Agricultural waste intelligent classification and treatment system based on intelligent algorithm module

Abstract

The invention relates to an agricultural waste intelligent classification and processing system based on an intelligent algorithm module, and the system comprises a sensor system, a data collection and processing module, an intelligent algorithm module, an execution mechanism module, and a user interface. The data acquisition and processing module cleans and preprocesses sensor data and extracts data features, the intelligent algorithm module provides a feasible scheme for classification and processing of agricultural wastes in the environment, and the execution mechanism module selects automatic processing programs including waste incineration and organic matter composting according to classification results. The user interface provides an interface for interaction between a user and the system, allows the user to monitor the operation state of the system, inquire the processing result and carry out necessary adjustment, and the system realizes intelligent processing of agricultural wastes, improves classification accuracy and processing efficiency, and provides an innovative solution for environmental protection and resource recovery.

Description

TECHNICAL FIELD

[0001] The application relates to the field of intelligent agriculture and heuristic algorithm, and particularly relates to an agricultural waste intelligent classification and treatment system based on an intelligent algorithm module. BACKGROUND

[0002] With the continuous development of agricultural production, the amount of agricultural waste is increasing rapidly, which brings a series of severe challenges to the environment and sustainable development. The traditional waste treatment method usually has the problems of inaccurate classification and low efficiency, resulting in resource waste and environmental pollution. Waste treatment mainly depends on manual classification and traditional treatment methods such as landfill and incineration. However, these methods have a series of problems. Manual classification is affected by subjective factors, has limited accuracy, and has high cost. Landfill method leads to waste of land resources and groundwater pollution, and incineration releases a large amount of harmful gases, which has an undeniable impact on the atmospheric environment. Therefore, it is particularly urgent to develop an intelligent waste treatment system.

[0003] With the rapid development of information technology and artificial intelligence, intelligent waste treatment system has gradually become a research hotspot in the field of waste management. Advanced sensing technology, deep learning algorithm and automation control technology are constantly mature, which provides a solid technical foundation for the construction of intelligent classification and treatment system. This trend not only improves the accuracy and efficiency of waste treatment, but also helps to maximize the use of resources and reduce the burden on the environment. Agricultural waste contains a large amount of recyclable resources such as organic matter, metal and plastic. The traditional treatment method cannot effectively recycle these resources, resulting in huge resource waste. The intelligent classification and treatment system is proposed to maximize the recycling and reuse of valuable substances in waste through accurate classification and efficient treatment, which conforms to the concept of sustainable development.

[0004] With the increase of population and the expansion of agricultural production, the pressure on the environment caused by agricultural waste is increasing. Improper disposal of waste leads to land pollution, water resource pollution and atmospheric environmental pollution, which further affects the ecological balance and human health. Solving the problem of agricultural waste has become a top priority to protect the environment and maintain ecological balance. Countries have formulated a series of regulations and policies to regulate waste treatment and recycling. The introduction of these regulations not only strengthens the supervision of waste treatment, but also provides policy support for the research and development of intelligent classification and treatment system. Therefore, it has become the research direction of scientific research institutions and enterprises in various countries to invent a waste treatment system that meets the requirements of regulations, is efficient and feasible.

[0005] With the increasing awareness of environmental protection and sustainable development, the requirements for waste treatment are becoming increasingly stringent. The demand for efficient and environmentally friendly waste management solutions in society has driven the innovation and development of related technologies. An intelligent waste classification and treatment system meets the expectations of green environmental protection and has broad social significance and market potential. SUMMARY

[0006] In view of the above problems, the present application aims to provide an agricultural waste intelligent classification and treatment system.

[0007] The purpose of the present application is achieved by the following technical solutions:

[0008] An agricultural waste intelligent classification and treatment system, the system comprises a sensor system, a data acquisition and processing module, an intelligent algorithm module, an execution mechanism module and a user interface, the sensor system is responsible for collecting the physical characteristics and chemical composition information of the waste, the physical characteristics include the color, shape, texture, temperature, humidity and weight of the waste, and the chemical composition information includes the chemical composition of the waste; the received raw data may exist noise, outliers and incomplete conditions, therefore, the data acquisition and processing module cleans, pre-processes and extracts data features of the sensor data before further processing; the intelligent algorithm module provides a feasible solution for the classification and treatment of agricultural waste in the environment, the execution mechanism module selects an automatic processing program according to the classification result, including garbage incineration and organic matter composting, and the user interface provides an interface for user interaction with the system, allowing the user to monitor the system running state and query the processing result.

[0009] Further, the sensor system comprises an image sensor, a temperature sensor, a humidity sensor, a chemical composition sensor, a weight sensor and a distance sensor, wherein the image sensor is used to obtain visual information of the waste, through a high-resolution camera, the system can capture the appearance characteristics of the waste, including color, shape and texture, the temperature sensor is used to measure the temperature of the waste, different types of waste have different temperature characteristics, so that the temperature sensor can identify and classify the waste, the humidity sensor measures the humidity level of the waste, the chemical composition sensor is used to detect the chemical composition in the waste, by analyzing the chemical composition of the waste, the system more accurately identifies the type of waste, and the weight sensor is used to measure the weight of the waste, to estimate the quantity and density of the waste.

[0010] Furthermore, the data acquisition and processing module receives waste data from the sensor system. The received sensor data contains noise, outliers, and incompleteness. Therefore, before further processing, the data needs to be cleaned and preprocessed. This step includes data denoising, outlier handling, and missing value imputation to ensure that subsequent intelligent algorithms can learn and analyze based on high-quality data. After data cleaning and preprocessing, data features are extracted from the waste data. These features include information on color, shape, temperature, humidity, and chemical composition. Feature extraction aims to reduce the dimensionality of the data while retaining information that is meaningful for waste classification.

[0011] Furthermore, agricultural waste is classified at agricultural waste collection points using sensor systems and data acquisition and processing modules. Assuming an agricultural environment with agricultural waste recycling stations, specific types of agricultural waste are processed based on their physical characteristics and chemical composition. A connection is established between the agricultural waste collection points and the recycling stations via transport vehicles, which deliver the agricultural waste from the collection points to the recycling stations for processing. The intelligent algorithm module described in this invention provides a solution, as detailed below:

[0012] In the field covered by this invention, transport vehicles deliver agricultural waste from agricultural waste collection sites to agricultural waste recycling stations for processing, and the following mathematical model is established:

[0013]

[0014] Where F is the objective function, λ ijk Let k be the decision variable, representing whether transport vehicle k is transported from target point i to j, as follows:

[0015]

[0016] C k Let C represent the cost of using transport vehicle k, where K is the set of transport vehicles, N is the set of agricultural waste collection sites, satisfying N = {0, 1, ..., n}, where n is the total number of waste collection sites, i, j, and k are indices, where i ∈ N. If i = 0, it represents the departure point of the transport vehicle. D represents agricultural waste recycling stations, with the following constraints: length(D) ≤ length(N), indicating that the number of agricultural waste recycling stations does not exceed the number of agricultural waste collection sites, where length() represents the length of the set. Also, D ∩ N ≠ Φ, indicating that agricultural waste collection stations can be agricultural waste recycling stations, where Φ represents the empty set. t Indicates time cost, This represents the load capacity of vehicle k after it retrieves agricultural waste at target point i. V represents the load of the transport vehicle k after unloading agricultural waste at target point j. In this invention, the agricultural waste recycling station only processes waste that can be recycled at that station; therefore, the number of agricultural wastes that the transport vehicle k can unload at each agricultural waste recycling station is limited. k f represents the speed of the transport vehicle k. k This represents the variable costs of transporting vehicles, including toll fees, repair costs, and maintenance costs. ∫V k Let dt represent the actual distance traveled by the transport vehicle k, and include the following constraints:

[0017]

[0018] Where, ∑ i ∑ j λ ijk ≤1, This means that each transport vehicle can only be used once, ∑ i ∑ k λ ijk ≤1, This means that each transport vehicle, after loading at the agricultural waste site, can only go to one agricultural waste recycling station for unloading at a maximum of ∑ i λ ijk =∑ i λ jik , This indicates that the transport vehicle's travel path is an undirected closed Hamiltonian path. This indicates that the remaining capacity of the transport vehicle k does not exceed the rated capacity Q.

[0019] Furthermore, in order to obtain the objective function with constraints...

[0020]

[0021] To find the optimal solution, an initial score for the operator is defined. Within the algorithmic field covered by this invention, cost is considered with the highest priority, and time with the second highest priority. The cost operator equilibrium score Z is defined accordingly. C Equilibrium score Z of time cost operator T as follows:

[0022]

[0023]

[0024] Where E() is the mathematical expectation, T ik T is the time required for transport vehicle k to reach agricultural waste site i for loading. jk The time required for transport vehicle k to reach agricultural waste recycling station j and unload. For the cost weighting operator of transport vehicle k, For the time cost weighting operator, f k For the variable cost weighting operator of transport vehicle k.

[0025] Furthermore, preliminary optimization was performed using a simulated annealing algorithm, with the initial simulated annealing temperature set to T. c0 Let the initial solution set be Λ, where Λ is a three-dimensional matrix, specifically:

[0026]

[0027] Where, λ 111 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station. 121 λ represents the decision quantity for the first transport vehicle to move agricultural waste from the first agricultural waste collection point to the second agricultural waste recycling point. 1length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the first agricultural waste collection point to the length(D)th agricultural waste recycling point. 211 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste site to the first agricultural waste recycling station. 221 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste site to the second agricultural waste recycling station. 2length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste collection site to the length(D)th agricultural waste recycling site. length(N)11 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the first agricultural waste recycling station. length(N)21 Let λ represent the decision quantity for the length(N)th transport vehicle to transport waste from the second agricultural waste site to the second agricultural waste recycling station. length(N)length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the length (N)-th agricultural waste collection point to the length (D)-th agricultural waste recycling point. 112 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station. 122 λ represents the decision quantity for the second transport vehicle to move from the first agricultural waste disposal site to the second agricultural waste recycling site. 1length(D)2 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the first agricultural waste site to the length(D)th agricultural waste recycling station. 212 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the second agricultural waste collection site to the first agricultural waste recycling site. 222λ represents the decision quantity for the second transport vehicle to transport waste from the second agricultural waste disposal site to the second agricultural waste recycling site. 2length(D)2 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the second agricultural waste site to the length(D)th agricultural waste recycling station. length(N)12 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the first agricultural waste recycling station. length(N)22 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the second agricultural waste recycling station. length(N)length(D)2 Let represent the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the length (D)th agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the first agricultural waste site to the second agricultural waste recycling site. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the first agricultural waste site to the length(D)th agricultural waste recycling site. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the second agricultural waste site to the first agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport waste from the second agricultural waste site to the second agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the second agricultural waste site to the length(D)th agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the length(N)th agricultural waste site to the first agricultural waste recycling station. Let represent the decision quantity for the length (K)th transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the second agricultural waste recycling station. Let Λ represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the length(N)th agricultural waste collection station to the length(D)th agricultural waste recycling station. Λ is also a binary matrix with values ​​of 0 or 1. Let Λ be the initial solution Λ. 0 and the optimal solution Λ * Let the feasible solution field be POOL, then POOL = {Λ 0 The objective function is determined by the solution set of the pool, based on the initial simulated annealing temperature T.c0 A new solution Λ is generated new The new solution Λ new With the initial solution Λ 0 Substitute into the objective function and calculate F(Λ) new )-F(Λ 0 The value of ) will be determined, and further action will be taken based on the following conditions:

[0028]

[0029] Where η is the disturbance parameter, used to control the degree of disturbance, and M max Let C be the perturbation limit, C be the initial deviation value, and Rand() be the random function. Rand(-ηM) max ηM max ) indicates that in (-ηM max ηM max Randomly select values ​​within the interval, for the update T c0 This invention uses a cooling rate method for cooling, and its differential expression is as follows:

[0030] T c0 (LOOP+1)-T c0 (LOOP)=-σ(T c0 (LOOP+1)-T ε )

[0031] Where LOOP is the number of iterations, T ε Let T be the temperature deviation, σ be the cooling rate, and σ > 0. c0 (LOOP+1) represents the initial temperature at iteration number LOOP+1, T. c0 (LOOP) represents the initial temperature at the number of LOOP iterations. The cooling rate is used to reduce the acceptable range of the simulated annealing algorithm POOL, thus reducing the leaps in the search. Then, this invention uses a proof-of-stake consensus mechanism to provide rewards or penalties for feasible solutions in each iteration, setting the total reward pool to R, and defining the rules:

[0032] (1) The prize pool amount decreases in each cycle using a halving method. For example, the prize amount for the first iteration is R / 2, the prize amount for the second iteration is R / 4, and so on, until the prize amount for the LOOP iteration is R / (LOOP). 2 This decreases in turn;

[0033] (2) In each iteration, all feasible solutions in the POOL share the current prize money equally;

[0034] (3) The reward held by the eliminated solution in each iteration will be distributed as a bonus to all feasible solutions in the existing POOL.

[0035] In the above rules, since the computing power system involved in this invention is an intelligent algorithm module, not a sub-computing power model, and the system involved in this invention is for the benefit of users, it is not affected by malicious node computing power competition. The amount of the remaining prize pool decreases exponentially in each iteration. Through the setting of the proof-of-stake consensus mechanism rules, the probability of the optimal solution contained in the initial solution will continue to increase, and the reward held by the eliminated solution in each iteration will be distributed as a bonus to all feasible solutions in the existing POOL. Furthermore, the combination of simulated annealing algorithm, perturbation operator, and cooldown rate settings ensures that the optimal solution will not be suppressed by the reward of the local optimal solution.

[0036] Furthermore, the actuator module automatically executes corresponding waste treatment procedures based on the classification results of the intelligent algorithm. According to the classification results, the actuator module selects the appropriate waste treatment procedure. Different types of waste require different treatment methods, including waste incineration, organic composting, and recycling. Automated waste treatment is achieved by controlling automated processing equipment, including waste incinerators, organic composting equipment, and recycling and sorting machinery. Controlling these devices requires ensuring that their operating parameters match the characteristics of the waste to guarantee an efficient and safe treatment process. During waste treatment, the actuator module adjusts the processing parameters according to the actual situation. By monitoring the waste treatment process, it obtains real-time status information of the processing equipment. The system can record key data of the treatment process for subsequent analysis and optimization. Considering the safety of the waste treatment process, it ensures that the equipment operates within a safe operating range and takes necessary safety measures to prevent accidents. During the operation of the waste treatment system, equipment failures or abnormalities may occur. The actuator module includes fault detection and corresponding response mechanisms to handle faults promptly and reduce system downtime.

[0037] Furthermore, the user interface provides an interface for user interaction with the system, allowing users to monitor the system's operating status, query processing results, and make necessary adjustments. The user interface provides real-time system status information, including the type of waste currently being processed, processing speed, and equipment operating status. The user interface displays the waste classification results from the intelligent algorithm module, presented through charts, images, and text, enabling users to clearly understand the system's accurate classification of different types of waste. The interface provides a historical record of waste processing, including past processing results, system performance indicators, and fault records. The user interface includes a manual intervention interface, allowing users to manually operate or adjust the system, including manually specifying the waste processing method. The system allows for pause and restart operations. The user interface provides system configuration options, allowing users to adjust parameters as needed, including algorithm parameters, processing equipment operating parameters, and alarm thresholds. If the system experiences abnormal conditions or events requiring user attention, the user interface should provide real-time alarms and notifications. The user interface is designed to be simple, intuitive, and easy to operate. The graphical interface presents information through charts and images. The user interface provides remote access functionality, enabling users to remotely monitor and manage the waste treatment system via the internet, improving system accessibility and management efficiency. The user interface supports data export functionality, allowing users to import system-generated data into other analytical tools for further research and analysis.

[0038] The beneficial effects of this invention are as follows: By integrating advanced sensor systems and intelligent algorithms, this invention can accurately and efficiently classify agricultural waste. Compared with traditional manual classification, the system can more comprehensively and accurately identify various characteristics of waste, improving the accuracy and efficiency of classification. Through the intelligent classification and processing system, it can effectively distinguish organic matter, metals, and recyclable plastic resources in waste, maximizing the recycling and utilization of these resources. This helps reduce the demand for new resources, lower production costs, and reduce the adverse environmental impact of waste. This invention introduces automated actuators to automate the treatment of different types of waste. Compared to traditional landfill and incineration methods, the system employs a more environmentally friendly waste treatment process, reducing harmful gas emissions and contributing to atmospheric protection and climate change mitigation. By establishing agricultural waste collection and recycling stations, specific types of agricultural waste are transported to designated locations, further enhancing environmental friendliness. Furthermore, an intelligent algorithm module provides solutions for the classification and disposal of agricultural waste, establishing a dynamic mathematical programming model and constructing constraint functions to ensure the closure of the feasible solution interval. This invention defines an initial operator score, prioritizing cost and time within the algorithmic field, defining a cost operator equilibrium score Z. C Equilibrium score Z of time cost operator TThis invention provides a solution for "cost reduction and efficiency improvement." Furthermore, to obtain the optimal solution of the objective function with constraints, the invention uses simulated annealing for initial optimization. The innovation lies in the addition of a perturbation term to expand and enrich the feasible solution range, and the introduction of a cooling rate to lower the initial temperature of the simulated annealing. This cooling rate reduces the acceptance range of the simulated annealing algorithm's pool, minimizing search jumps. Next, the invention employs a proof-of-stake consensus mechanism to provide rewards or penalties for feasible solutions in each iteration. The rules of the proof-of-stake consensus mechanism are defined, and through these rules, the probability of the optimal solution included in the initial solution continuously increases. The rewards held by solutions eliminated in each iteration are distributed equally among all feasible solutions in the existing pool as bonuses. The combination of simulated annealing, perturbation operators, and cooling rate settings ensures that the optimal solution is not suppressed by the rewards of locally optimal solutions. The user interface provides real-time status monitoring and historical data recording functions, enabling operators to grasp the system's operating status at any time. This not only helps in the timely discovery and resolution of problems but also provides a reliable data foundation for subsequent system optimization. The introduction of automated actuators reduces the need for operator intervention, lowers the safety risks caused by human operation, and the automated processing method of the system helps to improve the safety and stability of the waste treatment process. Attached Figure Description

[0039] The invention will be further illustrated with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the invention. For those skilled in the art, other drawings can be obtained based on the following drawings without any creative effort.

[0040] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0041] The present invention will be further described in conjunction with the following embodiments.

[0042] An intelligent classification and treatment system for agricultural waste includes a sensor system, a data acquisition and processing module, an intelligent algorithm module, an actuator module, and a user interface. The sensor system is responsible for collecting information on the physical characteristics and chemical composition of the waste. The physical characteristics include the color, shape, texture, temperature, humidity, and weight of the waste, while the chemical composition information includes the chemical composition of the waste. Since the received raw data contains noise, outliers, and incompleteness, the data acquisition and processing module cleans and preprocesses the sensor data and extracts data features before further processing. The intelligent algorithm module provides feasible solutions for the classification and treatment of agricultural waste in the environment. The actuator module selects an automated treatment program based on the classification results, including waste incineration and organic composting. The user interface provides an interface for user interaction with the system, allowing users to monitor the system's operating status and query processing results.

[0043] Specifically, the sensor system includes an image sensor, a temperature sensor, a humidity sensor, a chemical composition sensor, a weight sensor, and a distance sensor. The image sensor is used to acquire visual information about the waste. Through a high-resolution camera, the system can capture the appearance characteristics of the waste, including color, shape, and texture. The temperature sensor is used to measure the temperature of the waste. Different types of waste have different temperature characteristics, which allows the temperature sensor to identify and classify the waste. The humidity sensor measures the humidity level of the waste. The chemical composition sensor is used to detect the chemical components in the waste. By analyzing the chemical composition of the waste, the system can more accurately identify the type of waste. The weight sensor is used to measure the weight of the waste and estimate the quantity and density of the waste.

[0044] Specifically, the data acquisition and processing module receives waste data from the sensor system. The received sensor data contains noise, outliers, and incompleteness. Therefore, before further processing, the data needs to be cleaned and preprocessed. This step includes data denoising, outlier handling, and missing value imputation to ensure that subsequent intelligent algorithms can learn and analyze based on high-quality data. After data cleaning and preprocessing, data features are extracted from the waste data. These features include information on color, shape, temperature, humidity, and chemical composition. Feature extraction aims to reduce the dimensionality of the data while retaining information that is meaningful for waste classification.

[0045] Preferably, agricultural waste has been classified at the agricultural waste collection point via a sensor system and data acquisition and processing module. Assuming an agricultural environment with an agricultural waste recycling station, the waste is processed according to its physical characteristics and chemical composition. A connection is established between the agricultural waste collection point and the recycling station via transport vehicles, which deliver the agricultural waste from the collection point to the recycling station for processing. The intelligent algorithm module described in this invention provides the following solution:

[0046] In the field covered by this invention, transport vehicles deliver agricultural waste from agricultural waste collection sites to agricultural waste recycling stations for processing, and the following mathematical model is established:

[0047]

[0048] Where F is the objective function, λ ijk Let k be the decision variable, representing whether transport vehicle k is transported from target point i to j, as follows:

[0049]

[0050] C k Let C represent the cost of using transport vehicle k, where K is the set of transport vehicles, N is the set of agricultural waste collection sites, satisfying N = {0, 1, ..., n}, where n is the total number of waste collection sites, i, j, and k are indices, where i ∈ N. If i = 0, it represents the departure point of the transport vehicle. D represents agricultural waste recycling stations, with the following constraints: length(D) ≤ length(N), indicating that the number of agricultural waste recycling stations does not exceed the number of agricultural waste collection sites, where length() represents the length of the set. Also, D ∩ N ≠ Φ, indicating that agricultural waste collection stations can be agricultural waste recycling stations, where Φ represents the empty set. t Indicates time cost, This represents the load capacity of vehicle k after it retrieves agricultural waste at target point i. V represents the load of the transport vehicle k after unloading agricultural waste at target point j. In this invention, the agricultural waste recycling station only processes waste that can be recycled at that station; therefore, the number of agricultural wastes that the transport vehicle k can unload at each agricultural waste recycling station is limited. k f represents the speed of the transport vehicle k. k This represents the variable costs of transporting vehicles, including toll fees, repair costs, and maintenance costs. ∫V k Let dt represent the actual distance traveled by the transport vehicle k, and include the following constraints:

[0051]

[0052] Where, ∑ i ∑ j λ ijk ≤1, This means that each transport vehicle can only be used once, ∑ i ∑ k λ ijk ≤1, This means that each transport vehicle, after loading at the agricultural waste site, can only go to one agricultural waste recycling station for unloading at a maximum of ∑ i λ ijk =∑ i λ jik , This indicates that the transport vehicle's travel path is an undirected closed Hamiltonian path. This indicates that the remaining capacity of the transport vehicle k does not exceed the rated capacity Q.

[0053] Specifically, in order to obtain the objective function with constraints...

[0054]

[0055] To find the optimal solution, an initial score for the operator is defined. Within the algorithmic field covered by this invention, cost is considered with the highest priority, and time with the second highest priority. The cost operator equilibrium score Z is defined accordingly. C Equilibrium score Z of time cost operator T as follows:

[0056]

[0057] Where E() is the mathematical expectation, T ik T is the time required for transport vehicle k to reach agricultural waste site i for loading. jk The time required for transport vehicle k to reach agricultural waste recycling station j and unload. For the cost weighting operator of transport vehicle k, For the time cost weighting operator, f k For the variable cost weighting operator of transport vehicle k.

[0058] Preferably, the initial optimization is achieved through simulated annealing algorithm, with the initial simulated annealing temperature set to T. c0 Let the initial solution set be Λ, where Λ is a three-dimensional matrix, specifically:

[0059]

[0060] Where, λ 111 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station.121 λ represents the decision quantity for the first transport vehicle to move agricultural waste from the first agricultural waste collection point to the second agricultural waste recycling point. 1length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the first agricultural waste collection point to the length(D)th agricultural waste recycling point. 211 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste site to the first agricultural waste recycling station. 221 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste site to the second agricultural waste recycling station. 2length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste collection site to the length(D)th agricultural waste recycling site. length(N)11 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the first agricultural waste recycling station. length(N)21 Let λ represent the decision quantity for the length(N)th transport vehicle to transport waste from the second agricultural waste site to the second agricultural waste recycling station. length(N)length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the length (N)-th agricultural waste collection point to the length (D)-th agricultural waste recycling point. 112 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station. 122 λ represents the decision quantity for the second transport vehicle to move from the first agricultural waste disposal site to the second agricultural waste recycling site. 1length(D)2 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the first agricultural waste site to the length(D)th agricultural waste recycling station. 212 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the second agricultural waste collection site to the first agricultural waste recycling site. 222 λ represents the decision quantity for the second transport vehicle to transport waste from the second agricultural waste disposal site to the second agricultural waste recycling site. 2length(D)2 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the second agricultural waste site to the length(D)th agricultural waste recycling station. length(N)12 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the first agricultural waste recycling station. length(N)22 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the second agricultural waste recycling station. length(N)length(D)2 Let represent the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the length (D)th agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the first agricultural waste site to the second agricultural waste recycling site. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the first agricultural waste site to the length(D)th agricultural waste recycling site. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the second agricultural waste site to the first agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport waste from the second agricultural waste site to the second agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the second agricultural waste site to the length(D)th agricultural waste recycling station. Let represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the length(N)th agricultural waste site to the first agricultural waste recycling station. Let represent the decision quantity for the length (K)th transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the second agricultural waste recycling station. Let Λ represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the length(N)th agricultural waste collection station to the length(D)th agricultural waste recycling station. Λ is also a binary matrix with values ​​of 0 or 1. Let Λ be the initial solution Λ. 0 and the optimal solution Λ * Let the feasible solution field be POOL, then POOL = {Λ 0 The objective function is determined by the solution set of the pool, based on the initial simulated annealing temperature T. c0 A new solution Λ is generated new The new solution Λ new With the initial solution Λ 0 Substitute into the objective function and calculate F(Λ) new )-F(Λ 0 The value of ) will be determined, and further action will be taken based on the following conditions:

[0061]

[0062] Where η is the disturbance parameter, used to control the degree of disturbance, and M max Let C be the perturbation limit, C be the initial deviation value, and Rand() be the random function. Rand(-ηM) max ηM max) indicates that in (-ηM max ηM max Randomly select values ​​within the interval, for the update T c0 This invention uses a cooling rate method for cooling, and its differential expression is as follows:

[0063] T c0 (LOOP+1)-T c0 (LOOP)=-σ(T c0 (LOOP+1)-T ε )

[0064] Where LOOP is the number of iterations, T ε Let T be the temperature deviation, σ be the cooling rate, and σ > 0. c0 (LOOP+1) represents the initial temperature at iteration number LOOP+1, T. c0 (LOOP) represents the initial temperature at the number of LOOP iterations. The cooling rate is used to reduce the acceptable range of the simulated annealing algorithm POOL, thus reducing the leaps in the search. Then, this invention uses a proof-of-stake consensus mechanism to provide rewards or penalties for feasible solutions in each iteration, setting the total reward pool to R, and defining the rules:

[0065] (1) The prize pool amount decreases in each cycle using a halving method. For example, the prize amount for the first iteration is R / 2, the prize amount for the second iteration is R / 4, and so on, until the prize amount for the LOOP iteration is R / (LOOP). 2 This decreases in turn;

[0066] (2) In each iteration, all feasible solutions in the POOL share the current prize money equally;

[0067] (3) The reward held by the eliminated solution in each iteration will be distributed as a bonus to all feasible solutions in the existing POOL.

[0068] In the above rules, since the computing power system involved in this invention is an intelligent algorithm module, not a sub-computing power model, and the system involved in this invention is for the benefit of users, it is not affected by malicious node computing power competition. The amount of the remaining prize pool decreases exponentially in each iteration. Through the setting of the proof-of-stake consensus mechanism rules, the probability of the optimal solution contained in the initial solution will continue to increase, and the reward held by the eliminated solution in each iteration will be distributed as a bonus to all feasible solutions in the existing POOL. Furthermore, the combination of simulated annealing algorithm, perturbation operator, and cooldown rate settings ensures that the optimal solution will not be suppressed by the reward of the local optimal solution.

[0069] Specifically, the actuator module automatically executes the corresponding waste treatment program based on the classification results of the intelligent algorithm. According to the classification results, the actuator module selects the appropriate waste treatment program. Different types of waste require different treatment methods, including waste incineration, organic composting, and recycling. Automated waste treatment is achieved by controlling automated processing equipment, including waste incinerators, organic composting equipment, and recycling and sorting machinery. Controlling these devices requires ensuring that their operating parameters match the characteristics of the waste to guarantee an efficient and safe treatment process. During waste treatment, the actuator module adjusts the processing parameters according to the actual situation. By monitoring the waste treatment process, it obtains real-time status information of the processing equipment. The system can record key data of the treatment process for subsequent analysis and optimization. Considering the safety of the waste treatment process, it ensures that the equipment operates within a safe operating range and takes necessary safety measures to prevent accidents. During the operation of the waste treatment system, equipment failures or abnormalities may occur. The actuator module includes fault detection and corresponding response mechanisms to handle faults promptly and reduce system downtime.

[0070] Specifically, the user interface provides an interface for user interaction with the system, allowing users to monitor the system's operating status, query processing results, and make necessary adjustments. The user interface provides real-time system status information, including the current type of waste being processed, processing speed, and equipment operating status. The user interface displays the waste classification results from the intelligent algorithm module, presented through charts, images, and text, enabling users to clearly understand the system's accurate classification of different types of waste. The interface provides a historical record of waste processing, including past processing results, system performance indicators, and fault records. The user interface includes a manual intervention interface, allowing users to manually operate or adjust the system, including manually specifying the waste processing method. The system allows for pause and restart operations. The user interface provides system configuration options, allowing users to adjust parameters as needed, including algorithm parameters, processing equipment operating parameters, and alarm thresholds. If the system experiences abnormal conditions or events requiring user attention, the user interface should provide real-time alarms and notifications. The user interface is designed to be simple, intuitive, and easy to operate. The graphical interface presents information through charts and images. The user interface provides remote access functionality, enabling users to remotely monitor and manage the waste treatment system via the internet, improving system accessibility and management efficiency. The user interface supports data export functionality, allowing users to import system-generated data into other analytical tools for further research and analysis.

[0071] The beneficial effects of this invention are as follows: By integrating advanced sensor systems and intelligent algorithms, this invention can accurately and efficiently classify agricultural waste. Compared with traditional manual classification, the system can more comprehensively and accurately identify various characteristics of waste, improving the accuracy and efficiency of classification. Through the intelligent classification and processing system, it can effectively distinguish organic matter, metals, and recyclable plastic resources in waste, maximizing the recycling and utilization of these resources. This helps reduce the demand for new resources, lower production costs, and reduce the adverse environmental impact of waste. This invention introduces automated actuators to automate the treatment of different types of waste. Compared to traditional landfill and incineration methods, the system employs a more environmentally friendly waste treatment process, reducing harmful gas emissions and contributing to atmospheric protection and climate change mitigation. By establishing agricultural waste collection and recycling stations, specific types of agricultural waste are transported to designated locations, further enhancing environmental friendliness. Furthermore, an intelligent algorithm module provides solutions for the classification and disposal of agricultural waste, establishing a dynamic mathematical programming model and constructing constraint functions to ensure the closure of the feasible solution interval. This invention defines an initial operator score, prioritizing cost and time within the algorithmic field, defining a cost operator equilibrium score Z. C Equilibrium score Z of time cost operator T This invention provides a solution for "cost reduction and efficiency improvement." Furthermore, to obtain the optimal solution of the objective function with constraints, the invention uses simulated annealing for initial optimization. The innovation lies in the addition of a perturbation term to expand and enrich the feasible solution range, and the introduction of a cooling rate to lower the initial temperature of the simulated annealing. This cooling rate reduces the acceptance range of the simulated annealing algorithm's pool, minimizing search jumps. Next, the invention employs a proof-of-stake consensus mechanism to provide rewards or penalties for feasible solutions in each iteration. The rules of the proof-of-stake consensus mechanism are defined, and through these rules, the probability of the optimal solution included in the initial solution continuously increases. The rewards held by solutions eliminated in each iteration are distributed equally among all feasible solutions in the existing pool as bonuses. The combination of simulated annealing, perturbation operators, and cooling rate settings ensures that the optimal solution is not suppressed by the rewards of locally optimal solutions. The user interface provides real-time status monitoring and historical data recording functions, enabling operators to grasp the system's operating status at any time. This not only helps in the timely discovery and resolution of problems but also provides a reliable data foundation for subsequent system optimization. The introduction of automated actuators reduces the need for operator intervention, lowers the safety risks caused by human operation, and the automated processing method of the system helps to improve the safety and stability of the waste treatment process.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. An intelligent classification and treatment system for agricultural waste, comprising a sensor system, a data acquisition and processing module, an intelligent algorithm module, an actuator module, and a user interface. The sensor system is responsible for collecting information on the physical characteristics and chemical composition of the waste. The physical characteristics include the color, shape, texture, temperature, humidity, and weight of the waste, and the chemical composition information includes the chemical composition of the waste. Since the received raw data contains noise, outliers, and incompleteness, the data acquisition and processing module cleans and preprocesses the sensor data and extracts data features before further processing. The intelligent algorithm module provides feasible solutions for the classification and treatment of agricultural waste in the environment. The actuator module selects automated processing programs based on the classification results, including waste incineration and organic composting. The user interface provides an interface for users to interact with the system, allowing users to monitor the system's operating status and query processing results. Agricultural waste is classified at agricultural waste collection sites using sensor systems and data acquisition and processing modules. Assuming an agricultural environment with agricultural waste recycling stations, a specific type of agricultural waste is processed based on its physical characteristics and chemical composition. The agricultural waste collection sites and recycling stations are connected by transport vehicles that deliver the agricultural waste from the sites to the recycling stations for processing. The intelligent algorithm module provides the solution.

2. The intelligent classification and treatment system for agricultural waste according to claim 1, characterized in that, Preliminary optimization was performed using a simulated annealing algorithm, with the initial simulated annealing temperature set to T. c0 Let the initial solution set be Λ, where Λ is a three-dimensional matrix, specifically: Where, λ 111 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station. 121 λ represents the decision quantity for the first transport vehicle to move agricultural waste from the first agricultural waste collection point to the second agricultural waste recycling point. 1length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the first agricultural waste collection point to the length(D)th agricultural waste recycling point. 211 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste site to the first agricultural waste recycling station. 221 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste site to the second agricultural waste recycling station. 2length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the second agricultural waste collection site to the length(D)th agricultural waste recycling site. length(N)11 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the first agricultural waste recycling station. length(N)21 Let λ represent the decision quantity for the length(N)th transport vehicle to transport waste from the second agricultural waste site to the second agricultural waste recycling station. length(N)length(D)1 λ represents the decision quantity for the first transport vehicle to transport agricultural waste from the length (N)-th agricultural waste collection point to the length (D)-th agricultural waste recycling point. 112 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station. 122 λ represents the decision quantity for the second transport vehicle to move from the first agricultural waste disposal site to the second agricultural waste recycling site. 1length(D)2 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the first agricultural waste site to the length(D)th agricultural waste recycling station. 212 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the second agricultural waste collection site to the first agricultural waste recycling site. 222 λ represents the decision quantity for the second transport vehicle to transport waste from the second agricultural waste disposal site to the second agricultural waste recycling site. 2length(D)2 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the second agricultural waste site to the length(D)th agricultural waste recycling station. length(N)12 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the first agricultural waste recycling station. length(N)22 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)th agricultural waste site to the second agricultural waste recycling station. length(N)length(D)2 λ represents the decision quantity for the second transport vehicle to transport agricultural waste from the length (N)-th agricultural waste collection point to the length (D)-th agricultural waste recycling point. 11length(K) Let λ represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the first agricultural waste site to the first agricultural waste recycling station. 12length(K) Let λ represent the decision quantity for the length(K)th transport vehicle to move from the first agricultural waste site to the second agricultural waste recycling site. 1length(D)length(K) Let λ represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the first agricultural waste site to the length(D)th agricultural waste recycling site. 21length(K) Let λ represent the decision quantity for the length(K)th transport vehicle to move agricultural waste from the second agricultural waste collection point to the first agricultural waste recycling point. 22length(K) Let λ represent the decision quantity for the length(K)th transport vehicle to transport waste from the second agricultural waste site to the second agricultural waste recycling station. 2length(D)length(K) Let λ represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the second agricultural waste site to the length(D)th agricultural waste recycling site. length(N)1length(K) Let λ represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the length(N)th agricultural waste site to the first agricultural waste recycling station. length(N)2length(K) Let λ represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the length(N)th agricultural waste site to the second agricultural waste recycling station. length(N)length(D)length(K) Let Λ represent the decision quantity for the length(K)th transport vehicle to transport agricultural waste from the length(N)th agricultural waste collection station to the length(D)th agricultural waste recycling station. Λ is also a binary matrix with values ​​of 0 or 1. Let Λ be the initial solution Λ. 0 And the optimal solution Λ*, let the feasible solution field be POOL, then POOL = {Λ 0 The objective function is determined by the solution set of the pool, based on the initial simulated annealing temperature T. c0 A new solution Λ is generated new The new solution Λ new With the initial solution Λ 0 Substitute into the objective function and calculate F(Λ) new )-F(Λ 0 The value of ) will be determined, and further action will be taken based on the following conditions: Where η is the disturbance parameter, used to control the degree of disturbance, and M max Let C be the perturbation limit, C be the initial deviation value, and Rand() be the random function. Rand(-ηM) max ηM max ) indicates that in (-ηM max ηM max Randomly select values ​​within the interval, for the update T c0 The cooling rate is used for temperature reduction, and its difference expression is as follows: T c0 (LOOP+1)-T c0 (LOOP)=-σ(T c0 (LOOP+1)-T ε ) Where LOOP is the number of iterations, T ε Let T be the temperature deviation, σ be the cooling rate, and σ > 0. c0 (LOOP+1) represents the initial temperature at iteration number LOOP+1, T. c0 (LOOP) represents the initial temperature at the number of LOOP iterations. The cooling rate is used to reduce the acceptance range of the simulated annealing algorithm POOL, reducing the leaps in the search. Then, through the proof-of-stake consensus mechanism, a reward or penalty is provided for the feasible solution in each iteration. The total reward pool is set to R, and the rules are defined as follows: (1) The prize pool amount decreases in each cycle using a halving method. For example, the prize amount for the first iteration is R / 2, the prize amount for the second iteration is R / 4, and so on, until the prize amount for the LOOP iteration is R / (LOOP). 2 This decreases in turn; (2) In each iteration, all feasible solutions in the POOL share the current prize money equally; (3) The reward held by the eliminated solution in each iteration will be distributed as a bonus to all feasible solutions in the existing POOL. In the above rules, since the computing power system involved is an intelligent algorithm module, not a sub-computing power model, and the system involved is for the benefit of users, it is not affected by malicious node computing power competition. The amount of the remaining prize pool decreases exponentially with each iteration. Through the setting of the proof-of-stake consensus mechanism rules, the probability of the optimal solution contained in the initial solution will continue to increase. In addition, the reward held by the eliminated solution in each iteration will be distributed as a bonus to all feasible solutions in the existing pool. Furthermore, the combination of simulated annealing algorithm, perturbation operator, and cooldown rate settings ensures that the optimal solution will not be suppressed by the reward of the local optimal solution.

3. The intelligent classification and treatment system for agricultural waste according to claim 2, characterized in that, The intelligent algorithm module provides the following specific solution: The transport vehicles deliver agricultural waste from the agricultural waste collection points to the agricultural waste recycling stations for processing. The following mathematical model is established: Where F is the objective function, λ ijk Let k be the decision variable, representing whether transport vehicle k is transported from target point i to j, as follows: C k Let C represent the cost of using transport vehicle k, where K is the set of transport vehicles, N is the set of agricultural waste collection sites, satisfying N = {0, 1, ..., n}, where n is the total number of waste collection sites, i, j, and k are indices, where i ∈ N. If i = 0, it represents the departure point of the transport vehicle. D represents agricultural waste recycling stations, with the following constraints: length(D) ≤ length(N), indicating that the number of agricultural waste recycling stations does not exceed the number of agricultural waste collection sites, where length() represents the length of the set. Also, D ∩ N ≠ Φ, indicating that agricultural waste collection stations can be agricultural waste recycling stations, where Φ represents the empty set. t Indicates time cost, This represents the load capacity of vehicle k after it retrieves agricultural waste at target point i. V represents the load of transport vehicle k after unloading agricultural waste at target point j. Agricultural waste recycling stations only process waste that can be recycled at that specific station; therefore, the number of agricultural wastes that transport vehicle k can unload at each agricultural waste recycling station is limited. k f represents the speed of the transport vehicle k. k This represents the variable costs of transporting vehicles, including toll fees, repair costs, and maintenance costs. ∫V k Let dt represent the actual distance traveled by the transport vehicle k, and include the following constraints: Where, ∑ i ∑ j λ ijk ≤1, This means that each transport vehicle can only be used once, ∑ i ∑ k λ ijk ≤1, This means that each transport vehicle, after loading at the agricultural waste site, can only go to one agricultural waste recycling station for unloading at a maximum of ∑ i λ ijk =∑ i λ jik , This indicates that the transport vehicle's travel path is an undirected closed Hamiltonian path. This indicates that the remaining capacity of the transport vehicle k does not exceed the rated capacity Q; In order to obtain the objective function with constraints To find the optimal solution, define the initial score of the operator, prioritizing cost and time, and define the equilibrium score Z of the cost operator. C Equilibrium score Z of time cost operator T as follows: Where E() is the mathematical expectation, T ik T is the time required for transport vehicle k to reach agricultural waste site i for loading. jk The time required for transport vehicle k to reach agricultural waste recycling station j and unload. For the cost weighting operator of transport vehicle k, For the time cost weighting operator, f k For the variable cost weighting operator of transport vehicle k.

4. The intelligent sorting and treatment system for agricultural waste according to any one of claims 1-3, characterized in that, The sensor system includes an image sensor, a temperature sensor, a humidity sensor, a chemical composition sensor, a weight sensor, and a distance sensor. The image sensor is used to acquire visual information about the waste. Through a high-resolution camera, the system can capture the appearance characteristics of the waste, including color, shape, and texture. The temperature sensor is used to measure the temperature of the waste. Different types of waste have different temperature characteristics, which allows the temperature sensor to identify and classify the waste. The humidity sensor measures the humidity level of the waste. The chemical composition sensor is used to detect the chemical components in the waste. By analyzing the chemical composition of the waste, the system can more accurately identify the type of waste. The weight sensor is used to measure the weight of the waste and estimate the quantity and density of the waste.

5. The intelligent sorting and treatment system for agricultural waste according to any one of claims 1-3, characterized in that, The data acquisition and processing module receives waste data from the sensor system. The received sensor data contains noise, outliers, and incompleteness. Therefore, before further processing, the data needs to be cleaned and preprocessed. This step includes data denoising, outlier handling, and missing value imputation to ensure that subsequent intelligent algorithms can learn and analyze based on high-quality data. After data cleaning and preprocessing, data features are extracted from the waste data. These features include information on color, shape, temperature, humidity, and chemical composition. Feature extraction aims to reduce the dimensionality of the data while retaining information that is meaningful for waste classification.

6. The intelligent sorting and treatment system for agricultural waste according to any one of claims 1-3, characterized in that, The actuator module automatically executes the corresponding waste treatment program based on the classification results of the intelligent algorithm. According to the classification results, the actuator module will select the appropriate waste treatment program. Different types of waste require different treatment methods, including waste incineration, organic composting, and recycling. The automated treatment of waste is achieved by controlling automated treatment equipment, including waste incinerators, organic composting equipment, and recycling and sorting machinery. Controlling these devices requires ensuring that their operating parameters match the characteristics of the waste to ensure an efficient and safe treatment process. During waste treatment, the actuator module adjusts the treatment parameters according to the actual situation. By monitoring the waste treatment process, it obtains the status information of the treatment equipment in real time. The system can record key data of the treatment process for subsequent analysis and optimization. Considering the safety of the waste treatment process, it ensures that the equipment operates within a safe working range and takes necessary safety measures to prevent accidents. During the operation of the waste treatment system, equipment failures or abnormalities may occur. The actuator module includes fault detection and corresponding response mechanisms to handle faults in a timely manner and reduce system downtime.

7. The intelligent sorting and treatment system for agricultural waste according to any one of claims 1-3, characterized in that, The user interface provides an interface for user interaction with the system, allowing users to monitor system operation status, query processing results, and make necessary adjustments. The user interface provides real-time system status information, including the current type of waste being processed, processing speed, and equipment operating status. The user interface displays the waste classification results from the intelligent algorithm module, presented through charts, images, and text, enabling users to clearly understand the system's accurate classification of different types of waste. The interface provides a historical record of waste processing, including past processing results, system performance indicators, and fault records. The user interface includes a manual intervention interface, allowing users to manually operate or adjust the system, including manually specifying waste processing methods. The system can be paused and started. The user interface provides system configuration settings, allowing users to adjust parameters according to actual needs, including algorithm parameters, processing equipment operating parameters, and alarm thresholds. If the system experiences abnormal conditions or events requiring user attention, the user interface should be able to provide real-time alarms and notifications. The user interface is designed to be simple, intuitive, and easy to operate. The graphical interface presents information through charts and images. The user interface provides remote access functionality, enabling users to remotely monitor and manage the waste treatment system via the Internet, improving system accessibility and management efficiency. The user interface supports data export functionality, allowing users to import system-generated data into other analytical tools for more in-depth research and analysis.

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