multi-objective optimization to balance various indicators. Run the optimization model to generate intelligent recycling optimization strategies for each recycling station. These strategies should include optimized resource allocation, adjusted processing procedures and other improvement measures to ensure that overall recycling efficiency can be improved. Evaluate the effectiveness of the implemented optimization strategies and analyze their impact on recycling efficiency and costs. Based on the evaluation results, identify potential improvement points and develop a continuous optimization plan to improve the overall level of solid waste management.

[0020] Step S6: Based on the recycling feedback information, intelligent collaborative learning and global monitoring optimization are performed on the intelligent recycling optimization strategy, dynamic solid waste transportation strategy and dynamic solid waste sorting strategy to build a full-cycle monitoring optimization model.

[0021] In this embodiment, feedback data is collected from each recycling station, transport vehicle and sorting center, including recycling efficiency, transportation time, sorting accuracy, resource usage and customer satisfaction. This information can be obtained through real-time monitoring systems, manual records and management software. The collected feedback information is cleaned to remove invalid data and outliers. Statistical analysis techniques (such as box plots and Z-score methods) are used to identify and process abnormal data to ensure data quality. The cleaned data is organized into a structured format to ensure the existence of key fields, such as strategy type, implementation time, feedback indicators (such as efficiency, cost) and related notes, for subsequent analysis and modeling. Select a suitable machine learning algorithm, such as ensemble learning (such as random forest, gradient boosting tree) or deep learning (such as neural network) to build an intelligent collaborative learning model. Ensure that the model can handle multiple input features and adapt to changes in feedback information. Set the input parameters of the model, including key indicators of recycling strategy, transportation strategy and sorting strategy (such as efficiency, cost, time), as well as feedback information. Ensure the accuracy and timeliness of the input data to improve the predictive ability of the model. Use historical feedback information to train the collaborative learning model. Evaluate the accuracy and generalization ability of the model through methods such as cross-validation to ensure its effectiveness in practical applications. Record parameter adjustments and model performance during training for subsequent optimization. Determine key indicators of the global monitoring model, such as recycling efficiency, transportation cost, sorting accuracy, and customer satisfaction. These indicators should be able to reflect the overall operating status of the system and provide a basis for optimization. Integrate the intelligent collaborative learning model with the global monitoring model, and use multi-objective optimization methods (such as genetic algorithms or particle swarm optimization) to comprehensively evaluate and optimize various strategies. Ensure that the optimization model can balance various indicators and improve overall benefits. Establish a real-time monitoring system to monitor the implementation effects of various strategies and collect feedback information in a timely manner. The system should have data visualization functions to facilitate decision makers to understand the operating status and problems in real time. Regularly evaluate the effectiveness of the implemented optimization strategies and analyze their impact on recycling efficiency, cost, and customer satisfaction. Based on the evaluation results, identify potential improvement points, adjust strategy parameters and implementation plans to achieve optimization goals. Establish a feedback loop mechanism to promptly integrate new feedback information into the collaborative learning model and the global monitoring optimization model. Ensure that the model can adapt to changes in the environment and adjustments to the strategy to maintain the sustainability of the optimization effect. Build a multi-party collaboration platform to promote information sharing and cooperation among recycling stations, transportation companies and sorting centers. Through regular meetings and data sharing, ensure that all parties can participate in the optimization process and achieve the best allocation of resources.

[0022] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Collecting waste source monitoring data based on sensors to obtain real-time monitoring images of waste sources; Step S12: performing histogram equalization on the real-time monitoring image of the waste source to obtain a brightness enhanced monitoring image; Step S13: performing visual recognition of waste morphology on the brightness enhanced monitoring image to extract waste morphology features; Step S14: classifying waste characteristics according to waste morphological characteristics, thereby obtaining a plurality of waste classification information; Step S15: Distributed storage management is performed based on the waste source monitoring data and multiple waste classification information to build a solid waste real-time management database.

[0023] In this embodiment, suitable sensors are selected, such as high-definition cameras, infrared sensors, and environmental monitoring sensors, to ensure that the environment and image data of the waste source can be monitored in real time. The sensor should have high resolution and the ability to work under low light conditions. The sensors are deployed at key locations of the waste source, such as garbage collection points, waste treatment plants, and dumping areas, to ensure that all potential waste generation sources are covered. The working parameters of the sensor are set, such as the acquisition frequency once per second, to obtain monitoring images in real time. The connection between the sensor and the data transmission system is configured to ensure that the real-time collected images and environmental data can be quickly transmitted to the central processing system. A wireless network or wired connection is used to ensure the stability and reliability of data transmission. Data recording and storage strategies are set to ensure that each collected image and related environmental parameters (such as temperature, humidity, odor concentration, etc.) can be effectively recorded. A periodic backup mechanism for data storage is set to prevent data loss. Through the data processing system, monitoring images of the waste source are generated in real time and associated with environmental monitoring data for subsequent analysis. Ensure that the timestamp of the monitoring image is consistent with the environmental data to facilitate subsequent data fusion and analysis. Record metadata for each monitoring, including sensor status, acquisition time, and environmental conditions, for subsequent quality control and data analysis. Preprocess the acquired real-time monitoring images, the main purpose of which is to improve the contrast and clarity of the images for subsequent visual recognition analysis. First, perform image denoising, such as using a median filter to remove random noise in the image. Determine denoising parameters such as the size of the filter, usually selecting a 3x3 or 5x5 window to balance the denoising effect with image detail retention. Use histogram equalization technology to process the denoised monitoring images. This process enhances the contrast of the image by adjusting the brightness distribution of the image, thereby significantly improving the visualization of the waste. Set processing parameters, such as the selection of equalization algorithms (such as global equalization or adaptive equalization), and select the appropriate algorithm according to the specific scenario to achieve the best effect. Adaptive equalization (CLAHE) is often used to process images with local contrast differences. Generate a brightness-enhanced monitoring image processed by histogram equalization and compare it with the original monitoring image to ensure that the enhancement effect meets expectations. Record the histogram analysis results of the processed image to verify the improvement in brightness and contrast. Perform image quality assessment and use indicators such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) to evaluate the processing effect to ensure that the image quality meets the requirements of subsequent analysis. Determine the morphological features of waste that need to be extracted, such as shape, size, texture, and color. These features will play an important role in subsequent classification and analysis. Select appropriate image processing tools and algorithms, such as edge detection algorithms (Canny or Sobel) and contour extraction algorithms to extract morphological features from brightness-enhanced monitoring images. Apply morphological operations (such as dilation and erosion) to enhance the features of waste in the image to make it easier to identify.These operations can help remove small noise points and highlight the main object contours. Perform shape analysis and use contour detection algorithms to extract the edges of waste in the image and calculate their geometric features, such as area, perimeter, and shape moments. Organize the extracted waste morphological features into a data table, including the feature values ​​of each waste object (such as shape, size, position, etc.) to ensure the structure and traceability of the information. Evaluate the accuracy of the extracted features and use manually annotated samples for comparison to ensure the effectiveness and reliability of the extraction algorithm. Develop waste classification standards based on the extracted waste morphological features. These standards should cover different types of waste, such as plastics, metals, paper, and organic matter, as well as their morphological features. Determine classification parameters, such as thresholds for shape features, size ranges, and color features, for effective classification. Select a suitable classification algorithm, such as support vector machine (SVM), decision tree, or deep learning classifier (such as convolutional neural network), to classify waste based on the extracted morphological features. Set the algorithm's training parameters, such as learning rate, number of iterations, and regularization parameters, to ensure that the classification model can accurately identify different types of waste. Record the classification results of each waste sample and generate a classification report, which details the quantity and characteristics of each type of waste. Ensure the accuracy of the classification results and evaluate the model through methods such as cross-validation. Design the architecture of the real-time solid waste management database and determine the table structure of the database, including waste monitoring data tables, classification information tables, and resource management tables. Ensure that the database can efficiently store and retrieve information. Set database access rights and security policies to ensure data protection and privacy. Store data in the database in real time based on monitoring data and classification information. Connect the monitoring system to the database through API or data interface to ensure smooth data transmission. Set data storage strategies, such as hierarchical storage according to timestamps and categories, to facilitate subsequent data retrieval and analysis. Establish a real-time monitoring mechanism to ensure that data in the database can be updated in a timely manner. Set the frequency of data updates, such as updating immediately every hour or every time a collection is collected. Regularly maintain and back up the database to ensure data security and integrity. At the same time, establish a data quality control process to regularly check the accuracy of data in the database.

[0024] In this embodiment, the specific steps of step S15 are: Calculate the waste time series generation amount based on the waste source monitoring data to obtain the waste time series generation amount; Calculate the generation frequency of waste source monitoring data and extract the waste generation frequency; According to the waste generation frequency, discrete interpolation fitting is performed on the waste time series generation to construct a real-time waste generation curve; Analyze the composition of multiple wastes based on waste source monitoring data; Based on the real-time waste generation curve, the components of multiple wastes and multiple waste classification information, distributed storage management is carried out to build a real-time solid waste management database.

[0025] In this embodiment, waste source monitoring data is collected, including the weight, volume and type of waste at each time point. Ensure the integrity of the data and record the potential impact of environmental conditions (such as temperature and humidity) on waste generation. Preprocess the collected data, including removing outliers and missing data. For example, use the mean filling method to handle missing values, and identify and remove outliers through the standard deviation method to improve data quality. Arrange the monitoring data in time series and calculate the amount of waste generated in each time period. Set a time window (for example, every hour or every day) for calculation so that the generation can reflect the actual situation. Calculate the generation by the formula: Generation = Waste at the current time point - Waste at the previous time point. Record the generation in each time period and generate the corresponding time series data. Arrange the calculated waste time series generation into a report, including the generation and timestamp of each time period. Generate a time series chart to intuitively display the trend of waste generation and help subsequent analysis. Perform data quality assessment to ensure the accuracy of the calculation results, and use standard deviation and root mean square error (RMSE) to evaluate the stability and reliability of the generation. Determine the definition of waste generation frequency, such as the amount of waste generated per unit time. Set the calculation period (such as hourly or daily) for frequency analysis. Use the formula to calculate the generation frequency: Generation frequency = Time series generation / Time window (such as hourly generation). Record the frequency value for each time period to generate a frequency data set. Perform statistical analysis on the generation frequency data, including mean, variance, and extreme values, to evaluate the regularity and volatility of waste generation. Set a confidence interval (such as 95%) and calculate the confidence interval of the frequency to evaluate the stability of the generation frequency. Generate a frequency distribution graph to intuitively display the changes in waste generation frequency in different time periods to help identify peaks and troughs. Select a suitable interpolation algorithm, such as linear interpolation, spline interpolation, or Lagrange interpolation, and fit it based on the generation frequency data and the time series generation data. Select a suitable interpolation method based on the distribution characteristics of the data to ensure the fitting effect. Determine the interpolation parameters, such as the number and range of interpolation points, to ensure the smoothness and accuracy of the interpolation results. Perform discrete interpolation calculations on the time series generation data to generate a real-time waste generation curve. Use the selected interpolation algorithm to convert discrete generation data into a continuous curve to reflect real-time generation trends. Record key parameters in the interpolation process, such as interpolation error and goodness of fit, to ensure that the generated curve is consistent with the actual situation. Use mean square error (MSE) to evaluate the fitting effect to ensure the reliability of the results. Collect composition data at the source of waste, including the chemical composition, physical properties and biodegradability of different types of waste. Ensure that the data is representative and covers a variety of waste types. Set sampling standards and frequencies, such as conducting composition analysis once a week to ensure the timeliness and accuracy of the data.Select appropriate analytical methods, such as gas chromatography-mass spectrometry (GC-MS), infrared spectroscopy or chemical analysis, to conduct quantitative and qualitative analysis of the components of the waste. Determine analytical parameters, such as sample processing methods, analysis time and detection limits, to ensure the accuracy and reliability of component analysis. Organize the analysis results into a report, describing in detail the components and proportions of each type of waste. Generate a component distribution map to intuitively display the composition characteristics of different types of waste. Separate tables and waste classification information tables, etc. Ensure the scalability and efficiency of the database. Set data access rights and security controls to ensure data security and reliability. Store real-time waste generation curves, component information and classification data in the database, and use data interfaces to achieve automated data upload. Ensure the integrity and consistency of data storage. Set data storage strategies, such as hierarchical storage by time and category, to facilitate subsequent data retrieval and analysis. Establish a real-time data monitoring mechanism to ensure that the data in the database can be updated in a timely manner, and set the update frequency (such as once an hour) to ensure the timeliness of the data. Regularly maintain and back up the database to ensure data security and integrity. At the same time, establish a data quality control process to regularly check the accuracy of data in the database.

[0026] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: predicting waste recycling types one by one according to the solid waste real-time management database to generate solid waste recycling prediction types; Step S22: dynamically adaptively process the solid waste recycling prediction type, thereby obtaining adaptive recycling data for each type of solid waste; Step S23: performing source sorting path identification based on the solid waste real-time management database to obtain multiple source sorting paths; Step S24: Perform dynamic sorting control processing according to multiple source sorting paths and the recycling adaptive data of each type of solid waste, so as to generate a dynamic solid waste sorting strategy.

[0027] In this embodiment, relevant data, including the amount of waste generated, composition, classification information, etc., are extracted from the real-time solid waste management database. Ensure that the selected data can reflect the characteristics of the waste, such as chemical composition, physical form, and historical recycling data. Set feature selection criteria and use correlation analysis (such as Pearson correlation coefficient) to evaluate the relationship between each feature and the recycling type to determine which features are most important for recycling prediction. Select a suitable machine learning algorithm to build a waste recycling prediction model, such as random forest, support vector machine (SVM) or neural network. Select a suitable model according to the characteristics and complexity of the data to achieve the best prediction effect. Set the training parameters of the model, such as learning rate, number of iterations, and regularization parameters. Use cross-validation methods to ensure the generalization ability of the model on different data sets. Use existing historical data to train the prediction model and record the corresponding relationship between the characteristics of each type of waste and the recycling type. Use an 80 / 20 ratio to split the data set to ensure that the model can learn the rules and can be effectively tested. Evaluate the model and use indicators such as accuracy, recall, and F1-score to evaluate the prediction effect. Ensure that the model can efficiently identify the recycling type of waste and record the test results for subsequent analysis. The prediction results are compiled into a report, listing the predicted recycling type for each type of waste, and compared with the actual recycling situation, analyzing the accuracy and deviation of the model prediction. Generate a visual chart of the prediction results to facilitate decision makers to quickly understand the recycling potential of different types of waste and provide a basis for subsequent recycling strategies. Design a dynamic adaptive processing framework that can automatically adjust the waste recycling strategy based on real-time monitoring data and prediction results. The framework should be flexible to cope with changes in different types of waste. Set adaptive parameters, such as adaptive thresholds and periodic update frequencies, to ensure that the processing results can quickly reflect changes in the field situation. Integrate real-time monitoring data (such as waste generation, composition, and environmental conditions) with predicted recycling types and analyze their impact on recycling results. Record the generation and recycling needs of each type of waste. Use time series analysis methods, such as sliding window technology, to dynamically monitor the changing trends of waste generation and adjust the recycling strategy in real time. Generate adaptive recycling data for each solid waste based on real-time data and prediction results, and record them in the database for subsequent calls. Ensure that the adaptive data can reflect the current recycling needs and resource allocation. Select a suitable path identification algorithm, such as the Dijkstra algorithm or the A search algorithm, to identify the source sorting path. The algorithm should be able to consider the shortest distance and time cost of the path to optimize the sorting efficiency. Set the parameters of path identification, such as the starting point, end point, nodes passed through, and the weight of each path to ensure that the algorithm can accurately calculate the best path. Extract information such as source location, sorting point, transportation route, etc. from the solid waste real-time management database to build a spatial model for source sorting. Ensure the accuracy of the data for effective path calculation. Run the path identification algorithm to identify the optimal path from multiple sources to the designated sorting point.Record the length, estimated time and potential obstacles of each path for subsequent analysis. Visualize the identified source sorting paths and generate maps or charts to intuitively display the sorting paths of different sources. Ensure that the visualization results have clear labels and instructions for decision makers to understand. Organize the results of path identification and generate reports that list in detail the characteristics, advantages and disadvantages and applicable scenarios of each sorting path to provide a basis for the subsequent sorting strategy formulation. Design a dynamic sorting control framework that can adjust the sorting strategy in real time based on the source sorting path and recycling adaptive data. The framework should be flexible to cope with changes and recycling needs of different types of waste. Set control parameters such as sorting priority, sorting speed and resource allocation to ensure the efficient implementation of the sorting strategy. Establish a real-time monitoring system to collect data generated during the sorting process, such as sorting efficiency, classification accuracy and resource usage. Ensure that the system can obtain feedback information in a timely manner to adjust the sorting strategy. Set a feedback cycle, such as updating feedback information every 30 minutes, so as to monitor the sorting effect in real time and adjust the strategy. Generate dynamic solid waste sorting strategies based on source sorting paths and recycling adaptive data, and record them in the database for subsequent application. Ensure that the strategies can reflect current sorting needs and resource allocation in real time. Regularly evaluate the effectiveness of sorting strategies, use data analysis tools to analyze sorting efficiency and accuracy, and ensure the effectiveness and adaptability of strategies. Based on the evaluation results, continuously optimize sorting strategies to improve overall recycling efficiency.

[0028] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: Obtaining solid waste recycling and processing log; Step S32: Identify the garbage disposal and recycling station nodes in the city on the solid waste recycling log, and mark all the garbage disposal and recycling station nodes; Step S33: performing service radius analysis on all garbage disposal recycling station nodes to obtain the recycling service radius of each recycling station; Step S34: spatially locating all garbage disposal recycling station nodes one by one, thereby generating the spatial position coordinates of each recycling station; Step S35: performing a recycling bin spatial distribution analysis according to the spatial position coordinates of each recycling bin to generate recycling bin spatial distribution data; Step S36: Perform distribution positioning fitting on the spatial distribution data of recycling stations according to the recycling processing service radius of each recycling station, and construct a distribution map of recycling stations in the city.

[0029] In this embodiment, solid waste recycling logs are collected from various garbage disposal recycling stations. These logs include information such as collection date, recycling quantity, processing method, transportation route, etc. Ensure the integrity and accuracy of the data. Set the frequency of data collection, such as daily or weekly collection, to ensure the timeliness of log information. Use a spreadsheet or database management system to organize data to ensure the systematization of information. Clean the collected logs to remove duplicate records and outliers. Apply data validation rules, such as ensuring that the recycling quantity is not negative and the processing method is a preset valid option. Convert the cleaned data into a structured format for subsequent analysis. Set data fields such as collection date, recycling quantity, processing method, and recycling station number for easy classification and retrieval. Determine the identification criteria for garbage disposal recycling station nodes, including the definition of the recycling station, location type (such as fixed station, mobile station) and its function (such as classification, processing, reuse, etc.). Set important parameters such as the service scope, processing capacity and facility type of the recycling station for effective node identification. Use geographic information system (GIS) technology to extract recycling station node information from the recycling processing log. Organize the name, location, processing capacity and other information of the recycling station into a data set. Use spatial analysis tools (such as ArcGIS) to visualize the location of recycling stations and ensure that each recycling station node can be accurately identified and marked. Mark all identified waste disposal recycling station nodes in the GIS system to ensure that each node has a unique identifier. Record relevant information about the node, including service range, processing capacity, etc., for subsequent analysis. Generate a node identification report that describes the functions and characteristics of each recycling station in detail to provide basic data for subsequent service radius analysis and spatial positioning. Determine the definition of service radius, such as the maximum effective service distance that a recycling station can cover. Set different service radii according to the type and function of the recycling station. For example, fixed stations may have a larger service radius. Set service radius calculation parameters, such as traffic conditions, population density, and waste generation, which are factors that affect service coverage. Use network analysis methods to calculate the service radius of each waste disposal recycling station. Use GIS tools to calculate the shortest path and time to reach each recycling station based on network road network data. Record the service radius of each recycling station and generate a visual map to show the coverage area of ​​the recycling station to ensure that the effectiveness of the service radius can be intuitively demonstrated. Organize the service radius calculation results into a report that lists the service radius and its coverage area of ​​each recycling station in detail. Generate a service radius map to help decision makers understand service coverage. Analyze the effectiveness of the service radius and identify underserved areas for subsequent resource allocation and site optimization. Determine the spatial location positioning standard and use the Global Positioning System (GPS) or Geographic Information System (GIS) to accurately locate each recycling station. Set the coordinate system (such as WGS 84) to ensure data consistency. Record the accuracy requirements of the positioning and set the allowable error range, for example, the GPS positioning error should be controlled within 5 meters.Use GPS equipment or GIS software to locate each garbage disposal recycling station on site and record its latitude and longitude coordinates. Ensure that positioning is performed under different weather and environmental conditions to improve data reliability. Record the spatial coordinates of each recycling station and associate them with the previously marked node information to ensure the integrity of the information. Organize the spatial location coordinates obtained by positioning into a data set to ensure that the coordinate information of each recycling station is combined with its relevant characteristics (such as processing capacity, service radius, etc.). Generate a spatial location positioning report, which describes the geographic coordinates and functional characteristics of each recycling station in detail, and provides basic data for subsequent spatial distribution analysis. Select appropriate spatial analysis methods, such as Kriging interpolation and hot spot analysis, to analyze the spatial distribution characteristics of recycling stations. Select appropriate analysis tools based on the distribution of data to obtain accurate spatial distribution information. Set analysis parameters, such as the area range of analysis, data sampling rate, and spatial resolution, to ensure the accuracy of the results. Use GIS tools to analyze the recorded spatial location coordinates of recycling stations and generate a spatial distribution map to show the spatial layout of each recycling station. Identify the concentrated and sparse areas of recycling stations. Record key data in the analysis process, such as the density, distance, and distribution pattern of recycling stations, for subsequent comprehensive analysis. The results of the spatial distribution analysis are organized into a report, which describes in detail the spatial distribution characteristics of the recycling stations, including the concentrated areas, service coverage, etc. Visual charts are generated to facilitate decision makers to understand the distribution characteristics. The rationality of the spatial distribution is analyzed, and possible optimization areas are identified to provide a basis for subsequent resource allocation and site planning. A suitable fitting model, such as polynomial regression or spatial autocorrelation analysis, is selected to model the spatial distribution data of the recycling stations. A suitable model is selected according to the service radius and distribution characteristics of the recycling stations to achieve the best fitting effect. Fitting parameters, such as regression order and fitting accuracy standard, are determined to ensure that the model can truly reflect the distribution of the recycling stations. The selected fitting model is used to analyze the spatial distribution data of the recycling stations and generate a distribution map of the recycling stations. The service radius and coverage of each recycling station are recorded to ensure that the map is intuitive and accurate. Combined with GIS tools, the fitting results are combined with the actual spatial data to generate the final distribution map of the recycling stations in the city to ensure that the map can reflect the actual situation. The generated distribution map of the recycling stations in the city is verified to ensure the accuracy and reliability of the map. The validity of the fitting results is evaluated by comparing with the location of the actual recycling stations.

[0030] In this embodiment, step S4 includes the following steps: Step S41: identifying each waste source location information according to the waste source monitoring data; Step S42: performing recycling station location matching on each waste source location information based on the recycling station distribution map in the city, thereby obtaining a matching recycling station for each source; Step S43: Perform dynamic path planning for each matching recycling station at the source to generate an optimal solid waste transportation path; Step S44: making dynamic solid waste transportation decisions based on the optimal solid waste transportation route to generate a dynamic solid waste transportation strategy.

[0031] In this embodiment, waste source monitoring data is collected, including information such as waste generation location, timestamp and quantity. Ensure the accuracy and completeness of the data for subsequent source location analysis. Set key fields such as source ID, location coordinates, waste type and generation amount to ensure that the data is structured and easy to analyze. Use geographic information system (GIS) technology to extract the spatial location information of each waste source from the monitoring data. According to the location coordinates in the monitoring data, identify the specific waste source location. Combined with other auxiliary data (such as city maps, road networks), ensure that the identified source location information is consistent with the actual geographical location to reduce positioning errors. The location information of each identified waste source is organized into a data set to ensure that each source has a unique identifier and its corresponding spatial coordinates. Perform data verification to ensure the accuracy of the source location information through on-site inspections or comparisons with other data sources. Record problems encountered during the verification process and solutions for subsequent improvements. Extract the spatial location coordinates and related attributes of the recycling station from the city's recycling station distribution map, such as service radius, processing capacity and recycling type. Ensure the integrity and accuracy of the recycling station data. Set recycling station matching criteria, such as service radius, waste type matching, etc., to make effective matching. Use spatial matching algorithms (such as K nearest neighbor matching or overlap analysis) to match the recycling station location information for each waste source. Match according to the distance between the source location and the recycling station and the service radius. Record the matching results of each source and its matching recycling station, including the matching recycling station ID, distance and service radius, etc., to ensure the systematization of information. Organize the matching results into a report, listing each waste source and its corresponding matching recycling station information in detail. Generate a visual chart to facilitate decision makers to quickly understand the matching relationship between the source and the recycling station. Analyze the effectiveness of the matching results and identify potential matching problems, such as the distance between the source and the recycling station is too far or the service coverage is insufficient, which provides a basis for subsequent path planning. Select a suitable path planning algorithm, such as Dijkstra algorithm, A search algorithm or genetic algorithm, to achieve dynamic planning of solid waste transportation routes. Select a suitable algorithm based on transportation needs and actual conditions. Set path planning parameters, such as starting point (waste source), end point (matching recycling station), road network and transportation restrictions (such as vehicle load, traffic conditions, etc.). Apply the selected routing algorithm to calculate the optimal transportation route from each waste source to the matching recycling station by analyzing the urban road network. Record the distance, estimated transportation time and traffic conditions of each route. Consider dynamic factors such as real-time traffic conditions and weather conditions to ensure that the routing results can reflect the actual transportation situation. Update the routing plan with real-time data to ensure the timeliness of the route. Visualize the calculated optimal solid waste transportation route and generate a route map to show the transportation route from each source to the recycling station. Ensure that the graphics are clear and easy for decision makers to understand.Determine the criteria for dynamic solid waste transportation decisions, such as transportation efficiency, cost, time, and environmental impact. Set priorities according to different transportation needs to make reasonable decisions. Set decision parameters, such as vehicle type, load capacity, and transportation time limit, to ensure the scientificity and rationality of the decision. Build a dynamic transportation decision model, and formulate corresponding transportation strategies based on the optimal transportation route and real-time data (such as traffic conditions and weather changes). Use multi-objective optimization methods to ensure that decisions can take into account various indicators. Set the input parameters of the model, including source information, recycling station information, transportation route information, and external environmental data to ensure the comprehensiveness of the decision model. Implement transportation decisions based on the generated dynamic solid waste transportation strategy to ensure that each transportation activity can be adjusted according to real-time conditions. Record key data during transportation, such as transportation time, cost, and recycling efficiency. Establish a feedback mechanism to collect feedback information during transportation so as to adjust the transportation strategy in a timely manner. Regularly evaluate the effectiveness of the transportation strategy, identify potential problems through data analysis, and optimize them.

[0032] In this embodiment, step S5 includes the following steps: Step S51: making intelligent recycling and processing decisions based on the real-time solid waste management database, and generating a waste recycling and processing plan for each recycling station; Step S52: Performing recycling based on the waste recycling scheme of each recycling station, and monitoring the recycling process in real time; Step S53: performing recycling processing logic analysis between the recycling processing links to generate recycling processing logic relationships; Step S54: Calculating the recycling efficiency of the recycling process to generate solid waste recycling efficiency; Step S55: Perform intelligent recycling optimization based on solid waste recycling efficiency and recycling processing logic to generate an intelligent recycling optimization strategy.

[0033] In this embodiment, relevant data is extracted from the real-time solid waste management database, including historical recycling data, waste types, recycling volume, processing capacity and facility status of each recycling station. Ensure the integrity and accuracy of the data for intelligent decision-making. Set key fields such as recycling station ID, waste type, historical processing record and recycling capacity to ensure that the data can support subsequent decision analysis. Based on the extracted data, build an intelligent recycling and processing decision model. Select a suitable decision support tool, such as a multi-objective optimization model or a decision tree, and set decision parameters such as recycling efficiency, cost and environmental impact. Set the input parameters of the model, including the type, quantity, treatment method and facility information of the recycling station of the waste, to achieve the generation of the best treatment plan. Run the decision model to generate a corresponding waste recycling and treatment plan for each recycling station. The plan should include information such as treatment method, expected processing time, resource allocation and cost estimation. Record the generated recycling and treatment plan in the database to ensure the traceability of information and the convenience of subsequent analysis. Generate a report to describe the treatment plan of each recycling station in detail so that managers can review and make decisions. According to the generated waste recycling and treatment plan, implement specific recycling and treatment work. Ensure that all treatment activities are carried out according to the expected process in the plan, including classification, transportation and treatment. Set implementation parameters, such as processing time, personnel allocation and equipment usage, to ensure the efficiency and smoothness of the entire recycling process. Establish a real-time monitoring system to monitor various indicators of the recycling process, such as the amount of waste entering the warehouse, processing speed, classification accuracy and resource usage. Use sensors and monitoring equipment to ensure the real-time and accuracy of data. Set the monitoring frequency, such as recording data every hour or after each treatment, so as to adjust the treatment plan in time. Record the real-time monitoring data in the database to ensure the systematization and traceability of information. The monitoring data should include the treatment effect and resource usage of each link for subsequent analysis and optimization. Establish a feedback mechanism to adjust the recycling treatment plan in time according to the real-time monitoring data to ensure that the treatment process can flexibly respond to emergencies. Determine the definition of the logical relationship between the recycling and treatment links, including the dependency, sequence and information flow between the links. Set the analysis criteria of the logical relationship, such as the input-output relationship and time dependency between the links. Record the key parameters of each recycling and treatment link, such as processing time, resource requirements and output results, to ensure the comprehensiveness of the analysis. Use system analysis tools (such as flow charts or cause-and-effect diagrams) to conduct a logical analysis of the recycling and processing links to identify the dependencies and information flow paths between links. Ensure that the analysis results can reflect the actual recycling and processing process. Record the logical relationship of each link, generate a recycling and processing logic relationship diagram between links, and intuitively display the interaction between each link. Determine the calculation criteria for solid waste recycling efficiency, including input (total amount of waste), output (mass of recyclables after treatment) and resource use (manpower, material resources, etc.).Set key indicators for efficiency calculation, such as recovery rate and processing cost. Record key data of each recycling and processing link for subsequent efficiency calculation and analysis. Calculate the efficiency of each recycling and processing link according to the set standards. Use the formula: Recovery efficiency = output / input × 100% to calculate the recovery efficiency of each link. Record the efficiency calculation results of each link to ensure that the information is hierarchical and organized for subsequent analysis. According to the recovery efficiency calculation results and the recycling and processing logical relationship, set the goals of recycling optimization, such as improving efficiency, reducing costs, and improving resource utilization. Ensure that the optimization goals are measurable. Record the key factors that affect the optimization goals, such as equipment efficiency, staffing, and processing procedures, for comprehensive analysis. Construct an intelligent recycling optimization model, combine the recycling efficiency and logical relationship, and apply a multi-objective optimization algorithm (such as genetic algorithm or linear programming) for optimization. Set the input parameters of the model, including the efficiency data, resource allocation, and processing capacity of the recycling and processing links, to achieve the best optimization effect. Run the optimization model to generate an intelligent recycling optimization strategy for each recycling station to ensure that the strategy can improve the overall recycling efficiency and reduce operating costs. The generated optimization strategy is recorded in the database to ensure the traceability of information and the convenience of subsequent implementation. A strategy implementation report is generated, detailing the goals, measures and expected results of the optimization strategy.

[0034] In this embodiment, step S6 includes the following steps: Step S61: Identify and recycle feedback information; Step S62: performing intelligent collaborative learning on the intelligent recycling optimization strategy, the dynamic solid waste transportation strategy and the dynamic solid waste sorting strategy according to the recycling feedback information to generate an intelligent collaborative control strategy; Step S63: Perform global monitoring optimization on the intelligent collaborative control strategy and build a full-cycle monitoring optimization model.

[0035] In this embodiment, feedback information from each recycling station and transportation link is collected, including recycling volume, transportation efficiency, sorting accuracy, and customer satisfaction. This information can be collected through a monitoring system, a questionnaire survey, or direct communication to ensure the comprehensiveness and timeliness of the data. Set key indicators for feedback information, such as the average daily recycling volume, transportation time, and customer ratings of each recycling station, for subsequent analysis and decision-making. Sort out the collected feedback information and classify it into different types of feedback, such as operational feedback, customer feedback, and environmental feedback. This process helps to clearly identify the sources of various feedback and their impacts. Use data analysis tools (such as Excel or a database management system) to sort out the feedback data to ensure that each piece of feedback information has a timestamp, source, and specific content for subsequent query and analysis. Conduct an in-depth analysis of the sorted feedback information to identify key issues and improvement opportunities. For example, analyze the reasons for low transportation efficiency or identify the trend of declining recycling volume, and use descriptive statistical methods (such as mean, variance) and visualization tools (such as charts) to display the feedback results. Record the findings and conclusions during the analysis process to provide data support for subsequent strategy optimization. At the same time, establish a monitoring mechanism for feedback information to ensure that new feedback information can be obtained and responded to in a timely manner. Design an intelligent collaborative learning mechanism to combine recycling feedback information with existing recycling optimization strategies, transportation strategies, and sorting strategies. Ensure that the collaborative learning mechanism can dynamically adjust the strategy to cope with real-time changing environments and feedback. Set learning parameters such as learning rate, number of iterations, and tolerance to maintain flexibility and adaptability in strategy optimization. Integrate recycling feedback information with historical strategy data to build a training data set. Ensure that the data set can cover the relationship between various types of feedback and strategies for effective learning and optimization. Select a suitable machine learning algorithm (such as reinforcement learning or support vector machine) to train the integrated data. The model should be able to identify the impact of feedback information on the strategy to generate a collaborative control strategy. Run the trained model to generate an intelligent collaborative control strategy. This strategy should be able to comprehensively consider multiple factors such as recycling efficiency, transportation cost, and sorting accuracy to ensure the overall optimization effect. Record the generated collaborative control strategy in the database to ensure the traceability of information. At the same time, formulate an implementation plan to promote the new strategy to various recycling stations and transportation links for practical application. Determine the key indicators of the global monitoring optimization model, including recycling efficiency, transportation cost, sorting accuracy, and customer satisfaction. Ensure that the monitoring indicators can fully reflect the operating status of the recycling system. Set the monitoring frequency, such as daily or weekly comprehensive monitoring, so as to find problems and make adjustments in time. Build a full-cycle monitoring optimization model to integrate recycling feedback information, collaborative control strategies, and various monitoring indicators. Select appropriate modeling tools, such as system dynamics models or multi-level optimization models, to ensure that the dynamic changes of the system can be fully reflected.Set the model's input parameters, including real-time monitoring data, historical feedback information, and collaborative control strategies, to achieve the goal of comprehensive monitoring and optimization. Verify the constructed full-cycle monitoring and optimization model to ensure that the model can effectively reflect the actual operation situation. Evaluate the accuracy and effectiveness of the model by backtesting historical data, and record the verification results for subsequent adjustments.

[0036] In this embodiment, a full life cycle management system for solid waste is provided, which is used to implement the full life cycle management method for solid waste as described above, including: A distributed storage module is used to collect waste source monitoring data based on sensors, perform distributed storage management based on the waste source monitoring data, and build a solid waste real-time management database; Solid waste sorting module, used to predict waste recycling types one by one according to the solid waste real-time management database, and perform dynamic sorting control processing, so as to generate dynamic solid waste sorting strategy; The distribution positioning module is used to obtain the solid waste recycling log; identify the nodes of the garbage disposal and recycling stations in the city based on the solid waste recycling log, perform distribution positioning fitting, and build a distribution map of recycling stations in the city; Solid waste transportation module, which is used to identify the location information of each waste source based on the waste source monitoring data, and make dynamic solid waste transportation decisions based on the distribution map of recycling stations in the city to generate dynamic solid waste transportation strategies; The recycling optimization module is used to make intelligent recycling processing decisions and intelligent recycling optimization based on the real-time solid waste management database to generate intelligent recycling optimization strategies; The full-cycle monitoring module is used to conduct intelligent collaborative learning and global monitoring optimization of intelligent recycling optimization strategies, dynamic solid waste transportation strategies and dynamic solid waste sorting strategies based on recycling feedback information, and to build a full-cycle monitoring optimization model.

[0037] The present invention provides basic data support for subsequent recycling, sorting and other links by real-time monitoring of the generation and change trend of waste. Distributed storage can support the storage of massive data and provide efficient data access to ensure the reliability and real-time nature of data. Centralized management of waste data provides a basis for decision support, optimization strategy generation and system automation, and improves overall processing capabilities. Through accurate prediction of recycling types, automatic classification of waste is achieved, and the recycling rate and resource recovery efficiency are improved. Dynamic sorting can respond to changes in the source and type of waste in real time, and flexibly respond to the processing requirements of different wastes. The automated sorting system reduces manual operations, reduces human errors and work intensity, and improves work efficiency. Based on the spatial distribution optimization map of the recycling station, the location of the recycling station can be reasonably planned, the coverage rate can be improved, and the transportation distance can be reduced. The distribution map can reflect the load of the recycling station in real time, and help the scheduling system to perform intelligent matching and resource allocation. The location and operating status of the recycling station are clear at a glance, providing data support for subsequent recycling decisions and strategy formulation. Accurate waste source positioning and dynamic path adjustment can effectively reduce the empty load rate and transportation time during waste transportation. Optimized transportation routes reduce unnecessary driving distances, thereby reducing transportation costs and energy consumption. The dynamic transportation decision-making system can adjust transportation plans according to real-time traffic conditions to avoid transportation delays caused by emergencies. Intelligent decision-making can develop personalized recycling and treatment plans for different types of waste to ensure that each type of waste is treated most effectively. Real-time monitoring and optimization can reduce resource waste and improve the overall efficiency of the recycling process. By optimizing the recycling and treatment strategy, the continuous improvement of the entire recycling process is ensured, and the self-optimization ability of the system is enhanced. Through intelligent collaborative learning, the collaboration of each link is optimized to improve the efficiency and responsiveness of the entire system. The system can dynamically adjust and optimize according to real-time data to improve the overall effect of the waste treatment process. The full-cycle monitoring model continuously feeds back optimization suggestions, so that the system gradually has self-learning and self-adaptive capabilities, forming a long-term management mechanism.

[0038] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0039] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for the full life cycle management of solid waste, characterized in that: The following steps are involved: Step S1: collecting waste source monitoring data based on sensors, performing distributed storage management based on the waste source monitoring data, and building a solid waste real-time management database; Step S2: predicting waste recycling types one by one according to the solid waste real-time management database, and performing dynamic sorting control processing, thereby generating a dynamic solid waste sorting strategy; Step S3: obtaining a solid waste recycling log; identifying the nodes of garbage disposal and recycling stations in the city based on the solid waste recycling log, and performing distribution positioning fitting to construct a distribution map of recycling stations in the city; Step S4: Identify the location information of each waste source based on the waste source monitoring data, and make dynamic solid waste transportation decisions based on the distribution map of recycling stations in the city to generate a dynamic solid waste transportation strategy; Step S5: making intelligent recycling processing decisions and intelligent recycling optimization according to the real-time solid waste management database to generate an intelligent recycling optimization strategy; Step S6: Based on the recycling feedback information, intelligent collaborative learning and global monitoring optimization are performed on the intelligent recycling optimization strategy, dynamic solid waste transportation strategy and dynamic solid waste sorting strategy to build a full-cycle monitoring optimization model.

2. The method for full life cycle management of solid waste according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: collecting waste source monitoring data based on sensors to obtain real-time monitoring images of waste sources; Step S12: performing histogram equalization on the real-time monitoring image of the waste source to obtain a brightness enhanced monitoring image; Step S13: performing visual recognition of waste morphology on the brightness enhanced monitoring image to extract waste morphology features; Step S14: classifying waste characteristics according to waste morphological characteristics, thereby obtaining a plurality of waste classification information; Step S15: Distributed storage management is performed based on the waste source monitoring data and multiple waste classification information to build a solid waste real-time management database.

3. The method for full life cycle management of solid waste according to claim 2, characterized in that: The specific steps of step S15 are: Calculate the waste time series generation amount based on the waste source monitoring data to obtain the waste time series generation amount; Calculate the generation frequency of waste source monitoring data and extract the waste generation frequency; According to the waste generation frequency, discrete interpolation fitting is performed on the waste time series generation to construct a real-time waste generation curve; Analyze the composition of multiple wastes based on waste source monitoring data; Based on the real-time waste generation curve, the components of multiple wastes and multiple waste classification information, distributed storage management is carried out to build a real-time solid waste management database.

4. The method for full life cycle management of solid waste according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: predicting waste recycling types one by one according to the solid waste real-time management database to generate solid waste recycling prediction types; Step S22: dynamically adaptively process the solid waste recycling prediction type, thereby obtaining adaptive recycling data for each type of solid waste; Step S23: performing source sorting path identification based on the solid waste real-time management database to obtain multiple source sorting paths; Step S24: Perform dynamic sorting control processing according to multiple source sorting paths and the recycling adaptive data of each type of solid waste, so as to generate a dynamic solid waste sorting strategy.

5. The method for full life cycle management of solid waste according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: Obtaining solid waste recycling and processing log; Step S32: Identify the garbage disposal and recycling station nodes in the city on the solid waste recycling log, and mark all the garbage disposal and recycling station nodes; Step S33: performing service radius analysis on all garbage disposal recycling station nodes to obtain the recycling service radius of each recycling station; Step S34: spatially locating all garbage disposal recycling station nodes one by one, thereby generating spatial position coordinates of each recycling station; Step S35: performing a recycling bin spatial distribution analysis according to the spatial position coordinates of each recycling bin to generate recycling bin spatial distribution data; Step S36: Perform distribution positioning fitting on the spatial distribution data of recycling stations according to the recycling processing service radius of each recycling station, and construct a distribution map of recycling stations in the city.

6. The method for full life cycle management of solid waste according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: identifying each waste source location information according to the waste source monitoring data; Step S42: performing recycling station location matching on each waste source location information based on the recycling station distribution map in the city, thereby obtaining a matching recycling station for each source; Step S43: Dynamically plan the path for each matching recycling station at the source to generate the optimal solid waste transportation path; Step S44: making dynamic solid waste transportation decisions based on the optimal solid waste transportation route to generate a dynamic solid waste transportation strategy.

7. The method for full life cycle management of solid waste according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: making intelligent recycling and processing decisions based on the real-time solid waste management database, and generating a waste recycling and processing plan for each recycling station; Step S52: Performing recycling based on the waste recycling scheme of each recycling station, and monitoring the recycling process in real time; Step S53: performing recycling processing logic analysis between the recycling processing links to generate recycling processing logic relationships; Step S54: Calculating the recycling efficiency of the recycling process to generate solid waste recycling efficiency; Step S55: Perform intelligent recycling optimization based on solid waste recycling efficiency and recycling processing logic to generate an intelligent recycling optimization strategy.

8. The method for full life cycle management of solid waste according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: Identify and recycle feedback information; Step S62: performing intelligent collaborative learning on the intelligent recycling optimization strategy, the dynamic solid waste transportation strategy and the dynamic solid waste sorting strategy according to the recycling feedback information to generate an intelligent collaborative control strategy; Step S63: Perform global monitoring optimization on the intelligent collaborative control strategy and build a full-cycle monitoring optimization model.

9. A full life cycle management system for solid waste, characterized in that: The method for implementing the whole life cycle management of solid waste according to claim 1 comprises: A distributed storage module is used to collect waste source monitoring data based on sensors, perform distributed storage management based on the waste source monitoring data, and build a solid waste real-time management database; Solid waste sorting module, used to predict waste recycling types one by one according to the solid waste real-time management database, and perform dynamic sorting control processing, so as to generate dynamic solid waste sorting strategy; The distribution positioning module is used to obtain the solid waste recycling log; identify the nodes of the garbage disposal and recycling stations in the city based on the solid waste recycling log, perform distribution positioning fitting, and build a distribution map of recycling stations in the city; Solid waste transportation module, which is used to identify the location information of each waste source based on the waste source monitoring data, and make dynamic solid waste transportation decisions based on the distribution map of recycling stations in the city to generate dynamic solid waste transportation strategies; The recycling optimization module is used to make intelligent recycling processing decisions and intelligent recycling optimization based on the real-time solid waste management database to generate intelligent recycling optimization strategies; The full-cycle monitoring module is used to conduct intelligent collaborative learning and global monitoring optimization of intelligent recycling optimization strategies, dynamic solid waste transportation strategies and dynamic solid waste sorting strategies based on recycling feedback information, and to build a full-cycle monitoring optimization model.

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