Multi-community-oriented AI intelligent household garbage control treatment system and treatment method thereof

Abstract

The invention relates to the technical field of environment intelligent management, and discloses a multi-community-oriented AI intelligent household garbage control processing system which comprises a multi-mode sensing module, a data processing and edge calculation module, a garbage quantity prediction and scheduling optimization module, an intelligent decision and multi-source data fusion module and a self-adaptive adjustment and feedback mechanism module. The multi-community-oriented AI intelligent household garbage control processing method comprises the following steps that S1, a multi-modal sensing module collects garbage data; s2, an edge calculation module performs preprocessing, classification and encrypted transmission; s3, optimizing transportation and scheduling; s4, generating an optimal processing decision; and S5, adaptively adjusting the strategy. Through the technical scheme of combining multi-modal perception and edge calculation, accurate garbage data acquisition and efficient processing in a multi-community environment are realized, the technical effects of reducing data transmission burden and improving garbage classification data quality are achieved, and garbage classification is more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of ambient intelligent management, and particularly to an AI intelligent domestic waste control and processing system and its processing method for multiple communities. Background Art

[0002] In the current waste treatment system, waste classification and treatment in a multi-community environment mainly rely on fixed rules and traditional scheduling modes. Generally, each community has an independent waste collection point, and waste is transported to a treatment station or recycling center through regular cleaning. Waste classification mainly relies on single-sensor technology or manual sorting, and intelligent trash cans and weighing systems have also been introduced in some areas to improve the accuracy of waste classification. For waste transportation, common scheduling methods are based on preset fixed routes or periodic scheduling, rather than dynamic adjustment according to the actual waste volume. In addition, cloud storage and processing have been introduced in data management in some areas to improve the efficiency of waste classification and cleaning scheduling. However, in a multi-community environment, there are significant differences in waste generation volume, waste composition, and residents' classification habits in different regions, and traditional treatment methods still face certain technical challenges in terms of accuracy and adaptability.

[0003] First of all, waste classification mainly relies on single sensors or manual sorting, making it difficult to achieve high-precision and multi-dimensional waste identification, resulting in limited classification accuracy and affecting the efficiency of subsequent waste treatment processes. Secondly, waste cleaning still mainly relies on fixed times and fixed routes, lacking a dynamic adjustment mechanism for changes in waste volume, which is prone to waste overflow or waste of transportation resources. In addition, the decision-making mode is mainly based on single-community optimization, and waste treatment resources between different communities cannot be effectively shared, and some waste treatment facilities are in a state of long-term overloading or underloading, affecting the balance and resource utilization rate of the overall system. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an AI intelligent domestic waste control and processing system and its processing method for multiple communities, which solves the problem of how to improve the processing efficiency and classification accuracy of domestic waste through multi-modal perception, intelligent scheduling, and dynamic feedback mechanisms in a multi-community environment.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An AI intelligent domestic waste control and processing system for multiple communities, including:

[0006] A multi-modal perception module, which is used to collect multi-dimensional data of waste through multiple sensors;

[0007] A data processing and edge computing module, which is connected to the multi-modal perception module, and is used to locally process and preliminarily classify the multi-dimensional data collected by the multi-modal perception module, and perform data encryption and transmission;

[0008] The garbage volume prediction and scheduling optimization module, which is connected to the data processing and edge computing module, is used to predict the garbage generation volume according to the historical data and real-time data transmitted by the data processing and edge computing module, and optimize the garbage transportation and processing scheduling;

[0009] The intelligent decision-making and multi-source data fusion module, which is respectively connected to the data processing and edge computing module and the garbage volume prediction and scheduling optimization module, is used to generate a globally optimal garbage processing decision based on the garbage processing data and external environment data of multiple communities;

[0010] The adaptive adjustment and feedback mechanism module, which is respectively connected to the intelligent decision-making and multi-source data fusion module and the garbage volume prediction and scheduling optimization module, is used to dynamically adjust the garbage classification, transportation route and processing strategy according to the real-time feedback information.

[0011] Preferably, the multimodal perception module includes the following units:

[0012] The visual perception unit is used to obtain the image data of the garbage through an image sensor, and perform image processing through a convolutional neural network to identify the garbage type and form;

[0013] The gas perception unit is used to detect the harmful gases generated during the garbage decomposition process through a gas sensor, and monitor the gas concentration at the same time to provide real-time data;

[0014] The sound perception unit is used to capture the sound characteristics of the garbage when it lands through a sound sensor, and perform a preliminary classification of the garbage based on audio signal processing technology;

[0015] The vibration perception unit is used to monitor the vibration during the garbage transportation process through a vibration sensor, and use the vibration sensor to detect transportation abnormalities to assist in judging the garbage transportation situation;

[0016] The near-infrared perception unit is used to identify the chemical composition of the garbage through a near-infrared sensor to support the further classification and processing of the garbage.

[0017] Preferably, the data processing and edge computing module includes the following units:

[0018] The edge computing unit is used to perform local data processing and preliminary classification within each community, and upload the classification results to the central platform;

[0019] The local data processing unit is used to perform data preprocessing, feature extraction and preliminary classification, and optimize the processing results;

[0020] The encryption transmission unit encrypts the processed data and uses the Advanced Encryption Standard algorithm to ensure the secure transmission of the data.

[0021] Preferably, the garbage volume prediction and scheduling optimization module includes the following units:

[0022] A garbage volume prediction unit that analyzes historical data, real-time data, and external environmental factors through an LSTM network to predict the trend of garbage generation volume;

[0023] A path optimization unit that optimizes the garbage transportation path using the particle swarm optimization algorithm and the genetic algorithm;

[0024] An emergency situation scheduling unit that is used to correct the garbage generation volume according to special factors and adjust the transportation scheduling plan in real time.

[0025] Preferably, the intelligent decision-making and multi-source data fusion module includes the following units:

[0026] A local data fusion unit that is used to integrate garbage data from different communities and generate the optimal garbage treatment plan within the community;

[0027] A global data fusion unit that is used to combine the garbage treatment data and environmental data of multiple communities to generate a global optimization decision and ensure the reasonable allocation of resources;

[0028] The garbage treatment data includes garbage categories, quantities, recovery rates, and treatment methods;

[0029] The environmental data includes temperature, humidity, air quality, and rainfall;

[0030] A privacy protection unit that adopts federated learning technology to protect the privacy of data in each community through distributed computing and ensure that personal data is not leaked.

[0031] Preferably, the adaptive adjustment and feedback mechanism module includes the following units:

[0032] A real-time feedback unit that is used to receive real-time feedback information from users, garbage sorting equipment, and transportation vehicles;

[0033] An adaptive adjustment unit that dynamically adjusts garbage sorting, transportation paths, and treatment strategies based on the deep reinforcement learning algorithm through real-time feedback;

[0034] A user interaction unit that allows users to provide feedback on garbage sorting and treatment results through a mobile terminal;

[0035] The feedback specifically includes whether the garbage sorting result is correct, whether the treatment is thorough, and whether it meets environmental protection requirements.

[0036] Preferably, the edge computing unit includes the following mechanisms:

[0037] A computing resource scheduling mechanism, which is used to dynamically schedule computing tasks according to the computing power of each edge computing node, optimize resource allocation, and avoid excessive node load;

[0038] A fault tolerance mechanism, which is used to automatically switch to a standby node when a computing node fails;

[0039] The optimization of resource allocation includes task migration, load balancing, and priority scheduling strategies.

[0040] Preferably, both the local data fusion unit and the global data fusion unit include the following mechanisms:

[0041] A signal weighting mechanism, which dynamically adjusts the weights of each sensor in the data fusion process according to the intensity and credibility of each sensor signal;

[0042] A multi-modal data integration mechanism, which performs weighted integration on data from different sensors to generate a comprehensive waste classification decision;

[0043] The classification decision determines whether further processing or recycling is required based on the type, form, processing priority, and classification accuracy of the waste.

[0044] Preferably, the adaptive adjustment unit includes the following mechanisms:

[0045] A model update mechanism, which updates the optimization model of the waste classification and treatment strategy in real time according to the real-time feedback data of the system;

[0046] The model is established based on the multi-dimensional data collected;

[0047] A multi-objective optimization mechanism, which optimizes waste classification, path scheduling, and processing efficiency, taking into account environmental protection and cost-effectiveness.

[0048] An AI intelligent domestic waste control and treatment method for multiple communities includes the following steps:

[0049] S1. Collect multi-dimensional data of waste through a multi-modal perception module;

[0050] S2. Perform local preprocessing and preliminary classification on the multi-dimensional data through an edge computing module, and encrypt and transmit the processing results to the central platform;

[0051] S3. Use the waste generation prediction and scheduling optimization module to predict the waste generation amount, and adjust the waste transportation path and scheduling plan through an optimization algorithm;

[0052] S4. Through the intelligent decision-making and multi-source data fusion module, fuse the multi-dimensional data of the waste in each community through the central platform to generate a globally optimal waste treatment decision;

[0053] S5. According to the real-time feedback information of the central platform, dynamically adjust the garbage classification, transportation routes, and treatment strategies through the adaptive adjustment and feedback mechanism module;

[0054] The multi-dimensional data includes visual data, gas data, sound data, vibration data, and near-infrared spectral data.

[0055] The present invention provides an AI intelligent domestic waste control and treatment system and its treatment method for multiple communities, having the following beneficial effects:

[0056] 1. Through the technical solution of combining multi-modal perception and edge computing, the present invention realizes accurate garbage data collection and efficient processing in a multi-community environment, achieving the technical effects of reducing the data transmission burden and improving the quality of garbage classification data. Compared with the prior art that relies on a single data source for garbage classification, resulting in low classification accuracy and poor adaptability, the deficiencies of insufficient garbage classification data and unstable classification results in a multi-community environment are solved, making garbage classification more accurate and reliable.

[0057] 2. The present invention adopts an intelligent scheduling optimization strategy based on predicted garbage volume. In the case of different garbage generation patterns in different communities, it predicts the garbage generation trend in advance and optimizes the garbage transportation and treatment plan, achieving the technical effects of reducing the garbage retention time and optimizing the allocation of cleaning and transportation resources. Compared with the prior art that relies on fixed time and fixed routes for garbage cleaning and transportation, resulting in low garbage cleaning and transportation efficiency and limited treatment capacity, the deficiencies of unbalanced garbage treatment and lack of flexibility in transportation scheduling in a multi-community environment are solved, making the garbage treatment system more intelligent and efficient.

[0058] 3. The present invention adopts an intelligent decision-making and multi-source data fusion model to globally optimize the garbage treatment data of different communities, realizing cross-regional resource coordination and dynamic allocation of treatment capabilities, achieving the technical effects of improving the overall garbage treatment efficiency and reducing waste of treatment resources. Compared with the prior art that only relies on local data of a single community for local optimization, resulting in large differences in garbage treatment capabilities among different communities and insufficient overall coordination, the deficiencies of lack of unified planning for garbage treatment and low resource utilization rate in a multi-community environment are solved, enabling the garbage treatment systems of each community to form an intelligent collaborative mode.

[0059] 4. The present invention adopts an adaptive adjustment and real-time feedback mechanism, enabling the garbage treatment system to dynamically optimize the classification strategy and scheduling plan according to the garbage classification accuracy and treatment status of different communities, achieving the technical effects of improving the garbage classification accuracy and optimizing the classification execution effect. Compared with the problem that the existing garbage classification strategy is fixed and cannot be dynamically optimized according to the change in the proportion of garbage components in different communities, the deficiencies of limited garbage classification accuracy and difficult classification execution in a multi-community environment are solved, making the garbage classification system more intelligent and adaptable. Brief Description of the Drawings

[0060] Figure 1 It is a schematic diagram of the system modules of the present invention;

[0061] Figure 2 It is a schematic diagram of the multi-modal perception module of the present invention;

[0062] Figure 3 It is a schematic diagram of the data processing and edge computing module of the present invention;

[0063] Figure 4 It is a schematic diagram of the garbage volume prediction and scheduling optimization module of the present invention;

[0064] Figure 5 It is a schematic diagram of the intelligent decision-making and multi-source data fusion module of the present invention;

[0065] Figure 6 It is a schematic diagram of the adaptive adjustment and feedback mechanism module of the present invention;

[0066] Figure 7 It is a schematic diagram of the edge computing unit of the present invention;

[0067] Figure 8 It is a schematic diagram of the local data fusion unit and the global data fusion unit of the present invention;

[0068] Figure 9 It is a schematic diagram of the adaptive adjustment unit of the present invention;

[0069] Figure 10 It is a schematic diagram of the method steps of the present invention. Detailed Embodiments

[0070] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0071] Embodiment 1:

[0072] Please refer to the attached Figure 1 , the embodiment of the present invention provides an AI intelligent domestic waste control and treatment system for multiple communities, including:

[0073] A multi-modal perception module, which is used to collect multi-dimensional data of garbage through multiple sensors;

[0074] A data processing and edge computing module, which is connected to the multi-modal perception module, is used to locally process and preliminarily classify the multi-dimensional data collected by the multi-modal perception module, and perform data encryption and transmission;

[0075] A garbage volume prediction and scheduling optimization module, which is connected to the data processing and edge computing module, is used to predict the garbage generation volume according to the historical data and real-time data transmitted by the data processing and edge computing module, and optimize the garbage transportation and processing scheduling;

[0076] An intelligent decision-making and multi-source data fusion module, which is respectively connected to the data processing and edge computing module and the garbage volume prediction and scheduling optimization module, is used to generate a globally optimal garbage processing decision based on the garbage processing data and external environment data of multiple communities;

[0077] An adaptive adjustment and feedback mechanism module, which is respectively connected to the intelligent decision-making and multi-source data fusion module and the garbage volume prediction and scheduling optimization module, is used to dynamically adjust the garbage classification, transportation route and processing strategy according to the real-time feedback information.

[0078] Specifically, in the overall operation process, the collaborative work of each module ensures the high efficiency and intelligence of the garbage processing system. First, the garbage volume prediction and scheduling optimization module analyzes historical data, real-time data and external environmental factors to predict the future garbage generation volume, and optimizes the garbage transportation route according to the prediction results to ensure the efficient operation of the garbage collection and transportation process. On this basis, the intelligent decision-making and multi-source data fusion module integrates data from different communities and the external environment, generates a globally optimal garbage processing decision, and dynamically adjusts it in real time according to different situations. Then, the adaptive adjustment and feedback mechanism module automatically adjusts the system parameters according to the feedback data by monitoring the operation status of the garbage processing system in real time, optimizes the garbage collection, transportation and processing strategies, and makes necessary prediction adjustments to cope with emergencies or abnormal situations. With the cooperation of this series of modules, the entire garbage processing system can achieve the optimal allocation of resources, the balanced distribution of system load and the maximization of garbage processing efficiency, while ensuring the balance of environmental protection and cost-effectiveness, thus improving the operation quality and sustainability of the overall system.

[0079] Please refer to the attached Figure 2 , the multi-modal perception module includes the following units:

[0080] A visual perception unit, which is used to obtain the image data of garbage through an image sensor, and perform image processing through a convolutional neural network to identify the garbage type and form;

[0081] A gas perception unit, which is used to detect the harmful gases generated during the garbage decomposition process through a gas sensor, and monitor the gas concentration at the same time to provide real-time data;

[0082] A sound perception unit, which is used to capture the sound characteristics when garbage lands through a sound sensor, and preliminarily classify the garbage based on audio signal processing technology;

[0083] A vibration perception unit, which is used to monitor the vibration during garbage transportation through a vibration sensor, detect transportation anomalies using the vibration sensor, and assist in judging the garbage transportation situation;

[0084] A near-infrared perception unit, which is used to identify the chemical composition of garbage through a near-infrared sensor to support the further classification and processing of garbage.

[0085] Specifically, this embodiment provides a multimodal perception module for an intelligent domestic garbage control and processing system. This module collects multi-dimensional data of garbage through multiple sensors, including but not limited to visual, gas, sound, vibration, and near-infrared spectral data. This module plays a role in information collection and preliminary classification in the system, and is connected to data processing and edge computing modules, garbage volume prediction and scheduling optimization modules, intelligent decision-making and multi-source data fusion modules, etc. The multimodal perception module provides data support for garbage classification, transportation route optimization, and processing strategy adjustment by providing comprehensive real-time data. Combining the processing results of other modules, the output data of this module can reflect the changes of garbage in real time, which helps to improve the intelligence and efficiency of the garbage management system.

[0086] In some embodiments, the multimodal perception module collects multi-dimensional data related to garbage through various sensors. Through data fusion and processing, accurate identification of multiple characteristics such as garbage type, form, and composition can be achieved.

[0087] In this embodiment, the visual perception unit obtains garbage image data through an image sensor (such as a CMOS or CCD sensor), and uses a convolutional neural network (CNN) for image processing. The CNN model can extract features in the garbage image through multiple convolutional operations and classify the garbage type and form. Specifically, after the garbage image data is preprocessed, it is input into the CNN model for feature extraction, and then the garbage is classified through a fully connected layer. The convolutional kernel used in the CNN is usually defined as a matrix of size , which is convolved with a partial area of the input image, and the calculation formula is as follows:

[0088] ;

[0089] Among them, represents the element of the output feature map after the convolution operation, located in the th row and the th column; is the input image; is the convolutional kernel; is the bias term; is the size of the convolutional kernel; and represent the indices in the convolutional kernel, traversing all elements of the convolutional kernel. Through multiple convolutional and pooling operations, the classification result of the garbage is finally output.

[0090] The gas sensing unit is used to detect harmful gases generated during the garbage decomposition process. This unit uses gas sensors (such as metal oxide gas sensors, semiconductor gas sensors, or electrochemical gas sensors) to detect gases in the air. Specifically, the gas sensor outputs an electrical signal related to the gas concentration through the adsorption and reaction of gases in the environment. For each gas, there is usually a linear relationship between the output signal of the gas sensor and its concentration, and the mapping relationship between the gas concentration and the electrical signal can be obtained through calibration data. The gas concentration can be calculated by the following formula:

[0091] ;

[0092] where, represents the gas concentration; is the sensor output voltage; is the reference voltage; is the voltage change of the sensor; is the maximum gas concentration measured by the sensor.

[0093] The sound sensing unit uses a sound sensor (such as a microphone sensor) to capture the sound characteristics when the garbage lands. Audio signal processing technology is used to analyze the sound data and identify the type of garbage and its treatment method. The sound sensor converts the received sound wave signal into an electrical signal and performs frequency analysis on these signals. The sound signal when the garbage lands can be used to distinguish the type of garbage and the treatment process through specific frequency characteristics. For example, by analyzing features such as spectrograms and time-domain waveforms, different types of garbage such as plastics and glass can be effectively distinguished.

[0094] The vibration sensing unit monitors the vibration during the garbage transportation process through a vibration sensor and detects abnormal situations during transportation. Vibration sensors (such as piezoelectric sensors or accelerometers) can capture vibration signals that occur during transportation in real time and analyze characteristics such as the amplitude and frequency of the vibration signals. By monitoring the vibration data, it is possible to determine whether the garbage has suffered collisions or abnormal disturbances during transportation. The monitoring and analysis of vibration data can be quantified by the following formula:

[0095] ;

[0096] where, is the vibration acceleration; is the sensor mass; is the force acting on the sensor. Through real-time analysis of vibration signals, transportation anomalies can be detected in a timely manner and feedback can be provided.

[0097] The near-infrared sensing unit identifies the chemical composition of garbage through near-infrared sensors (such as near-infrared spectroscopy sensors). The near-infrared sensor can identify the composition of substances by emitting and receiving infrared spectra of different wavelengths. Specifically, the chemical composition of garbage is related to the absorption characteristics of the near-infrared spectrum. The sensor analyzes the reflection spectrum diagram or transmission spectrum diagram to identify the composition of different substances. According to different wavelength bands of spectral absorption, the following formula can be used to extract features:

[0098] ;

[0099] where is the wavelength is the transmitted light intensity at the wavelength; is the incident light intensity; is the absorption coefficient of the substance at the wavelength ; is the thickness of the substance; is the base of the natural logarithm, approximately equal to 2.718. By analyzing these spectral data, the material composition of the garbage can be judged, providing a basis for further classification.

[0100] Please refer to Appendix Figure 3 and Appendix Figure 7 , the data processing and edge computing module includes the following units:

[0101] The edge computing unit is used to perform local data processing and preliminary classification within each community and upload the classification results to the central platform;

[0102] The local data processing unit is used to perform data preprocessing, feature extraction and preliminary classification, and optimize the processing results;

[0103] The encryption transmission unit encrypts the processed data and uses the Advanced Encryption Standard algorithm to ensure the secure transmission of data;

[0104] The edge computing unit includes the following mechanisms:

[0105] The computing resource scheduling mechanism is used to dynamically schedule computing tasks according to the computing power of each edge computing node, optimize resource allocation, and avoid overloading of nodes;

[0106] The fault tolerance mechanism is used to automatically switch to a standby node when a computing node fails;

[0107] Optimizing resource allocation includes task migration, load balancing and priority scheduling strategies.

[0108] Specifically, this embodiment relates to a data processing and edge computing module for an intelligent domestic waste treatment system. The core task of this module is to perform local processing and preliminary classification on the multi-dimensional data collected from the multi-modal perception module. At the same time, this module is also responsible for encrypting the processed data and performing secure transmission. Specifically, the data processing and edge computing module can efficiently process data within each community, reduce the burden on the central platform, and achieve distributed computing and decision optimization of the system. The data processing and edge computing module not only improves the response speed but also enhances the intelligence and reliability of the system.

[0109] In some embodiments, the data processing and edge computing module consists of multiple sub-units. Each unit undertakes specific tasks in the data processing process and works together to improve the efficiency of waste classification and treatment. Especially when faced with high-frequency and large-scale sensor data, the local processing ability of edge computing demonstrates its unique advantages.

[0110] In this embodiment, the edge computing unit is used to perform local data processing and preliminary classification within each community. Compared with traditional centralized computing, local processing can effectively reduce data transmission latency and ensure fast response. In some embodiments, the edge computing unit deploys lightweight computing resources to perform real-time preprocessing on data from sensors such as visual perception, gas perception, and sound perception. These preprocessing steps include operations such as data denoising, normalization, and feature extraction. Specifically, when visual perception data passes through the edge computing unit, the image data is first grayscale processed, and then a convolutional neural network (CNN) is used for feature extraction and classification. For gas data, the edge computing unit filters and calibrates the output signal of the sensor, and then uses a predetermined algorithm to predict the gas concentration. Through this distributed computing method, each community can independently process sensor data to ensure the timeliness and accuracy of information.

[0111] In some possible implementation manners, the local data processing unit is responsible for further preprocessing and feature extraction of the collected data. This unit not only completes basic operations such as data denoising and outlier removal but can also improve the data processing effect through deep learning algorithms. For example, for sound data, the local processing unit may use MFCC (Mel Frequency Cepstral Coefficients) to extract the features of the audio signal to support subsequent waste classification tasks. When processing image data, the local processing unit can adopt image segmentation technology to extract different partial regions of the garbage to further improve the classification accuracy.

[0112] As an option, the local data processing unit can also dynamically adjust the data processing strategy according to the different characteristics of the input data through an adaptive algorithm. For example, when there is a large amount of abnormal data in the sensor data, the local data processing unit can start a stronger data denoising algorithm to ensure that the final output classification result has higher reliability.

[0113] To ensure data security, an encryption transmission unit is introduced in this embodiment. After the data processing is completed, this unit encrypts all results and then transmits them to the central platform through a secure channel. In some embodiments, the encryption transmission unit uses the Advanced Encryption Standard (AES) algorithm to encrypt the data. Specifically, during the data encryption process, the encryption transmission unit first generates a random key , and then uses this key to encrypt the data to be encrypted for encryption processing to obtain ciphertext data . The encryption formula is as follows:

[0114] ;

[0115] where represents the ciphertext data; is the encryption key; is the original data. In this way, even if the data is intercepted during transmission, the confidentiality of the data can be effectively protected. In addition, in some embodiments, a combination of symmetric encryption and asymmetric encryption can also be used to further improve data security.

[0116] In the overall workflow of the data processing and edge computing module, multiple sub-modules operate in coordination. First, the multi-dimensional data collected by the multi-modal perception module enters the edge computing unit through the sensor. After the edge computing unit performs local preliminary classification, the data is transmitted to the local data processing unit for further optimization processing. The processing result is then encrypted by the encryption transmission unit and uploaded to the central platform. During this process, the data processing module maintains close contact with other modules such as the garbage volume prediction and scheduling optimization module and the intelligent decision-making and multi-source data fusion module. Through the data flow between modules, the system can adjust the waste classification strategy, transportation route, and processing plan in real time.

[0117] Please refer to Appendix Figure 4 . The garbage volume prediction and scheduling optimization module includes the following units:

[0118] The garbage volume prediction unit analyzes historical data, real-time data, and external environmental factors through an LSTM network to predict the trend of garbage generation volume;

[0119] A path optimization unit that optimizes the garbage transportation path using the particle swarm optimization algorithm and the genetic algorithm;

[0120] A sudden situation scheduling unit that is used to correct the garbage generation volume according to special factors and adjust the transportation scheduling plan in real time.

[0121] Specifically, this embodiment relates to a garbage volume prediction and scheduling optimization module for an intelligent garbage treatment system. The core task of this module is to predict the future garbage generation volume based on historical data, real-time data, and external environmental factors, and optimize the scheduling plan for garbage transportation and treatment based on the prediction results. By closely connecting with the multi-modal perception module, the data processing and edge computing module, and the intelligent decision-making and multi-source data fusion module, the garbage volume prediction and scheduling optimization module can dynamically adjust the garbage treatment strategy to achieve the optimal utilization of system resources. Especially in the face of special situations, such as holidays or sudden events, the module can perform flexible scheduling optimization to ensure the stable operation of the system.

[0122] In some embodiments, the garbage volume prediction and scheduling optimization module includes multiple sub-units. These sub-units undertake different tasks in the system and work together to achieve overall optimization. The following is the specific implementation method of each sub-unit in this module.

[0123] In this embodiment, the garbage volume prediction unit analyzes historical data, real-time data, and external environmental factors through an LSTM (Long Short-Term Memory) network. LSTM is a deep learning algorithm that effectively processes time series data and is particularly suitable for predicting time-related change trends. Specifically, the garbage volume prediction unit uses the historical garbage generation data and real-time garbage data as inputs, combines environmental factors such as temperature, humidity, and weather, and uses the LSTM network to predict the garbage generation volume. The basic model of garbage volume prediction can be expressed as:

[0124] ;

[0125] Among them, represents the predicted garbage volume; is the input vector containing historical garbage data and external environmental data; is the weight parameter of the model. The LSTM network obtains the weights through training and predicts the garbage volume at future time steps. Through this prediction result, the system can predict the future garbage generation volume and provide a decision basis for subsequent scheduling optimization.

[0126] The path optimization unit optimizes the garbage transportation path through the Particle Swarm Optimization (PSO) algorithm and the Genetic Algorithm (GA). Specifically, in some embodiments, the PSO algorithm is used to simulate the path search problem during garbage transportation. The PSO algorithm is based on swarm intelligence, simulating the movement of particles in the search space and iteratively optimizing the optimal path. The GA, on the other hand, optimizes the path by simulating the processes of natural selection and genetics, selecting the most suitable transportation path. The combination of the two can effectively solve the problem of optimizing the transportation path. The path optimization problem can be expressed by the following formula:

[0127] ;

[0128] where, represents the total distance of the path, that is, the cumulative distance function of the entire path; represents a set of path points; represents the point to the point and the distance between the point ; is the total number of path points. Through the PSO and GA optimization algorithms, the system can dynamically select the optimal garbage transportation path to ensure transportation efficiency and reduce transportation costs.

[0129] In some embodiments, the emergency situation scheduling unit can adjust the scheduling plan of garbage treatment and transportation in real time according to special factors. This unit corrects the amount of garbage processed during the operation of the system according to factors such as changes in the external environment, holidays, and special events. For example, during holidays or large-scale events, the amount of garbage generated may fluctuate abnormally. The emergency situation scheduling unit will dynamically adjust the garbage transportation plan according to real-time data to cope with these changes. The scheduling optimization model can be corrected by the following formula:

[0130] ;

[0131] where, is the adjusted amount of garbage; is the amount of garbage predicted by LSTM; is the change in the amount of garbage caused by the emergency situation. For example, can be dynamically calculated through data such as weather forecasts and event scales to correct the future amount of garbage generated. In this way, the system can perform intelligent scheduling according to real-time situations to ensure the rationality and efficiency of the garbage treatment plan.

[0132] After calculating and adjusting the garbage generation volume, the scheduling optimization unit further optimizes resource allocation. By combining garbage volume prediction, transportation route optimization, and emergency situation scheduling data, the scheduling optimization unit reasonably schedules garbage collection equipment, transportation vehicles, and treatment facilities. By establishing a multi-objective optimization model, environmental protection and cost-effectiveness can be taken into account. In the multi-objective optimization model, the objective function usually consists of multiple parts, such as minimizing the transportation distance, maximizing the recycling efficiency, minimizing the treatment time, etc. The objective function can be expressed as:

[0133] ;

[0134] Among them, represents the total distance of the route; represents the time required for transportation; is the cost of transportation and treatment; 、 、 are weight coefficients used to balance the priorities of each objective. Through the multi-objective optimization algorithm, the system can adjust resource allocation in real time to ensure the maximization of each objective.

[0135] Please refer to Appendix Figure 5 and Appendix Figure 8 , the intelligent decision-making and multi-source data fusion module includes the following units:

[0136] The local data fusion unit is used to integrate garbage data from different communities and generate the optimal garbage treatment plan within the community;

[0137] The global data fusion unit is used to combine the garbage treatment data and environmental data of multiple communities to generate global optimization decisions and ensure the reasonable allocation of resources;

[0138] The garbage treatment data includes garbage categories, quantities, recovery rates, and treatment methods;

[0139] The environmental data includes temperature, humidity, air quality, and rainfall;

[0140] The privacy protection unit adopts federated learning technology to protect the privacy of community data through distributed computing and ensure that personal data is not leaked;

[0141] Both the local data fusion unit and the global data fusion unit include the following mechanisms:

[0142] The signal weighting mechanism dynamically adjusts the weights of each sensor in the data fusion process according to the intensity and credibility of each sensor signal;

[0143] The multi-modal data integration mechanism weights and integrates data from different sensors to generate comprehensive garbage classification decisions;

[0144] Classification decision-making, deciding whether further processing or recycling is needed based on the type, form, processing priority, and classification accuracy of the garbage.

[0145] Specifically, this embodiment introduces an intelligent decision-making and multi-source data fusion module. The main function of this module is to generate a globally optimal garbage disposal decision based on garbage disposal data from different communities and external environmental data. Through close connection with the data processing and edge computing module and the garbage volume prediction and scheduling optimization module, the intelligent decision-making and multi-source data fusion module can achieve global optimization of the garbage disposal process. Specifically, through the fusion of multiple data sources and the application of intelligent algorithms, this module can dynamically adjust the garbage classification, processing plan, and resource allocation plan to ensure the efficient operation of the system and the rational use of resources.

[0146] In some embodiments, the intelligent decision-making and multi-source data fusion module includes multiple sub-units, adopting different data fusion technologies and intelligent algorithms to ensure that all available data can be fully utilized in the decision-making process. The combination of these technologies and methods enables this module to make quick and accurate decisions in the face of changing garbage generation situations and external environments.

[0147] In this embodiment, the local data fusion unit is responsible for integrating the garbage data from different communities to generate the optimal garbage disposal plan within the community. Each community has different garbage generation patterns. Therefore, this unit must process diverse data types, including but not limited to garbage categories, quantities, recycling rates, and processing methods. The local data fusion unit integrates the garbage data of each community through methods such as weighted summation and weighted average. Specifically, the basic formula for local data fusion is:

[0148] ;

[0149] where, represents the garbage data of the th community; is the weight of the th community; represents the total number of data sources participating in the fusion; is the data after fusion. Through this method, the garbage data of each community can be integrated to generate a processing plan that adapts to local needs.

[0150] In some embodiments, the global data fusion unit combines the garbage disposal data of multiple communities with external environmental data to generate global optimization decisions. The optimization goal of the global decision is to ensure the reasonable allocation of resources and improve the efficiency of garbage disposal. External environmental data such as temperature, humidity, air quality, and rainfall have an important impact on garbage disposal. Therefore, when processing garbage data, the global data fusion unit needs to comprehensively consider external environmental factors. The objective function of the global optimization decision can be expressed as:

[0151] ;

[0152] where, is the total cost of garbage disposal; is the processing efficiency; is the impact on the environment during the garbage disposal process; , , are the weight coefficients of the objective function. By optimizing this objective function, the system can minimize costs and improve processing efficiency while ensuring environmental protection.

[0153] To protect the data privacy of each community, this embodiment also includes a privacy protection unit. The privacy protection unit uses federated learning technology to ensure that personal privacy information is not leaked during the multi-community data fusion. Federated learning is a distributed learning method that can perform model training without the data leaving the local. Specifically, the privacy protection unit generates a local model by training data locally in each community, and then sends the parameters of the model rather than the data itself to the central platform for aggregation, finally obtaining a global optimization model. Through this method, privacy protection is ensured while achieving efficient fusion of multi-source data.

[0154] In some possible implementation manners, the intelligent decision-making and multi-source data fusion module ensures the real-time and accuracy of the fusion decision through the processing of real-time data streams. For example, when the garbage generation volume of a certain community suddenly increases, the system can timely adjust the global optimization decision according to the new data to avoid overloading the system or wasting resources. Through the continuous update and real-time calculation of multi-source data, the system can maintain a high-efficiency response to meet the changing garbage disposal requirements.

[0155] Please refer to Appendix Figure 6 and Appendix Figure 9 , the adaptive adjustment and feedback mechanism module includes the following units:

[0156] A real-time feedback unit, which is used to receive real-time feedback information from users, garbage classification devices, and transportation vehicles;

[0157] An adaptive adjustment unit that, based on a deep reinforcement learning algorithm, dynamically adjusts waste classification, transportation routes, and processing strategies through real-time feedback;

[0158] A user interaction unit that allows users to provide feedback on waste classification and processing results via a mobile device;

[0159] The feedback specifically includes whether the waste classification result is correct, whether the processing is thorough, and whether it meets environmental protection requirements;

[0160] The adaptive adjustment unit includes the following mechanisms:

[0161] A model update mechanism that, based on the real-time feedback data of the system, updates the optimization model for waste classification and processing strategies in real time;

[0162] The model is established based on the multi-dimensional data collected;

[0163] A multi-objective optimization mechanism that optimizes waste classification, path scheduling, and processing efficiency, taking into account environmental protection and cost-effectiveness.

[0164] Specifically, this embodiment relates to an adaptive adjustment and feedback mechanism module that is used to dynamically adjust the operating state of a waste treatment system and optimize system decisions based on real-time feedback. This module is closely connected to the intelligent decision-making and multi-source data fusion module and the waste volume prediction and scheduling optimization module to ensure that the system can operate efficiently under different environments and waste generation patterns. By integrating adaptive control strategies, this module can automatically identify abnormal states and adjust the waste treatment plan within a short time, optimize resource utilization, reduce energy consumption, and improve the system's response speed.

[0165] In some embodiments, the adaptive adjustment and feedback mechanism module consists of multiple sub-units. Each sub-unit is responsible for feedback control at different levels to ensure the flexibility and stability of the system.

[0166] A real-time monitoring and data collection unit: In this embodiment, the real-time monitoring and data collection unit is responsible for collecting various key parameters of the waste treatment system, including waste generation volume, waste transportation routes, the operating state of waste treatment facilities, energy consumption, and environmental factors, etc. Data collection adopts a multi-source fusion method, combining sensor data, camera monitoring data, intelligent terminal feedback data, etc., to achieve comprehensive data coverage.

[0167] Generally, the real-time monitoring data set can be expressed as:

[0168] ;

[0169] Where, represents the set of state data collected at time ; Represents a specific parameter, such as the full load rate of trash cans, the driving trajectory of transport vehicles, the load of treatment facilities, etc. By integrating this data, the system can dynamically determine whether there are abnormalities in the waste treatment process and trigger corresponding adjustment mechanisms.

[0170] In a possible implementation, the adaptive adjustment unit uses a reinforcement learning algorithm to optimize and adjust the waste treatment process. This unit adjusts waste collection, transportation, and treatment strategies based on feedback data to ensure that the system can quickly respond to changing waste generation situations. The core policy function of reinforcement learning is defined as:

[0171] ;

[0172] Where is the optimal policy; represents the state when performing the action the cumulative return value obtained; represents the parameter for finding the maximum value. The system learns the optimal adjustment plan through continuous iteration to meet different waste treatment requirements. For example, when the full overflow rate of trash cans in a certain area continues to rise, the system can automatically increase the waste collection frequency in this area and dynamically adjust the waste transportation route to reduce the waste retention time.

[0173] As an option, the adaptive adjustment unit can also combine fuzzy control methods to adapt to complex and uncertain environments. The basic formula of fuzzy control can be expressed as:

[0174] ;

[0175] Where is the adjusted system control variable; is the fuzzy membership function; is the corresponding weight; represents the total number of terms. Through this method, the system can make flexible adjustments in different waste treatment situations to avoid problems of over-adjustment or under-adjustment.

[0176] Decision rule. This unit adopts a rolling optimization mechanism to dynamically correct decision parameters based on historical adjustment effects and the current system state. For example, the system can adjust the waste collection frequency this week based on the waste treatment situation in the past week and optimize the scheduling plan of waste recycling stations.

[0177] The optimization function of the feedback optimization unit can be defined as:

[0178] ;

[0179] Where is the optimization goal; is the waste treatment cost; is the energy consumption; is the garbage retention time; , , is the weight coefficient; represents the total number of time steps or stages. By optimizing this objective function, the system can balance cost, efficiency, and processing latency, improving the overall performance of the garbage management system.

[0180] In some embodiments, this module also has an adaptive prediction function, which can identify potential problems in the garbage disposal process in advance and actively adjust system parameters. For example, through the joint analysis of historical data and environmental data, the system can predict future peak garbage volumes and allocate disposal resources in advance to avoid garbage overload problems. The prediction formula can be expressed as:

[0181] ;

[0182] where, is the future time is the predicted value of the garbage volume; is the current garbage volume; are external environmental factors (such as weather, holidays, etc.); is the random disturbance term; , , are the regression coefficients. This method can improve the forward-looking decision-making ability of the garbage management system and reduce the impact of emergencies on the system.

[0183] Embodiment 2:

[0184] Please refer to the appendix Figure 10 , an AI intelligent domestic garbage control and disposal method for multiple communities, includes the following steps:

[0185] S1. Collect multi-dimensional data of garbage through a multi-modal perception module;

[0186] S2. Conduct local preprocessing and preliminary classification on the multi-dimensional data through an edge computing module, and encrypt and transmit the processing results to the central platform;

[0187] S3. Use the garbage volume prediction and scheduling optimization module to predict the garbage generation volume, and adjust the garbage transportation route and scheduling plan through an optimization algorithm;

[0188] S4. Through the intelligent decision-making and multi-source data fusion module, fuse the multi-dimensional data of the garbage in each community through the central platform to generate a globally optimal garbage disposal decision;

[0189] S5. According to the real-time feedback information of the central platform, dynamically adjust the garbage classification, transportation route and treatment strategy through the adaptive adjustment and feedback mechanism module;

[0190] The multi-dimensional data includes visual data, gas data, sound data, vibration data and near-infrared spectrum data.

[0191] Specifically, the technical solution details in the corresponding processing method steps are the same as those of the AI intelligent domestic waste control system for multiple communities, which have been described in detail in Embodiment 1 and will not be repeated here.

[0192] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An AI intelligent domestic waste control and treatment system for multiple communities, characterized in that Including: A multi-modal perception module, which is used to collect multi-dimensional data of garbage through multiple sensors; A data processing and edge computing module, which is connected to the multi-modal perception module, and is used to perform local processing and preliminary classification on the multi-dimensional data collected by the multi-modal perception module, and perform data encrypted transmission; A garbage volume prediction and scheduling optimization module, which is connected to the data processing and edge computing module, and is used to predict the garbage generation volume according to the historical data and real-time data transmitted by the data processing and edge computing module, and optimize the garbage transportation and processing scheduling; An intelligent decision-making and multi-source data fusion module, which is respectively connected to the data processing and edge computing module and the garbage volume prediction and scheduling optimization module, and is used to generate a globally optimal garbage processing decision based on the garbage processing data of multiple communities and external environment data; An adaptive adjustment and feedback mechanism module, which is respectively connected to the intelligent decision-making and multi-source data fusion module and the garbage volume prediction and scheduling optimization module, and is used to dynamically adjust the garbage classification, transportation route and processing strategy according to the real-time feedback information.

2. The AI intelligent domestic waste control and treatment system for multiple communities according to claim 1, characterized in that The multi-modal perception module includes the following units: A visual perception unit, which is used to obtain the image data of garbage through an image sensor, and perform image processing through a convolutional neural network to identify the garbage type and form; A gas perception unit, which is used to detect harmful gases generated during the garbage decomposition process through a gas sensor, and monitor the gas concentration at the same time to provide real-time data; A sound perception unit, which is used to capture the sound characteristics when the garbage lands through a sound sensor, and perform preliminary classification on the garbage based on audio signal processing technology; A vibration perception unit, which is used to monitor the vibration during the garbage transportation process through a vibration sensor, and use the vibration sensor to detect transportation anomalies to assist in judging the garbage transportation situation; A near-infrared perception unit, which is used to identify the chemical composition of garbage through a near-infrared sensor to support the further classification and processing of garbage.

3. The AI intelligent domestic waste control and treatment system for multiple communities according to claim 1, characterized in that, The data processing and edge computing module includes the following units: An edge computing unit, which is used to perform local data processing and preliminary classification within each community, and upload the classification results to the central platform; A local data processing unit, which is used to perform data preprocessing, feature extraction and preliminary classification, and optimize the processing results; An encrypted transmission unit, which encrypts the processed data and uses the Advanced Encryption Standard algorithm to ensure the secure transmission of data.

4. The AI intelligent domestic waste control and treatment system for multiple communities according to claim 1, characterized in that, The garbage volume prediction and scheduling optimization module includes the following units: A garbage volume prediction unit, which analyzes historical data, real-time data and external environmental factors through an LSTM network to predict the trend of garbage generation volume; A path optimization unit, which optimizes the garbage transportation path by using the particle swarm optimization algorithm and the genetic algorithm; An emergency situation scheduling unit, which is used to correct the garbage generation volume according to special factors and adjust the transportation scheduling plan in real time.

5. The AI intelligent domestic waste control and treatment system for multiple communities according to claim 1, wherein The intelligent decision-making and multi-source data fusion module includes the following units: A local data fusion unit, which is used to integrate the garbage data from different communities and generate the optimal garbage processing plan within the community; A global data fusion unit, which is used to combine the garbage disposal data and environmental data of multiple communities to generate global optimization decisions and ensure the reasonable allocation of resources; The garbage disposal data includes garbage categories, quantities, recovery rates, and treatment methods; The environmental data includes temperature, humidity, air quality, and rainfall; A privacy protection unit, which uses federated learning technology to protect the privacy of data of each community through distributed computing and ensure that personal data is not leaked.

6. The AI intelligent domestic waste control and treatment system for multiple communities according to claim 1, characterized in that, The adaptive adjustment and feedback mechanism module includes the following units: A real-time feedback unit, which is used to receive real-time feedback information from users, garbage classification devices, and transportation vehicles; An adaptive adjustment unit, which based on the deep reinforcement learning algorithm, dynamically adjusts garbage classification, transportation routes, and treatment strategies through real-time feedback; A user interaction unit, which allows users to provide feedback on garbage classification and treatment results through a mobile terminal; The feedback specifically includes whether the garbage classification result is correct, whether the treatment is thorough, and whether it meets environmental protection requirements.

7. The AI intelligent domestic waste control and treatment system for multiple communities according to claim 3, characterized in that The edge computing unit includes the following mechanisms: A computing resource scheduling mechanism, which is used to dynamically schedule computing tasks according to the computing capabilities of each edge computing node, optimize resource allocation, and avoid excessive node loads; A fault tolerance mechanism, which is used to automatically switch to a standby node when a computing node fails; The optimization of resource allocation includes task migration, load balancing, and priority scheduling strategies.

8. The AI intelligent domestic waste control and treatment system for multiple communities according to claim 5, characterized in that Both the local data fusion unit and the global data fusion unit include the following mechanisms: A signal weighting mechanism, which dynamically adjusts the weights of each sensor in the data fusion process according to the intensity and credibility of each sensor signal; A multi-modal data integration mechanism, which performs weighted integration on data from different sensors to generate comprehensive garbage classification decisions; The classification decision determines whether further treatment or recycling is required based on the type, form, treatment priority, and classification accuracy of the garbage.

9. The AI intelligent domestic waste control and treatment system for multiple communities according to claim 6, wherein The adaptive adjustment unit includes the following mechanisms: A model update mechanism, which real-time updates the optimization model of garbage classification and treatment strategies according to the real-time feedback data of the system; The model is established based on the multi-dimensional data collected; A multi-objective optimization mechanism, which optimizes garbage classification, path scheduling, and treatment efficiency, taking into account environmental protection and cost-effectiveness.

10. An AI intelligent domestic waste control and treatment method for multiple communities, based on the AI intelligent domestic waste control and treatment system for multiple communities according to any one of claims 1-9, characterized in that, Including the following steps: S1. Collect multi-dimensional data of garbage through a multi-modal perception module; S2. Locally preprocess and preliminarily classify the multi-dimensional data through an edge computing module, and encrypt and transmit the processing results to the central platform; S3. Use a garbage volume prediction and scheduling optimization module to predict the garbage generation volume, and adjust the garbage transportation route and scheduling plan through an optimization algorithm; S4. Through an intelligent decision-making and multi-source data fusion module, fuse the multi-dimensional data of garbage from each community through the central platform to generate a globally optimal garbage disposal decision; S5. According to the real-time feedback information from the central platform, dynamically adjust garbage classification, transportation routes, and treatment strategies through the adaptive adjustment and feedback mechanism module; The multi-dimensional data includes visual data, gas data, sound data, vibration data, and near-infrared spectral data.

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