Agricultural waste intelligent classification and treatment system

Abstract

The invention relates to an agricultural waste intelligent classification and processing system, which comprises a sensor system, a data acquisition 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 invention relates to the fields of smart agriculture and heuristic algorithms, and specifically to an intelligent classification and treatment system for agricultural waste. Background Art

[0002] With the continuous development of agricultural production, the amount of agricultural waste has increased dramatically, bringing a series of severe challenges to the environment and sustainable development. Traditional waste treatment methods usually have problems of inaccurate classification and low efficiency, resulting in waste of resources and environmental pollution. Waste treatment mainly relies 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 is costly. Landfilling leads to waste of land resources and groundwater pollution, while incineration releases a large amount of harmful gases, which has an impact on the atmospheric environment that cannot be ignored. 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 systems have gradually become a research hotspot in the field of waste management. The continuous maturity of advanced sensing technology, deep learning algorithms and automatic control technology has provided a solid technical foundation for building intelligent classification and treatment systems. This trend not only improves the accuracy and efficiency of waste treatment, but also helps to maximize the utilization of resources and reduce the burden on the environment. Agricultural waste contains a large amount of recyclable resources, such as organic matter, metals and plastics. Traditional treatment methods have failed to effectively recycle these resources, resulting in huge waste of resources. The proposal of intelligent classification and treatment systems aims to maximize the recovery and reuse of valuable substances in waste through precise classification and efficient treatment, which is in line with the concept of sustainable development.

[0004] With the increase in population and the expansion of agricultural production, the pressure of agricultural waste on the environment is increasing. Improper disposal of waste leads to land pollution, water pollution and atmospheric pollution, which in turn affects the ecological balance and human health. Solving the problem of agricultural waste has become a top priority for protecting the environment and maintaining the ecological balance. Countries have formulated a series of laws and policies for waste management to regulate the treatment and recycling of waste. 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 systems. Inventing a waste treatment system that meets regulatory requirements, is efficient and feasible has become a research direction for scientific research institutions and enterprises in various countries.

[0005] As people's awareness of environmental protection and sustainable development continues to improve, the requirements for waste disposal are becoming increasingly stringent. The society's demand for efficient and environmentally friendly waste management solutions has promoted the innovation and development of related technologies. An intelligent waste classification and treatment system meets the society's expectations for green environmental protection and has broad social significance and market potential. Summary of the invention

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

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] The invention discloses an intelligent classification and treatment system for agricultural wastes. The system comprises 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 physical characteristics and chemical composition information of the wastes. The physical characteristics include the color, shape, texture, temperature, humidity and weight of the wastes. The chemical composition information includes the chemical composition of the wastes. The received raw data contains noise, abnormal values ​​and incompleteness. Therefore, before further processing, the data acquisition and processing module cleans and pre-processes the sensor data and extracts data features. The intelligent algorithm module provides a feasible solution for the classification and treatment of agricultural wastes in the environment. The actuator module selects an automated treatment program according to the classification results, including garbage incineration and organic composting. The user interface provides an interface for users to interact with the system, allowing users to monitor the system operation status and query the processing results.

[0009] Furthermore, the sensor system includes 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 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.

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

[0011] Furthermore, agricultural waste has been classified at the agricultural waste site through the sensor system and the data collection and processing module, and it is assumed that in the agricultural environment background, there is an agricultural waste recycling station, and a certain type of agricultural waste is processed according to the physical characteristics and chemical composition information of the waste. The agricultural waste site and the agricultural waste recycling station are connected through a transport vehicle, and the transport vehicle transports the agricultural waste from the agricultural waste site to the agricultural waste recycling station for processing. The intelligent algorithm module of the present invention provides a solution, which is as follows:

[0012] In the field involved in the present invention, a transport vehicle transports agricultural waste from an agricultural waste station to an agricultural waste recycling station for treatment, and the following mathematical model is established:

[0013]

[0014] Among them, F is the objective function, λ ijk is the decision quantity, indicating whether the transport vehicle k is transported from the target point i to j, as follows:

[0015]

[0016] C k represents the cost of using transport vehicle k, K is the set of transport vehicles, N is the set of agricultural waste sites, and satisfies N = {0,1,…,n}, n is the total number of waste sites, i, j, k are index numbers, index number i∈N, if i = 0, it means the shipping point of the transport vehicle, D is the agricultural waste recycling station, and there is a restriction: length(D)≤length(N) means that the number of agricultural waste recycling stations does not exceed the number of agricultural waste sites, where length() represents the length of the set, and D∩N≠Φ, indicating that an agricultural waste site can be an agricultural waste recycling station, where Φ represents the empty set, C t Indicates the time cost, represents the load of agricultural waste after the transport vehicle k picks up the agricultural waste at the target point i, V represents the load after the transport vehicle k unloads the agricultural waste at the target point j. In the present invention, the agricultural waste recycling station only processes the waste that can be recycled at the station. Therefore, the number of unloadings of the transport vehicle k at each agricultural waste recycling station is limited. k represents the speed of transport vehicle k, f k represents the variable cost of the transport vehicle, including the tolls, repair costs and maintenance costs of the transport vehicle, ∫V k dt represents the actual travel distance of the transport vehicle k and contains the following constraints:

[0017]

[0018] in, This means that each delivery vehicle can only be used once. This means that after each transport vehicle goes to an agricultural waste station to load, it can only go to one agricultural waste recycling station to unload. Indicates that the driving path of the transport vehicle is an undirected closed Hamiltonian path, It means that the remaining capacity of transport vehicle k does not exceed the rated capacity Q.

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

[0020]

[0021] The optimal solution is defined, and the initial score of the operator is defined. In the algorithm field involved in the present invention, cost is considered as the highest priority, and time is considered as the second priority. The cost operator equilibrium score Z is defined respectively. C and time cost operator equilibrium score Z T as follows:

[0022]

[0023]

[0024] Among them, E() is the mathematical expectation, T ik is the time required for transport vehicle k to reach agricultural waste loading station i, T jk is the time required for transport vehicle k to reach agricultural waste recycling station j for unloading, is the cost weight operator for transporting vehicle k, is the time cost weight operator, f k is the variable cost weight operator for transporting vehicle k.

[0025] Furthermore, the simulated annealing algorithm is used to preliminarily optimize the process, and the initial temperature of the simulated annealing is set to T c0, let the initial solution set be Λ, Λ is a three-dimensional matrix, Λ is specifically:

[0026]

[0027] Among them, λ 111 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 121 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the lenght(D)th agricultural waste recycling station, λ 211 represents the decision quantity of the first transport vehicle transporting agricultural waste from the second agricultural waste station to the first agricultural waste recycling station, λ 221 represents the decision quantity of the first transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the second agricultural waste station to the length(D)th agricultural waste recycling station, λ lengt h(N)11 represents the decision quantity of the first transport vehicle transporting from the length(N)th agricultural waste station to the first agricultural waste recycling station, λ lengt h(N)21 represents the decision quantity of the length(N)th transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ lengt h(N)lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the length(N)th agricultural waste station to the length(D)th agricultural waste recycling station, D 112 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 122 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)2 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the length(D)th agricultural waste recycling station, λ 212 represents the decision quantity of the second transport vehicle transporting agricultural waste from the second agricultural waste station to the first agricultural waste recycling station, λ 222 represents the decision quantity of the second transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2leng h(D)2 represents the decision quantity of the second transport vehicle transporting from the second agricultural waste station to the length(D)th agricultural waste recycling station, λ length(N)12represents the decision quantity of the second transport vehicle transporting from the length(N)th agricultural waste station to the first agricultural waste recycling station, λ lengt h(N)22 represents the decision quantity of the second transport vehicle transporting from the length(N)th agricultural waste station to the second agricultural waste recycling station, λ length(N)lengt h(D)2 represents the decision quantity of the second transport vehicle transporting from the length(N)th agricultural waste station to the length(D)th agricultural waste recycling station, λ 11lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 12lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the length(D)th agricultural waste recycling station, λ 21length(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the first agricultural waste recycling station, λ 22length(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2lengt h(D)length(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the length(D)th agricultural waste recycling station, λ lengt h(N)1lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the length(N)th agricultural waste station to the first agricultural waste recycling station, λ lengt h(N)2lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the length(N)th agricultural waste station to the second agricultural waste recycling station, λ lengt h(N)lengt h(D)lengt h(K) It represents the decision quantity of the length(K)th transport vehicle transporting from the length(N)th agricultural waste station to the length(D)th agricultural waste recycling station, and Λ is also a binary matrix, whose value elements are 0 or 1, and Λ is the initial solution Λ 0 And the optimal solution Λ * , let the feasible solution domain be POOL, there are The objective function is determined by the solution set of POOL, according to the initial temperature T of simulated annealing c0 Generate a new solution Λ 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) and further executes according to the following conditions:

[0028]

[0029] Among them, η is the disturbance parameter, which is used to control the degree of disturbance, M max is the perturbation limit, C is the initial deviation value, Rand() is the random function, Rand(-ηM max ,ηM max ) means in (-ηM max ,ηM max ) interval, for the update Tc0, the present invention adopts the cooling rate to cool down, and its differential expression is as follows:

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

[0031] Among them, LOOP is the number of iterations, T ε is the temperature deviation, σ is the cooling rate, and σ>0, T c0 (LOOP+1) represents the initial temperature at LOOP+1 iterations, T c0 (LOOP) represents the initial temperature at the LOOP iteration number. The cooling rate is used to reduce the acceptance range of the simulated annealing algorithm POOL and reduce the jumpiness of the search. Then, the present invention provides rewards or penalties for feasible solutions of each iteration through the equity proof consensus mechanism, sets the total reward pool amount to R, and defines the rules:

[0032] (1) The amount of the prize pool is reduced in each cycle by the folding method. For example, the prize amount for the first iteration is R / 2, the prize amount for the second iteration is R / 4, and the prize amount for the LOOPth iteration is R / (LOOP). 2 , and so on;

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

[0034] (3) The rewards held by the eliminated solutions in each iteration will be used as dividends and divided equally among all feasible solutions in the existing POOL;

[0035] In the above rules, since the computing power system involved in the present invention is an intelligent algorithm module, not a sub-computing power model, and the system involved in the present invention serves the users, it is not affected by the computing power competition of malicious nodes. 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 rewards held by the solutions eliminated in each iteration will be used as dividends and divided equally among all feasible solutions in the existing POOL. In addition, the simulated annealing algorithm, perturbation operator, and cooling rate settings are combined to ensure that the optimal solution will not be suppressed by the reward of the local optimal solution.

[0036] Furthermore, the actuator module automatically executes the corresponding waste treatment program according to the classification results of the intelligent algorithm. According to the classification results, the actuator module will select the corresponding waste treatment program. Different types of waste require different treatment methods, including garbage incineration, organic composting, and recycling. The automated treatment of waste is achieved by controlling automated treatment equipment. These equipment include garbage incinerators, organic composting equipment, and recycling sorting machinery. Controlling these equipment requires ensuring that their working parameters match the characteristics of the waste to ensure an efficient and safe treatment process. In the process of executing waste treatment, the actuator module adjusts the treatment parameters according to actual conditions, and obtains the status information of the treatment equipment in real time by monitoring the waste treatment process. The system can record key data of the treatment process for subsequent analysis and optimization. Considering the safety of the waste treatment process, ensure that the equipment operates within a safe working range, and take necessary safety measures to prevent accidents. In the operation of the waste treatment system, equipment failures or abnormal conditions may occur. The actuator module includes fault detection and corresponding response mechanisms to handle failures in a timely manner and reduce system downtime.

[0037] Furthermore, the user interface provides an interface for users to interact with the system, allowing users to monitor the system operation 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 operation status. The user interface displays the classification results of the waste by the intelligent algorithm module, and presents them in the form of charts, images, and text, so that users can clearly understand the system's accurate classification of different types of waste. The interface provides a historical record of waste treatment, including past treatment results, performance indicators of system operation, and fault records. The user interface includes an interface for manual intervention, allowing users to manually operate or adjust the system, including users manually specifying the waste treatment method. , pause and start the system. The user interface provides setting options for system configuration, allowing users to adjust parameters according to actual needs, including algorithm parameters, working parameters of processing equipment, and alarm thresholds. If the system has abnormal conditions or events that require user attention, the user interface should be able to alarm and provide notifications in real time. The user interface is designed to be simple and intuitive, easy to operate, and the graphical interface presents information in the form of charts and images. The user interface provides remote access function, allowing users to remotely monitor and manage the waste treatment system through the Internet, improving the accessibility and management efficiency of the system. The user interface supports data export function so that users can import the data generated by the system into other analysis tools for more in-depth research and analysis.

[0038] Beneficial effects of the present invention: The present invention can accurately and efficiently classify agricultural waste by integrating advanced sensor systems and intelligent algorithms. Compared with traditional manual classification, the system can more comprehensively and accurately identify various characteristics of waste, improve classification accuracy and efficiency, and through intelligent classification and processing systems, can effectively distinguish organic matter, metals, and plastic recyclable resources in waste, and realize maximum recycling of these resources, which helps to reduce the demand for new resources, reduce production costs, and reduce the adverse effects of waste on the environment. The present invention introduces an automated actuator to achieve automatic treatment of different types of waste. Compared with traditional landfill and incineration methods, the system adopts a more environmentally friendly waste treatment procedure, reduces the emission of harmful gases, and helps protect the atmospheric environment and mitigate climate change. By establishing agricultural waste stations and agricultural waste recycling stations, designated types of agricultural waste are sent to designated locations, further enhancing environmental friendliness. In addition, the intelligent algorithm module provides a solution for the classification and destination of agricultural waste, establishes a dynamic mathematical programming model, and constructs a constraint function to close the feasible solution interval. The present invention defines an operator initial score. In the algorithm field involved in the present invention, cost is considered as the highest priority, and time is considered as the second priority, and the cost operator equilibrium score Z is defined respectively. C and time cost operator equilibrium score Z T, providing a solution for "reducing costs and increasing efficiency". In addition, in order to obtain the optimal solution of the objective function with constraints, the present invention uses the simulated annealing algorithm for preliminary optimization. The innovation lies in that the present invention adds a disturbance term to expand and enrich the feasible solution range, and introduces a cooling rate to cool the initial temperature of the simulated annealing. The cooling rate is used to reduce the acceptance range of the simulated annealing algorithm POOL and reduce the jumpiness of the search. Then, the present invention provides rewards or penalties for feasible solutions of each iteration through the equity proof consensus mechanism, and defines the equity proof consensus mechanism rules. Through the setting of the equity proof consensus mechanism rules, the probability of the optimal solution contained in the initial solution will continue to increase, and the rewards held by the solutions eliminated in each iteration will be used as dividends and divided equally among all feasible solutions in the existing POOL. In addition, the simulated annealing algorithm, the disturbance operator, and the setting of the cooling rate are combined to ensure that the optimal solution will not be suppressed by the reward of the local optimal solution. The user interface provides real-time status monitoring and historical data recording functions, so that the operator can grasp the system operation status at any time, which not only helps to discover and solve problems in a timely manner, but also provides a reliable data basis for subsequent system optimization. The introduction of automated actuators reduces the need for operator intervention and reduces the safety risks caused by human operation. The system's automated processing method helps to improve the safety and stability of the waste treatment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0040] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0041] The present invention is further described in conjunction with the following examples.

[0042] The invention discloses an intelligent classification and treatment system for agricultural wastes. The system comprises 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 physical characteristics and chemical composition information of the wastes. The physical characteristics include the color, shape, texture, temperature, humidity and weight of the wastes. The chemical composition information includes the chemical composition of the wastes. The received raw data contains noise, abnormal values ​​and incompleteness. Therefore, before further processing, the data acquisition and processing module cleans and pre-processes the sensor data and extracts data features. The intelligent algorithm module provides a feasible solution for the classification and treatment of agricultural wastes in the environment. The actuator module selects an automated treatment program according to the classification results, including garbage incineration and organic composting. The user interface provides an interface for users to interact with the system, allowing users to monitor the system operation status and query the 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. Among them, 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 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 the waste data from the sensor system. The received sensor system data contains noise, outliers and incompleteness. Therefore, before further processing, the data needs to be cleaned and preprocessed. This step includes data denoising, outlier processing, and missing value filling to ensure that the subsequent intelligent algorithm can learn and analyze based on high-quality data. After data cleaning and preprocessing, data features are extracted from the waste data. The features include information on color, shape, temperature, humidity, and chemical composition. Feature extraction is to reduce the dimension of the data while retaining information that is meaningful to waste classification.

[0045] Preferably, agricultural waste has been classified at an agricultural waste site through a sensor system and a data acquisition and processing module, and it is assumed that in an agricultural environment, an agricultural waste recycling station is provided to process a certain type of agricultural waste according to the physical properties and chemical composition information of the waste, and the agricultural waste site and the agricultural waste recycling station are connected by a transport vehicle, and the transport vehicle transports the agricultural waste from the agricultural waste site to the agricultural waste recycling station for processing. The intelligent algorithm module of the present invention provides a solution, which is as follows:

[0046] In the field involved in the present invention, a transport vehicle transports agricultural waste from an agricultural waste station to an agricultural waste recycling station for treatment, and the following mathematical model is established:

[0047]

[0048] Among them, F is the objective function, λ ijk is the decision quantity, indicating whether the transport vehicle k is transported from the target point i to j, as follows:

[0049]

[0050] C k represents the cost of using transport vehicle k, K is the set of transport vehicles, N is the set of agricultural waste sites, and satisfies N = {0,1,…,n}, n is the total number of waste sites, i, j, k are index numbers, index number i∈N, if i = 0, it means the shipping point of the transport vehicle, D is the agricultural waste recycling station, and there is a restriction: length(D)≤length(N) means that the number of agricultural waste recycling stations does not exceed the number of agricultural waste sites, where length() represents the length of the set, and D∩N≠Φ, indicating that an agricultural waste site can be an agricultural waste recycling station, where Φ represents the empty set, C t Indicates the time cost, represents the load of agricultural waste after the transport vehicle k picks up the agricultural waste at the target point i, V represents the load after the transport vehicle k unloads the agricultural waste at the target point j. In the present invention, the agricultural waste recycling station only processes the waste that can be recycled at the station. Therefore, the number of unloadings of the transport vehicle k at each agricultural waste recycling station is limited. k represents the speed of transport vehicle k, f k represents the variable cost of the transport vehicle, including the tolls, repair costs and maintenance costs of the transport vehicle, ∫V k dt represents the actual travel distance of the transport vehicle k and contains the following constraints:

[0051]

[0052] in, This means that each delivery vehicle can only be used once. This means that after each transport vehicle goes to an agricultural waste station to load, it can only go to one agricultural waste recycling station to unload. Indicates that the driving path of the transport vehicle is an undirected closed Hamiltonian path, It means that the remaining capacity of transport vehicle k does not exceed the rated capacity Q.

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

[0054]

[0055] The optimal solution is defined, and the initial score of the operator is defined. In the algorithm field involved in the present invention, cost is considered as the highest priority, and time is considered as the second priority. The cost operator equilibrium score Z is defined respectively. C and time cost operator equilibrium score Z T as follows:

[0056]

[0057]

[0058] Among them, E() is the mathematical expectation, T ik is the time required for transport vehicle k to reach agricultural waste loading station i, T jk is the time required for transport vehicle k to reach agricultural waste recycling station j for unloading, is the cost weight operator for transporting vehicle k, is the time cost weight operator, f k is the variable cost weight operator for transporting vehicle k.

[0059] Preferably, the simulated annealing algorithm is used for preliminary optimization, and the initial temperature of the simulated annealing is set to T c0 , let the initial solution set be Λ, Λ is a three-dimensional matrix, Λ is specifically:

[0060]

[0061] Among them, λ 111 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 121 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the lenght(D)th agricultural waste recycling station, λ211 represents the decision quantity of the first transport vehicle transporting agricultural waste from the second agricultural waste station to the first agricultural waste recycling station, λ 221 represents the decision quantity of the first transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the second agricultural waste station to the length(D)th agricultural waste recycling station, λ lengt h(N)11 represents the decision quantity of the first transport vehicle transporting from the length(N)th agricultural waste station to the first agricultural waste recycling station, λ lengt h(N)21 represents the decision quantity of the length(N)th transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ lengt h(N)lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the length(N)th agricultural waste station to the length(D)th agricultural waste recycling station, D 112 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 122 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)2 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the length(D)th agricultural waste recycling station, λ 212 represents the decision quantity of the second transport vehicle transporting agricultural waste from the second agricultural waste station to the first agricultural waste recycling station, λ 222 represents the decision quantity of the second transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2leng h(D)2 represents the decision quantity of the second transport vehicle transporting from the second agricultural waste station to the length(D)th agricultural waste recycling station, λ length(N)12 represents the decision quantity of the second transport vehicle transporting from the length(N)th agricultural waste station to the first agricultural waste recycling station, λ lengt h(N)22 represents the decision quantity of the second transport vehicle transporting from the length(N)th agricultural waste station to the second agricultural waste recycling station, λ length(N)lengt h(D)2 represents the decision quantity of the second transport vehicle transporting from the length(N)th agricultural waste station to the length(D)th agricultural waste recycling station, λ 11lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 12lengt h(K)represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the length(D)th agricultural waste recycling station, λ 21length(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the first agricultural waste recycling station, λ 22length(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2lengt h(D)length(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the length(D)th agricultural waste recycling station, λ lengt h(N)1lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the length(N)th agricultural waste station to the first agricultural waste recycling station, λ lengt h(N)2lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the length(N)th agricultural waste station to the second agricultural waste recycling station, λ lengt h(N)lengt h(D)lengt h(K) It represents the decision quantity of the length(K)th transport vehicle transporting from the length(N)th agricultural waste station to the length(D)th agricultural waste recycling station, and Λ is also a binary matrix, whose value elements are 0 or 1, and Λ is the initial solution Λ 0 And the optimal solution Λ * , let the feasible solution domain be POOL, there are The objective function is determined by the solution set of POOL, according to the initial temperature T of simulated annealing c0 Generate a new solution Λ 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 ) and further executes according to the following conditions:

[0062]

[0063] Among them, η is the disturbance parameter, which is used to control the degree of disturbance, M max is the perturbation limit, C is the initial deviation value, Rand() is the random function, Rand(-ηM max ,ηM max ) means in (-ηM max ,ηM max) interval, for the update Tc0, the present invention adopts the cooling rate to cool down, and its differential expression is as follows:

[0064] T c0 (LOOP+1)-T c0 (LOOP)=-σ(T c0 (LOOP+1)-T ε )

[0065] Among them, LOOP is the number of iterations, T ε is the temperature deviation, σ is the cooling rate, and σ>0, T c0 (LOOP+1) represents the initial temperature at LOOP+1 iterations, T c0 (LOOP) represents the initial temperature at the LOOP iteration number. The cooling rate is used to reduce the acceptance range of the simulated annealing algorithm POOL and reduce the jumpiness of the search. Then, the present invention provides rewards or penalties for feasible solutions of each iteration through the equity proof consensus mechanism, sets the total reward pool amount to R, and defines the rules:

[0066] (1) The amount of the prize pool is reduced in each cycle by the folding method. For example, the prize amount for the first iteration is R / 2, the prize amount for the second iteration is R / 4, and the prize amount for the LOOPth iteration is R / (LOOP). 2 , and so on;

[0067] (2) In each iteration, all feasible solutions in POOL share the current prize equally;

[0068] (3) The rewards held by the eliminated solutions in each iteration will be used as dividends and divided equally among all feasible solutions in the existing POOL;

[0069] In the above rules, since the computing power system involved in the present invention is an intelligent algorithm module, not a sub-computing power model, and the system involved in the present invention serves the users, it is not affected by the computing power competition of malicious nodes. 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 rewards held by the solutions eliminated in each iteration will be used as dividends and divided equally among all feasible solutions in the existing POOL. In addition, the simulated annealing algorithm, perturbation operator, and cooling rate settings are combined to ensure that the optimal solution will not be suppressed by the reward of the local optimal solution.

[0070] Specifically, the actuator module automatically executes the corresponding waste treatment program according to the classification results of the intelligent algorithm. According to the classification results, the actuator module will select the corresponding waste treatment program. Different types of waste require different treatment methods, including garbage incineration, organic composting, and recycling. The automated treatment of waste is achieved by controlling automated treatment equipment. These equipment include garbage incinerators, organic composting equipment, and recycling sorting machinery. Controlling these equipment requires ensuring that their working parameters match the characteristics of the waste to ensure an efficient and safe treatment process. In the process of executing waste treatment, the actuator module adjusts the treatment parameters according to actual conditions, and obtains the status information of the treatment equipment in real time by monitoring the waste treatment process. The system can record key data of the treatment process for subsequent analysis and optimization. Considering the safety of the waste treatment process, ensure that the equipment operates within a safe working range, and take necessary safety measures to prevent accidents. In the operation of the waste treatment system, equipment failures or abnormal conditions may occur. The actuator module contains fault detection and corresponding response mechanisms to handle failures in a timely manner and reduce system downtime.

[0071] Specifically, the user interface provides an interface for users to interact with the system, allowing users to monitor the system operation 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 operation status. The user interface displays the classification results of the waste by the intelligent algorithm module, and presents them in the form of charts, images, and text, so that users can clearly understand the system's accurate classification of different types of waste. The interface provides a historical record of waste treatment, including past treatment results, performance indicators of system operation, and fault records. The user interface includes an interface for manual intervention, allowing users to manually operate or adjust the system, including users manually specifying the waste treatment method. The system can be paused and started. The user interface provides system configuration setting options, allowing users to adjust parameters according to actual needs, including algorithm parameters, processing equipment working parameters, and alarm thresholds. If the system has abnormal conditions or events that require user attention, the user interface should be able to provide real-time alarms and notifications. The user interface is simple and intuitive, easy to operate, and the graphical interface presents information in the form of charts and images. The user interface provides remote access functions, allowing users to remotely monitor and manage waste treatment systems through the Internet, thereby improving the accessibility and management efficiency of the system. The user interface supports data export functions so that users can import the data generated by the system into other analysis tools for more in-depth research and analysis.

[0072] Beneficial effects of the present invention: The present invention can accurately and efficiently classify agricultural waste by integrating advanced sensor systems and intelligent algorithms. Compared with traditional manual classification, the system can more comprehensively and accurately identify various characteristics of waste, improve classification accuracy and efficiency, and through intelligent classification and processing systems, can effectively distinguish organic matter, metals, and plastic recyclable resources in waste, and realize maximum recycling of these resources, which helps to reduce the demand for new resources, reduce production costs, and reduce the adverse effects of waste on the environment. The present invention introduces an automated actuator to achieve automatic treatment of different types of waste. Compared with traditional landfill and incineration methods, the system adopts a more environmentally friendly waste treatment procedure, reduces the emission of harmful gases, and helps protect the atmospheric environment and mitigate climate change. By establishing agricultural waste stations and agricultural waste recycling stations, designated types of agricultural waste are sent to designated locations, further enhancing environmental friendliness. In addition, the intelligent algorithm module provides a solution for the classification and destination of agricultural waste, establishes a dynamic mathematical programming model, and constructs a constraint function to close the feasible solution interval. The present invention defines an operator initial score. In the algorithm field involved in the present invention, cost is considered as the highest priority, and time is considered as the second priority, and the cost operator equilibrium score Z is defined respectively. C and time cost operator equilibrium score Z T , providing a solution for "reducing costs and increasing efficiency". In addition, in order to obtain the optimal solution of the objective function with constraints, the present invention uses the simulated annealing algorithm for preliminary optimization. The innovation lies in that the present invention adds a disturbance term to expand and enrich the feasible solution range, and introduces a cooling rate to cool the initial temperature of the simulated annealing. The cooling rate is used to reduce the acceptance range of the simulated annealing algorithm POOL and reduce the jumpiness of the search. Then, the present invention provides rewards or penalties for feasible solutions of each iteration through the equity proof consensus mechanism, and defines the equity proof consensus mechanism rules. Through the setting of the equity proof consensus mechanism rules, the probability of the optimal solution contained in the initial solution will continue to increase, and the rewards held by the solutions eliminated in each iteration will be used as dividends and divided equally among all feasible solutions in the existing POOL. In addition, the simulated annealing algorithm, the disturbance operator, and the setting of the cooling rate are combined to ensure that the optimal solution will not be suppressed by the reward of the local optimal solution. The user interface provides real-time status monitoring and historical data recording functions, so that the operator can grasp the system operation status at any time, which not only helps to discover and solve problems in a timely manner, but also provides a reliable data basis for subsequent system optimization. The introduction of automated actuators reduces the need for operator intervention and reduces the safety risks caused by human operation. The system's automated processing method helps to improve the safety and stability of the waste treatment process.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified without departing from the essence and scope of the technical solution of the present invention.

Claims

1. An intelligent classification and treatment system for agricultural waste, the system 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 physical properties and chemical composition information of the waste, the physical properties include the color, shape, texture, temperature, humidity, weight of the waste, and the chemical composition information includes the chemical composition of the waste; the received raw data contains noise, abnormal values ​​and incompleteness, so before further processing, the data acquisition and processing module cleans, pre-processes the sensor data, and extracts data features; the intelligent algorithm module provides a feasible solution for the classification and treatment of agricultural waste in the environment, the actuator module selects an automated processing program according to the classification results, including garbage incineration and organic composting, and the user interface provides an interface for users to interact with the system, allowing users to monitor the system operation status and query processing results.

2. According to claim 1, an intelligent classification and treatment system for agricultural waste is 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. Among them, 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 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.

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

4. According to claim 1, an intelligent classification and treatment system for agricultural waste is characterized in that: Agricultural waste has been classified at the agricultural waste site through the sensor system, data collection and processing module, and it is assumed that in the agricultural environment background, there is an agricultural waste recycling station, and a certain type of agricultural waste is processed according to the physical characteristics and chemical composition information of the waste. The agricultural waste site and the agricultural waste recycling station are connected through a transport vehicle, and the transport vehicle transports the agricultural waste from the agricultural waste site to the agricultural waste recycling station for processing. The intelligent algorithm module of the present invention provides a solution, which is as follows: In the field involved in the present invention, a transport vehicle transports agricultural waste from an agricultural waste station to an agricultural waste recycling station for treatment, and the following mathematical model is established: Among them, F is the objective function, λ ijk is the decision quantity, indicating whether the transport vehicle k is transported from the target point i to j, as follows: C k represents the cost of using transport vehicle k, K is the set of transport vehicles, N is the set of agricultural waste sites, and satisfies N = {0,1,…,n}, n is the total number of waste sites, i, j, k are index numbers, index number i∈N, if i = 0, it means the shipping point of the transport vehicle, D is the agricultural waste recycling station, and there is a restriction: length(D)≤length(N) means that the number of agricultural waste recycling stations does not exceed the number of agricultural waste sites, where length() represents the length of the set, and D∩N≠Φ, indicating that an agricultural waste site can be an agricultural waste recycling station, where Φ represents the empty set, C t Indicates the time cost, represents the load of agricultural waste after the transport vehicle k picks up the agricultural waste at the target point i, V represents the load after the transport vehicle k unloads the agricultural waste at the target point j. In the present invention, the agricultural waste recycling station only processes the waste that can be recycled at the station. Therefore, the number of unloadings of the transport vehicle k at each agricultural waste recycling station is limited. k represents the speed of transport vehicle k, f k represents the variable cost of the transport vehicle, including the tolls, repair costs and maintenance costs of the transport vehicle, ∫V k dt represents the actual travel distance of the transport vehicle k and contains the following constraints: in, This means that each delivery vehicle can only be used once. This means that after each transport vehicle goes to an agricultural waste station to load, it can only go to one agricultural waste recycling station to unload. Indicates that the driving path of the transport vehicle is an undirected closed Hamiltonian path, It means that the remaining capacity of transport vehicle k does not exceed the rated capacity Q.

5. According to claim 4, an intelligent classification and treatment system for agricultural waste is characterized in that: In order to obtain the objective function with constraints The optimal solution is defined, and the initial score of the operator is defined. In the algorithm field involved in the present invention, cost is considered as the highest priority, and time is considered as the second priority. The cost operator equilibrium score Z is defined respectively. C and time cost operator equilibrium score Z T as follows: Among them, E() is the mathematical expectation, T ik is the time required for transport vehicle k to reach agricultural waste loading station i, T jk is the time required for transport vehicle k to reach agricultural waste recycling station j for unloading, is the cost weight operator for transporting vehicle k, is the time cost weight operator, f k is the variable cost weight operator for transporting vehicle k.

6. The intelligent classification and treatment system for agricultural waste according to claim 5 is characterized in that: The simulated annealing algorithm is used to preliminarily optimize the process, and the initial temperature of the simulated annealing is set to T. c0 , let the initial solution set be Λ, Λ is a three-dimensional matrix, Λ is specifically: Among them, λ 111 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 121 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the first agricultural waste station to the length(D)th agricultural waste recycling station, λ 211 represents the decision quantity of the first transport vehicle transporting agricultural waste from the second agricultural waste station to the first agricultural waste recycling station, λ 221 represents the decision quantity of the first transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the second agricultural waste station to the length(D)th agricultural waste recycling station, λ lengt h(N)11 represents the decision quantity of the first transport vehicle transporting from the length(N)th agricultural waste station to the first agricultural waste recycling station, λ lengt h(N)21 represents the decision quantity of the length(N)th transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ lengt h(N)lengt h(D)1 represents the decision quantity of the first transport vehicle transporting from the lengt(N)th agricultural waste station to the lengt(D)th agricultural waste recycling station, λ 112 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 122 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)2 represents the decision quantity of the second transport vehicle transporting from the first agricultural waste station to the length(D)th agricultural waste recycling station, λ 212 represents the decision quantity of the second transport vehicle transporting agricultural waste from the second agricultural waste station to the first agricultural waste recycling station, λ 222 represents the decision quantity of the second transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2length(D)2 represents the decision quantity of the second transport vehicle transporting from the second agricultural waste station to the lengt(D)th agricultural waste recycling station, λ length(N)12 represents the decision quantity of the second transport vehicle transporting from the lengt(N)th agricultural waste station to the first agricultural waste recycling station, λ length(N)22 represents the decision quantity of the second transport vehicle transporting from the length(N)th agricultural waste station to the second agricultural waste recycling station, λ length(N)length(D)2 represents the decision quantity of the second transport vehicle transporting from the length(N)th agricultural waste station to the length(D)th agricultural waste recycling station, λ 11lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the first agricultural waste recycling station, λ 12lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the second agricultural waste recycling station, λ 1lengt h(D)lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the first agricultural waste station to the length(D)th agricultural waste recycling station, λ 21lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the first agricultural waste recycling station, λ 22lenght(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the second agricultural waste recycling station, λ 2length(D)length(K) represents the decision quantity of the length(K)th transport vehicle transporting from the second agricultural waste station to the length(D)th agricultural waste recycling station, λ lengt h(N)1lengt h(K) represents the decision quantity of the length(K)th transport vehicle transporting from the length(N)th agricultural waste station to the first agricultural waste recycling station, λ lengt h(N)2length(K) represents the decision quantity of the length(K)th transport vehicle transporting from the length(N)th agricultural waste station to the second agricultural waste recycling station, λ length(N)length(D)length(K) It represents the decision quantity of the lenth(K)th transport vehicle transporting from the length(N)th agricultural waste station to the length(D)th agricultural waste recycling station, and Λ is also a binary matrix, whose value elements are 0 or 1, and Λ is the initial solution Λ 0 And the optimal solution Λ * , let the feasible solution domain be POOL, and POOL={Λ 0 }, the objective function is determined by the solution set of POOL, according to the simulated annealing initial temperature T c0 Generate a new solution Λ 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 ) and further executes according to the following conditions: Among them, η is the disturbance parameter, which is used to control the degree of disturbance, M max is the perturbation limit, C is the initial deviation value, Rand() is the random function, Eand(-ηM max ,ηM max ) means in (-ηM max ,ηM max ) interval, for the update T c0 , the present invention adopts the cooling rate to reduce the temperature, and its differential expression is as follows: T c0 (LOOP+1)-T c0 (LOOP)=-σ(T c0 (LOOP+1)-t ε ) Among them, LOOP is the number of iterations, t ε is the temperature deviation, σ is the cooling rate, and σ>0, T c0 (LOOP+1) represents the initial temperature at LOOP+1 iterations, T c0 (LOOP) represents the initial temperature at the LOOP iteration number. The cooling rate is used to reduce the acceptance range of the simulated annealing algorithm POOL and reduce the jumpiness of the search. Then, the present invention provides rewards or penalties for feasible solutions of each iteration through the equity proof consensus mechanism, sets the total reward pool amount to R, and defines the rules: (1) The amount of the prize pool is reduced in each cycle by the folding method. For example, the prize amount for the first iteration is R / 2, the prize amount for the second iteration is R / 4, and the prize amount for the LOOPth iteration is R / (LOOP). 2 , and so on; (2) In each iteration, all feasible solutions in POOL share the current prize equally; (3) The rewards held by the eliminated solutions in each iteration will be used as dividends and divided equally among all feasible solutions in the existing POOL; In the above rules, since the computing power system involved in the present invention is an intelligent algorithm module, not a sub-computing power model, and the system involved in the present invention serves the users, it is not affected by the computing power competition of malicious nodes. 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 rewards held by the solutions eliminated in each iteration will be used as dividends and divided equally among all feasible solutions in the existing POOL. In addition, the simulated annealing algorithm, perturbation operator, and cooling rate settings are combined to ensure that the optimal solution will not be suppressed by the reward of the local optimal solution.

7. The intelligent classification and treatment system for agricultural waste according to claim 1 is characterized in that: The actuator module automatically executes the corresponding waste treatment program according to the classification results of the intelligent algorithm. According to the classification results, the actuator module will select the corresponding 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. These equipment include waste incinerators, organic composting equipment, and recycling sorting machinery. Controlling these equipment requires ensuring that their working parameters match the characteristics of the waste to ensure an efficient and safe treatment process. During the waste treatment process, the actuator module adjusts the treatment parameters according to the actual situation. By monitoring the waste treatment process, the status information of the treatment equipment is obtained in real time. The system can record the 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 abnormal conditions may occur. The actuator module contains fault detection and corresponding response mechanisms to handle faults in a timely manner and reduce system downtime.

8. The intelligent classification and treatment system for agricultural waste according to claim 1 is characterized in that: The user interface provides an interface for users to interact with the system, allowing users to monitor the system operation 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 operation status. The user interface displays the classification results of waste by the intelligent algorithm module, and presents them in the form of charts, images, and text, so that users can clearly understand the system's accurate classification of different types of waste. The interface provides a historical record of waste treatment, including past treatment results, system operation performance indicators, and fault records. The user interface includes an interface for manual intervention, allowing users to manually operate or adjust the system, including users manually specifying the waste treatment method and performing The system pause and start operations, the user interface provides system configuration setting options, allowing users to adjust parameters according to actual needs, including algorithm parameters, processing equipment working parameters, and alarm thresholds. If the system has abnormal conditions or events that require user attention, the user interface should be able to alarm and provide notifications in real time. The user interface is designed to be simple and intuitive, easy to operate, and the graphical interface presents information in the form of charts and images. The user interface provides remote access function, allowing users to remotely monitor and manage the waste treatment system through the Internet, improving the accessibility and management efficiency of the system. The user interface supports data export function so that users can import the data generated by the system into other analysis tools for more in-depth research and analysis.

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