Electrostatic sorting system, method and device based on Internet of Things and artificial intelligence

Abstract

The present application provides an electrostatic sorting system, method, and device based on the Internet of Things and artificial intelligence, relating to the field of waste recycling and resource regeneration technology. The system includes: a feeding system, a receiving system, a charging processing module, an electric field control module, an artificial intelligence optimization system, and an Internet of Things system; the Internet of Things system is used to collect characteristic data of materials and operating data of the electrostatic sorting system and perform data processing and analysis; the artificial intelligence optimization system is used to generate optimal electric field adjustment instructions and charging control strategies based on the received characteristic data and operating data; the charging processing module is used to adjust the charging mode and charging parameters of the material according to the charging control strategy; the electric field control module is used to optimize the electric field distribution according to the electric field adjustment instructions; and the receiving system is used to receive materials after electrostatic sorting. The present application can significantly improve the system's responsiveness, sorting accuracy, and energy efficiency, and realize intelligent optimization and adaptive adjustment of the electrostatic sorting process.

Description

Technical Field

[0001] The present application relates to the field of waste recycling and resource regeneration technology, and in particular to an electrostatic sorting system, method and device based on the Internet of Things and artificial intelligence. Background Art

[0002] With the development of waste recycling and resource regeneration industries, electrostatic separation technology has been widely used to process complex mixed materials. However, conventional electrostatic separation equipment in the existing technology usually has the following problems that need to be solved:

[0003] 1. Fixed power output and low energy efficiency: The change in electric field intensity during the electrical sorting process requires precise control. Traditional equipment lacks an intelligent adjustment system, resulting in unnecessary energy waste.

[0004] 2. Lack of real-time adjustment function: The electric field configuration and charging mode of traditional equipment are usually fixed and cannot be optimized in real time according to material changes.

[0005] 3. Frequent equipment maintenance: The operation and maintenance of traditional equipment usually rely on manual labor and lack automated and intelligent diagnosis and maintenance mechanisms.

[0006] At present, with the deep integration of AI and Internet of Things technologies in intelligent manufacturing and environmental protection equipment, intelligent sorting technology that uses AI to identify and optimize the control of electrostatic parameters and uses IoT to perceive the operating status of equipment has gradually become an important research direction for improving resource recovery rate and system efficiency. Summary of the Invention

[0007] In response to the problems in the prior art, the embodiments of the present application provide an electrostatic sorting system, method and device based on the Internet of Things and artificial intelligence, which can solve the problems in the prior art that the traditional system has a fixed electric field output, low energy efficiency, and cannot be adjusted in real time according to material changes; the live processing method is fixed, resulting in poor sorting adaptability; there is a lack of abnormality detection and energy efficiency optimization mechanism, and insufficient operational stability and energy consumption control.

[0008] In a first aspect, the present application provides an electrostatic sorting system based on the Internet of Things and artificial intelligence, comprising: a feeding system, a receiving system, a charged processing module, an electric field control module, an artificial intelligence optimization system, and an Internet of Things system;

[0009] The feeding system is connected to the charged processing module and is used to evenly feed the material into the electrostatic separation system;

[0010] The Internet of Things system is connected to the artificial intelligence optimization system via network communication, and is used to collect characteristic data of the material and operating data of the electrostatic separation system and perform data processing and analysis on the characteristic data and the operating data;

[0011] The artificial intelligence optimization system is used to generate optimal electric field adjustment instructions and electrification control strategies based on the received characteristic data and the operating data;

[0012] The charging processing module is connected to the electric field control module and is connected to the artificial intelligence optimization system via a communication bus, and is used to adjust the charging mode and charging parameters of the material according to the received charging control strategy;

[0013] The electric field control module is connected to the artificial intelligence optimization system via a communication bus and is used to optimize the electric field distribution according to the received electric field adjustment instructions;

[0014] The material receiving system is connected to the electric field control module and is used to receive materials after electrostatic separation.

[0015] Furthermore, the Internet of Things system includes a sensor module, an Internet of Things communication module and a cloud platform;

[0016] The sensor module is used to collect characteristic data of the material and operating data of the electrostatic separation system;

[0017] The Internet of Things communication module is used to receive the feature data and the operating data and send them to the artificial intelligence optimization system and the cloud platform;

[0018] The cloud platform is used to process and analyze the received feature data and operation data.

[0019] Furthermore, the artificial intelligence optimization system is specifically used to:

[0020] receiving the characteristic data from the Internet of Things system;

[0021] Determining a charging mode based on the characteristic data and a pre-trained decision tree model;

[0022] generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode;

[0023] A charging control strategy is generated using the charging mode and the charging parameters and sent to the charging processing module.

[0024] Furthermore, the artificial intelligence optimization system is specifically used to:

[0025] receiving the characteristic data and the operation data from the Internet of Things system;

[0026] Performing simulation modeling on the electric field of the electrostatic separation system according to the operating data to obtain a numerical model of the electric field distribution;

[0027] generating an electric field distribution state according to the electric field distribution numerical model;

[0028] generating electric field distribution control parameters according to the characteristic data, the electric field distribution state, and a pre-built reinforcement learning agent that calls the electric field distribution numerical model;

[0029] An electric field adjustment instruction is generated using the electric field distribution control parameter and sent to the electric field control module.

[0030] Furthermore, it also includes: a security intelligent management system;

[0031] The safety intelligent management system is connected to the Internet of Things system and the artificial intelligence optimization system, and is used to identify the abnormal state and failure trend of the electrostatic sorting system based on the received operation data, and send the identification results to the artificial intelligence optimization system to adjust the operation strategy.

[0032] Furthermore, the security intelligent management system is specifically used to:

[0033] receiving the operating data from the Internet of Things system;

[0034] Identifying an abnormal state of the electrostatic separation system based on the operating data and a pre-trained anomaly detection model; the anomaly detection model is obtained by unsupervised training of historical operating data of the electrostatic separation system under normal operating conditions;

[0035] Identifying the failure trend of the electrostatic separation system based on the operating data and a pre-trained fault prediction model; the fault prediction model is obtained by supervised training of historical operating data and fault records of the electrostatic separation system in different operating cycles;

[0036] The recognition results are sent to the artificial intelligence optimization system to adjust the operation strategy.

[0037] Furthermore, it also includes: an energy efficiency optimization system;

[0038] The energy efficiency optimization system is connected to the Internet of Things system and the electric field control module, and is used to generate an optimization control strategy based on the received operating data to perform adaptive optimization and regulation on the electric field power.

[0039] Furthermore, the energy efficiency optimization system is specifically used to:

[0040] receiving the operating data from the Internet of Things system;

[0041] generating an optimized control strategy through dynamic analysis based on the operating data;

[0042] The optimization control strategy is sent to the electric field control module to perform adaptive optimization adjustment on the electric field power.

[0043] In a second aspect, the present application provides an electrostatic separation method based on the Internet of Things and artificial intelligence, which is applied to the electrostatic separation system based on the Internet of Things and artificial intelligence described in any of the above embodiments, including:

[0044] receiving the characteristic data from the Internet of Things system;

[0045] Determining a charging mode based on the characteristic data and a pre-trained decision tree model;

[0046] generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode;

[0047] A charging control strategy is generated using the charging mode and the charging parameters and sent to the charging processing module.

[0048] Furthermore, it also includes:

[0049] receiving the characteristic data and the operation data from the Internet of Things system;

[0050] Performing simulation modeling on the electric field of the electrostatic separation system according to the operating data to obtain a numerical model of the electric field distribution;

[0051] generating an electric field distribution state according to the electric field distribution numerical model;

[0052] generating electric field distribution control parameters according to the characteristic data, the electric field distribution state, and a pre-built reinforcement learning agent that calls the electric field distribution numerical model;

[0053] An electric field adjustment instruction is generated using the electric field distribution control parameter and sent to the electric field control module.

[0054] In a third aspect, the present application provides an electrostatic separation device based on the Internet of Things and artificial intelligence, which is applied to the electrostatic separation system based on the Internet of Things and artificial intelligence described in any of the above embodiments, including:

[0055] A first data receiving unit, configured to receive the feature data from the Internet of Things system;

[0056] a charging mode determining unit, configured to determine a charging mode based on the characteristic data and a pre-trained decision tree model;

[0057] a charged parameter generating unit, configured to generate a charged parameter according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charged mode;

[0058] The charging control strategy output unit is used to generate a charging control strategy using the charging mode and the charging parameters and send the strategy to the charging processing module.

[0059] Furthermore, it also includes:

[0060] a second data receiving unit, configured to receive the feature data and the operation data from the Internet of Things system;

[0061] A model building unit, configured to simulate and model the electric field of the electrostatic separation system according to the operating data to obtain a numerical model of the electric field distribution;

[0062] An electric field distribution state generating unit, configured to generate an electric field distribution state according to the electric field distribution numerical model;

[0063] a control parameter generating unit, configured to generate electric field distribution control parameters according to the characteristic data, the electric field distribution state, and a pre-built reinforcement learning agent that calls the electric field distribution numerical model;

[0064] An electric field adjustment instruction output unit is used to generate an electric field adjustment instruction using the electric field distribution control parameter and send the instruction to the electric field control module.

[0065] In a fourth aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the electrostatic sorting method based on the Internet of Things and artificial intelligence described in any of the above embodiments is implemented.

[0066] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the electrostatic sorting method based on the Internet of Things and artificial intelligence described in any of the above embodiments.

[0067] In a sixth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the electrostatic sorting method based on the Internet of Things and artificial intelligence described in any of the above embodiments.

[0068] The present application provides an electrostatic sorting system, method and device based on the Internet of Things and artificial intelligence, which includes: a feeding system, a material receiving system, a charging processing module, an electric field control module, an artificial intelligence optimization system and an Internet of Things system; the feeding system is connected to the charging processing module to uniformly feed the material into the electrostatic sorting system; the Internet of Things system is connected to the artificial intelligence optimization system through network communication to collect characteristic data of the material and operating data of the electrostatic sorting system and perform data processing and analysis on the characteristic data and the operating data; the artificial intelligence optimization system is used to generate optimal electric field adjustment instructions and charging control strategies based on the received characteristic data and the operating data; the charging processing module is connected to the electric field control module and is connected to the artificial intelligence optimization system through a communication bus to adjust the charging mode and charging parameters of the material according to the received charging control strategy; the electric field control module is connected to the artificial intelligence optimization system through a communication bus to optimize the electric field distribution according to the received electric field adjustment instructions; the material receiving system is connected to the electric field control module to receive the material after electrostatic sorting. This application realizes intelligent optimization and adaptive adjustment of electrostatic sorting, improving sorting accuracy and system stability.

[0069] Among them, the feeding system realizes uniform material transportation, ensuring the stability and sorting accuracy of the subsequent charging and sorting process; the Internet of Things system realizes real-time perception and data collection of material characteristics and equipment operating status, providing data support for intelligent optimization; the artificial intelligence optimization system realizes adaptive optimization based on material characteristics and equipment status, automatically generates control strategies for electric fields and charging parameters, and improves the intelligence level and energy efficiency level of the equipment; the charging processing module realizes intelligent selection and adjustment of charging modes and charging parameters for different materials, and improves the uniformity of material charging and subsequent sorting efficiency; the electric field control module realizes dynamic optimization of electrode voltage, electric field strength and distribution, ensuring that the electric field adapts to the material status and reduces energy consumption; the collecting system realizes efficient classification and collection of sorted materials, ensuring the continuity and practicality of the entire electrostatic sorting process.

[0070] By incorporating real-time IoT sensing and AI-powered dynamic optimization and control, this invention achieves intelligent optimization and adaptive regulation of the electrostatic sorting process, significantly improving sorting accuracy, energy efficiency, and system stability. Unlike traditional electrostatic sorting systems that rely on manual adjustments or single-sensor control, this invention establishes an intelligent closed-loop optimization system of "perception-decision-execution-feedback," enhancing the system's real-time responsiveness and adaptability to diverse material handling, possessing significant engineering application value and potential for widespread adoption. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0072] Figure 1 This is a structural diagram of an electrostatic separation system based on the Internet of Things and artificial intelligence provided by an embodiment of the present application;

[0073] Figure 2 This is a structural diagram of an electrostatic separation system based on the Internet of Things and artificial intelligence provided by an embodiment of the present application;

[0074] Figure 3 This is a structural diagram of an electrostatic separation system based on the Internet of Things and artificial intelligence provided by an embodiment of the present application;

[0075] Figure 4 This is a structural diagram of an electrostatic separation system based on the Internet of Things and artificial intelligence provided by an embodiment of the present application;

[0076] Figure 5 This is a flow chart of an electrostatic separation method based on the Internet of Things and artificial intelligence provided by one embodiment of the present application;

[0077] Figure 6 This is a flow chart of an electrostatic separation method based on the Internet of Things and artificial intelligence provided by one embodiment of the present application;

[0078] Figure 7 This is a schematic structural diagram of an electrostatic separation device based on the Internet of Things and artificial intelligence provided by one embodiment of the present application;

[0079] Figure 8 This is a schematic structural diagram of an electrostatic separation device based on the Internet of Things and artificial intelligence provided by one embodiment of the present application;

[0080] Figure 9 This is a schematic block diagram of the system structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0081] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clearly understood, the embodiments of the present application are further described in detail below with reference to the accompanying drawings. The illustrative embodiments of the present application and their descriptions are used to explain the present application but are not intended to limit the present application. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application may be combined with each other in any manner.

[0082] Figure 1This is a structural diagram of an electrostatic separation system based on the Internet of Things and artificial intelligence provided by an embodiment of the present application. Figure 1 As shown, the electrostatic separation system based on the Internet of Things and artificial intelligence provided by this application includes: a feeding system 101, a receiving system 104, a charged processing module 102, an electric field control module 103, an artificial intelligence optimization system 106 and an Internet of Things system 105;

[0083] The feeding system 101 is connected to the charged processing module 102 and is used to evenly feed the material into the electrostatic separation system;

[0084] The Internet of Things system 105 is connected to the artificial intelligence optimization system 106 via network communication, and is used to collect characteristic data of the material and operating data of the electrostatic separation system and perform data processing and analysis on the characteristic data and the operating data;

[0085] The artificial intelligence optimization system 106 is used to generate the optimal electric field adjustment instructions and electrification control strategy based on the received characteristic data and the operating data;

[0086] The charging processing module 102 is connected to the electric field control module 103 and connected to the artificial intelligence optimization system 106 via a communication bus, and is used to adjust the charging mode and charging parameters of the material according to the received charging control strategy;

[0087] The electric field control module 103 is connected to the artificial intelligence optimization system 106 via a communication bus, and is used to optimize the electric field distribution according to the received electric field adjustment instructions;

[0088] The material receiving system 104 is connected to the electric field control module 103 and is used to receive materials after electrostatic separation.

[0089] Specifically, the feeding system 101 evenly feeds the mixed material to be sorted into the electrostatic sorting system, ensuring the stability of the subsequent charging treatment and electric field sorting process, and controlling the feeding speed and uniformity.

[0090] The charging module 102, located between the feed system 101 and the electric field control module 103, pre-charges the material using methods such as contact charging, friction charging, or corona discharge, depending on its characteristics, ensuring efficient and accurate sorting. This module dynamically adjusts the charging mode and parameters based on the control strategy it receives.

[0091] The electric field control module 103 receives electric field adjustment instructions from the artificial intelligence optimization system 106 and adjusts the voltage, electric field strength or polarity configuration between the electrodes to optimize the electric field distribution, improve the sorting effect and reduce unnecessary energy consumption.

[0092] The artificial intelligence optimization system 106 receives material characteristic data and equipment operating status information by communicating with the Internet of Things system 105, uses machine learning, deep reinforcement learning and other algorithms to model and optimize the data, and automatically generates live control strategies and electric field adjustment instructions to achieve adaptive control.

[0093] The Internet of Things system 105 collects operational data such as material particle size, resistivity, humidity, electric field load, and power consumption, and transmits the data in real time to the artificial intelligence optimization system 106 for processing.

[0094] The material collection system 104 is disposed downstream of the electric field control module 103 and collects materials with different charge characteristics according to the electrostatic separation results to achieve physical separation.

[0095] The above modules are connected through a communication bus or physical interface. The data is collected by the Internet of Things system 105 and sent to the artificial intelligence optimization system 106. The latter automatically calculates and sends the control strategy to the charged processing module 102 and the electric field control module 103 based on the data, realizing closed-loop optimization control of the electrostatic sorting process.

[0096] In one embodiment, the receiving system 104 may be configured with multiple receiving bins according to the sorting channels.

[0097] In one embodiment, the feeding system 101 conveys the material to the charging processing module 102 at a uniform dispersion and stable rate to ensure uniform effect of subsequent charging processing and avoid uneven charging or sorting failure due to accumulation or overload; after receiving the charging control strategy issued by the artificial intelligence optimization system 106, the charging processing module 102 performs contact charging, friction charging or corona discharge treatment on the material to make the material carry a charge suitable for sorting, and different particles have different charges and polarities; then, the charged material is fed into the electric field control module 103, and the electric field control module 103 dynamically adjusts the electrode voltage, spacing and polarity switching frequency according to the electric field adjustment instructions issued by the artificial intelligence optimization system 106 to generate an optimized electrostatic sorting electric field to dynamically adjust the deflection trajectory of the charged material in the electric field, so as to realize effective sorting of the material based on the charge characteristics; finally, materials with different charging characteristics enter the receiving system 104 according to the sorting trajectory for classification and collection.

[0098] The charging processing module 102 selects an operating mode based on the received charging control strategy, including contact charging, friction charging, and corona discharge. The contact charging unit charges the material through mechanical friction; the friction charging unit charges the material through contact between a high-speed rotating friction roller and the material; and the corona discharge unit ionizes and charges the material surface through high-voltage corona discharge.

[0099] The electric field control module 103 includes parallel electrode plates that can apply a preset voltage based on the received electric field adjustment command to form a spatial electric field, causing particles of different charges and polarities to deviate from the transmission trajectory. The spacing between the electrode plates is adjustable to accommodate the sorting requirements of materials of different particle sizes. The electrode voltage and polarity switching frequency can be dynamically adjusted in real time based on the material characteristics and charge state to optimize the material sorting trajectory and improve sorting accuracy. Different charges result in different deviation trajectories, and the deviation distance can be determined based on the material mass, material charge, electric field strength, and electric field action time.

[0100] The material collection system 104 includes multiple guide structures and multiple receiving bins connected thereto. Based on the charged polarity and offset trajectory of the sorted materials, the corresponding guide structures guide the corresponding categories of materials into the corresponding receiving bins. When the offset trajectory of the materials changes, the positions of the receiving bins and guide structures need to be adjusted to accurately receive the corresponding materials. The guide structures can be fixed baffles, dynamically switching baffles, or guide rail systems. The number of receiving bins can be configured into at least two or more groups based on the type of materials. The guide mechanisms can be, for example, pipes or flat plates, but this application is not limited thereto.

[0101] Figure 2 This is a structural diagram of an electrostatic separation system based on the Internet of Things and artificial intelligence provided by an embodiment of the present application. Figure 2 As shown, the IoT system 105 includes a sensor module 201, an IoT communication module 202 and a cloud platform 203;

[0102] The sensor module 201 is used to collect characteristic data of the material and operating data of the electrostatic separation system;

[0103] The IoT communication module 202 is used to receive the feature data and the operation data and send them to the artificial intelligence optimization system 106 and the cloud platform 203;

[0104] The cloud platform 203 is used to process and analyze the received feature data and operation data.

[0105] Specifically, the Internet of Things system 105 further includes: a sensor module 201, an Internet of Things communication module 202 and a cloud platform 203, so as to achieve comprehensive perception of the system operation status, efficient data transmission and centralized processing.

[0106] Sensor module 201 collects real-time material characteristic data and electrostatic separation system operational data. Material characteristic data includes, but is not limited to, material type, charge requirement, particle size, humidity, resistivity, and dielectric constant; operational data includes, but is not limited to, key equipment parameters such as electric field voltage, electrode spacing, power consumption, discharge frequency, electric field strength, electric field uniformity, electrode status, charged system current, operating temperature, and vibration spectrum. Sensor module 201 includes an electric field strength sensor, temperature and humidity sensor, current / voltage sensor, Hall effect sensor, vibration sensor, pressure sensor, electrostatic field measurement array, dielectric constant meter, surface charge detector, material flow sensor, and leakage current detector. Suitable installation locations are selected based on the equipment layout.

[0107] The IoT communication module 202 transmits the collected data to the artificial intelligence optimization system 106 and the cloud platform 203 via wireless communication. The IoT communication module 202 supports a two-way interaction mechanism, which can upload collected data and receive control commands returned by the cloud platform 203.

[0108] Cloud platform 203 centrally processes and analyzes the received feature data and operational data to generate a system health report. Cloud platform 203 integrates big data analysis tools and edge computing interfaces, and is responsible for data storage, status visualization, operational log generation, and historical trend prediction.

[0109] Through the coordinated operation of the above-mentioned Internet of Things system 105, not only is the system able to fully perceive the material status and equipment operation status, but it also ensures the efficiency of data interaction between the optimization system and the sorting site, laying the foundation for subsequent intelligent analysis and precise control.

[0110] In one embodiment, the communication mode of the IoT communication module 202 may include Wi-Fi, 4G / 5G, LoRa or NB-IoT, etc., to ensure the real-time and stability of data transmission.

[0111] In one embodiment, the cloud platform 203 analyzes device data to promptly warn of possible device failures and provide maintenance suggestions.

[0112] In one embodiment, the cloud platform 203 can also serve as a unified management center for multiple sorting devices, supporting parallel collaboration and remote scheduling of multiple devices.

[0113] In one embodiment, the artificial intelligence optimization system 106 is specifically used to:

[0114] receiving the characteristic data from the IoT system 105;

[0115] Determining a charging mode based on the characteristic data and a pre-trained decision tree model;

[0116] generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode;

[0117] A charging control strategy is generated using the charging mode and the charging parameters and sent to the charging processing module 102 .

[0118] Specifically, the artificial intelligence optimization system 106 receives characteristic data of materials from the Internet of Things system 105. The characteristic data includes physical and electrical properties of the materials such as particle size, resistivity, humidity, and dielectric constant. The characteristic data are collected in real time by the sensor module 201 and uploaded to the artificial intelligence optimization system 106 to serve as the input basis for subsequent electrification strategy decisions.

[0119] AI Optimization System 106 incorporates a charging pattern classification model, preferably a hybrid model combining decision trees (such as XGBoost and Random Forest) with deep learning. Based on historical data, it establishes optimal matching rules for charging patterns for different material types. AI Optimization System 106 automatically determines the optimal charging pattern based on currently collected feature data, improving adaptability and sorting efficiency.

[0120] For a given charging mode, the AI optimization system 106 invokes the corresponding deep reinforcement learning model, combining the current material characteristics and target sorting accuracy to output a set of optimized charging parameters. Different charging modes require different adjusted parameters. Ultimately, the AI optimization system 106 encapsulates the generated charging mode and charging parameters into a complete charging control strategy and sends it to the charging processing module 102 for execution, achieving intelligent closed-loop control of the charging mode and operating parameters. The AI optimization system 106 also performs online fine-tuning and self-learning based on feedback data to continuously optimize the control strategy.

[0121] In one embodiment, the step of pre-training the decision tree model includes:

[0122] During actual operation, the AI optimization system 106 conducts multiple batches of experiments with different types of mixed waste (such as plastics, metals, rubber, and minerals), collecting corresponding material characteristic data, including but not limited to: particle size, moisture, resistivity, dielectric constant, material composition labels, etc. It also records sorting effect indicators under different charging modes, such as charging uniformity, sorting rate, and residue rate.

[0123] The aforementioned feature data is used as the input vector, and the "optimal charging mode" determined by manual annotation or an expert system is used as the output label to construct a training sample set. Charging mode labels include contact charging, friction charging, and corona discharge. A highly interpretable ensemble decision tree model, such as XGBoost (Extreme Gradient Boosting Trees) or Random Forest, is preferably used. Through supervised learning training, the model structure is continuously iterated within the training set, so that after inputting material characteristics, it can output a probability distribution or classification result for the optimal charging mode.

[0124] The trained model is evaluated in multiple dimensions, including accuracy, recall, and F1 score, using independent test sets. Cross-validation and hyperparameter adjustment can be used to improve generalization capabilities and ensure stable prediction performance on new materials.

[0125] In one embodiment, plastic materials are suitable for tribocharging, metal materials are suitable for contact charging, and high-humidity materials are preferably subjected to corona discharge mode.

[0126] In one embodiment, in contact charging mode, the parameters adjusted are friction time, pressure, and friction material type; in tribocharging mode, the parameters adjusted are friction speed, material flow rate, and friction surface resistance; and in corona discharge mode, the parameters adjusted are electrode voltage, electrode spacing, and corona frequency. A charging parameter optimization strategy constructed through reinforcement learning can dynamically balance charging efficiency and energy consumption, significantly improving system operational efficiency. A reward function is established to provide positive or negative feedback on parameter adjustments based on material charging uniformity, energy consumption, and sorting accuracy, enabling the artificial intelligence optimization system 106 to continuously optimize the adjustment strategy.

[0127] In one embodiment, the step of pre-training the reinforcement learning model includes:

[0128] After determining the charging mode, the artificial intelligence optimization system 106 needs to further determine the corresponding charging parameter combination (such as voltage, contact pressure, rotation speed, electrode spacing, etc.) to meet the following goals: maximize charging efficiency and uniformity; minimize energy consumption; ensure stable operation and avoid electrical breakdown or charge imbalance.

[0129] A deep neural network (DQN (Deep Q-Network) or PPO (Proximal Policy Optimization)) is used. The model input is material feature data; the model output is the optimal combination of charging parameters; the model training goal is to minimize the charging deviation between the predicted value and the historical optimal sample, while meeting the power consumption threshold and safety constraints.

[0130] In one embodiment, because indicators such as material moisture and charge retention fluctuate dynamically over time, the AI optimization system 106 incorporates a time series prediction module based on a long short-term memory (LSTM) network to provide short-term predictions of material charge status and sorting efficiency trends. Based on feedback data, the AI optimization system 106 assesses the deviation between current charging parameters and actual charging results. If efficiency drops or charging drifts, the system quickly fine-tunes the parameters, achieving closed-loop adjustment and adaptive correction.

[0131] The artificial intelligence optimization system 106 uses LSTM (long short-term memory network) to predict the charge change trend of the material and implement pre-adjustment to prevent the sorting efficiency from decreasing due to environmental changes (such as increased humidity and charge attenuation).

[0132] In one embodiment, the artificial intelligence optimization system 106 is specifically used to:

[0133] receiving the characteristic data and the operating data from the IoT system 105;

[0134] Performing simulation modeling on the electric field of the electrostatic separation system according to the operating data to obtain a numerical model of the electric field distribution;

[0135] generating an electric field distribution state according to the electric field distribution numerical model;

[0136] generating electric field distribution control parameters according to the characteristic data, the electric field distribution state, and a pre-built reinforcement learning agent that calls the electric field distribution numerical model;

[0137] An electric field adjustment instruction is generated using the electric field distribution control parameter and sent to the electric field control module 103 .

[0138] Specifically, AI optimization system 106 receives material characteristic data and system operation data from IoT system 105, including electric field strength, voltage, electrode spacing, electrode temperature, electric field fluctuation, residual charge, material type, resistivity, humidity, and charge status feedback. This data is collected in real time by sensor module 201 and uploaded to AI optimization system 106 via IoT communication module 202 for unified processing.

[0139] The AI optimization system 106 uses the finite element method to simulate the electrostatic field of the electrode structure and voltage configuration in the electrostatic separation zone, constructing a high-precision numerical model of the electric field distribution. This model reflects spatial characteristics such as potential distribution, electric field line orientation, and field intensity gradient under different input parameters. The simulation model is fitted and corrected based on historical operating data and measured electric field sensor data to ensure high consistency between the numerical model and the physical system. During operation, the AI optimization system 106 uses this model to generate the electric field distribution state (including uniformity indicators and hotspot identification) under the current configuration, which serves as an input reference for the reinforcement learning agent.

[0140] The AI optimization system 106 includes a reinforcement learning agent (RLA). Its role is to continuously learn and optimize the electric field control strategy in the electrostatic sorting system, supported by an established numerical model of electric field distribution. During operation, the system inputs a state vector in real time. The RL agent outputs electric field distribution control parameters, including electrode voltage, electrode spacing, and electrode morphology, based on the strategy network. This agent generates the final electric field adjustment instructions and sends them to the electric field control module 103, optimizing the electric field distribution.

[0141] Finally, AI optimization system 106 encapsulates the generated electric field distribution control parameters into electric field adjustment instructions and sends them to electric field control module 103 for execution, thus achieving intelligent closed-loop control of the electric field distribution control parameters. AI optimization system 106 can also perform online fine-tuning and self-learning based on feedback data to continuously optimize the control strategy.

[0142] By introducing reinforcement learning agents, the artificial intelligence optimization system 106 can not only achieve dynamic adaptation of the electric field configuration under complex materials and operating conditions, but also continuously learn feedback during the sorting process, improve decision-making flexibility, optimize energy efficiency control, and have high environmental adaptability and expansion capabilities.

[0143] In one embodiment, a reinforcement learning agent includes: an environment interface, a state space, an action space, a policy network and a learning algorithm, a reward function design, and a strategy output and online call mechanism.

[0144] A reinforcement learning agent is integrated with a numerical model of the electric field distribution. Finite element simulation (FEM) is used to simulate the electrostatic field response under different electrode configurations. This simulation environment provides state feedback and reward signals, forming the reinforcement learning training environment. The state received by the reinforcement learning agent at each decision includes the current electric field distribution and material properties. Adjustable parameters for the reinforcement learning agent include electrode voltage, electrode spacing, electrode polarity switching strategy, discharge frequency, or trigger period. The reinforcement learning agent is constructed using the Deep Deterministic Policy Gradient (DDPG) or Proximal Policy Optimization (PPO) algorithm. Its policy network maps state to action, and the training process continuously iterates to optimize the control strategy. The reward function is designed as a multi-objective function to guide the reinforcement learning agent in generating optimized electric field configuration parameters. Specific metrics include: improved electric field uniformity (positive reward); improved electrostatic separation efficiency (positive reward); reduced system energy consumption (positive reward); and negative penalties for electric field instability, excessive energy consumption, local overload, or breakdown (negative penalty).

[0145] In one embodiment, the artificial intelligence optimization system 106 identifies non-uniform areas in the electric field and interference factors that may affect the sorting effect (such as electrode loss, environmental humidity, etc.) through potential distribution and field intensity gradient analysis.

[0146] In one embodiment, the artificial intelligence optimization system 106 uses RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) to predict the short-term trend of electric field changes and adjust electrode parameters in advance to reduce sudden abnormalities during the sorting process.

[0147] In one embodiment, in an industrial operating environment, AI optimization system 106 adjusts the voltage source, grounding configuration, and electrode layout in real time to achieve adaptive electric field optimization and improve sorting stability. Integrating edge computing with AI models deployed on-device reduces latency and improves optimization response speed.

[0148] Figure 3 This is a structural diagram of an electrostatic separation system based on the Internet of Things and artificial intelligence provided by an embodiment of the present application. Figure 3 As shown, the electrostatic separation system based on the Internet of Things and artificial intelligence provided by the present application further includes: a safety intelligent management system 301;

[0149] The safety intelligent management system 301 is connected to the Internet of Things system 105 and the artificial intelligence optimization system 106, and is used to identify the abnormal state and failure trend of the electrostatic sorting system based on the received operating data, and send the identification results to the artificial intelligence optimization system 106 to adjust the operating strategy.

[0150] Specifically, the safety and intelligent management system 301 ensures the system's safety, stability, and maintainability in high-voltage electrostatic sorting environments. Connected to both the IoT system 105 and the AI optimization system 106, the safety and intelligent management system 301 identifies potential abnormalities or failure trends based on key equipment status data collected during operation. It then feeds the results back to the AI optimization system 106 to dynamically adjust operational strategies, thereby achieving the safety and intelligent control goal of "operating, monitoring, and optimizing simultaneously."

[0151] In one embodiment, the electrostatic sorting system integrates overload protection, leakage protection, and short-circuit protection to ensure electrical system safety. If an abnormality occurs in the electrostatic sorting system, the power supply is automatically cut off and the operator is remotely notified via the Internet of Things system 105.

[0152] In one embodiment, the security intelligent management system 301 is specifically used to:

[0153] Receiving the operating data from the Internet of Things system 105;

[0154] Identifying an abnormal state of the electrostatic separation system based on the operating data and a pre-trained anomaly detection model; the anomaly detection model is obtained by unsupervised training of historical operating data of the electrostatic separation system under normal operating conditions;

[0155] Identifying the failure trend of the electrostatic separation system based on the operating data and a pre-trained fault prediction model; the fault prediction model is obtained by supervised training of historical operating data and fault records of the electrostatic separation system in different operating cycles;

[0156] The recognition result is sent to the artificial intelligence optimization system 106 to adjust the operation strategy.

[0157] Specifically, the security intelligent management system 301 obtains operating data from multiple sensors through the Internet of Things system 105, including but not limited to operating environment parameters such as current, voltage, electrode temperature, electric field uniformity and charge change, vibration, noise and discharge frequency.

[0158] Based on the real-time data received, the safety intelligent management system 301 uses a pre-trained anomaly detection model to conduct real-time assessments of the device status. This anomaly detection model employs unsupervised learning, trained on a large amount of historical normal operating data. This model establishes a "behavioral boundary" for normal conditions, allowing it to accurately identify abnormal patterns that exceed thresholds during operation, such as abnormal electrode current fluctuations, sudden temperature rises, uneven electric fields, or polarity drift.

[0159] The Safety Intelligent Management System 301 further incorporates a supervised training fault prediction model, combining historical fault labels with operational cycle data to assess whether the current state presents a potential failure risk. This prediction is made by analyzing trend indicators such as decreased charging efficiency due to electrode aging, electric field imbalance caused by unstable high-voltage power supply, and electrode misalignment caused by long-term vibration.

[0160] Once an anomaly is identified or a high-risk trend is predicted, the security intelligent management system 301 sends the relevant results to the artificial intelligence optimization system 106, which adjusts the operating strategy to avoid the impact of the failure.

[0161] In one embodiment, the anomaly detection model uses Isolation Forest or LSTM-based anomaly sequence detection. The LSTM algorithm learns the normal operating patterns of the device and predicts parameter change trends over short periods of time. The Isolation Forest algorithm trains the model to identify abnormal characteristics of device operation, such as short circuits, leakage, and electric field imbalances caused by electrode aging. When an anomaly is detected, a safety protection mechanism is triggered and a detailed anomaly log is generated.

[0162] In one embodiment, the fault prediction model is preferably trained using algorithms such as Random Forest, LSTM, or ARIMA, enabling prediction of potential future faults or component failures. This includes determining whether current / voltage exceeds specified limits to predict short-circuit risks; generating temperature anomaly curves to detect potential high-temperature burnout failures; and identifying abnormal loads in the energized system to provide early warning of potential electrode damage. A Random Forest classification algorithm is used to classify faults: electrode failure (abnormal voltage fluctuations and decreased charging efficiency); unstable corona discharge (abnormal electric field distribution and incorrect charging patterns); and excessive temperature (heat dissipation system failure).

[0163] In one embodiment, strategies for dealing with abnormalities or failures include, but are not limited to: reducing voltage, switching polarity operation strategies, starting self-test or cleaning processes, switching to energy-saving or low-load operation modes, and remotely notifying operation and maintenance personnel.

[0164] In one embodiment, the intelligent safety management system 301 provides detailed fault reports when equipment anomalies occur, and remotely transmits these reports via the IoT system 105, notifying operators in advance of maintenance. Based on the equipment's operating status and historical data, it assesses the equipment's maintenance cycle, provides regular maintenance recommendations, and provides automatic maintenance notifications.

[0165] In one embodiment, the safety intelligent management system 301 uses wavelet transform to perform data noise reduction and extract fault precursor features: abnormal current fluctuations and abnormal electrode temperature, etc.

[0166] In one embodiment, the safety intelligent management system 301 calculates the safety health score of the system based on historical data and real-time monitoring results: 80-100 safe: the equipment is in normal condition and no intervention is required; 50-80 warning: there is a slight abnormality, and it is recommended to check the loose electrodes and current fluctuations of specific components; 0-50 high risk: the equipment may have a serious failure, automatically cutting off the power supply and triggering an alarm.

[0167] Figure 4 This is a structural diagram of an electrostatic separation system based on the Internet of Things and artificial intelligence provided by an embodiment of the present application. Figure 4 As shown, the electrostatic separation system based on the Internet of Things and artificial intelligence provided by this application also includes: an energy efficiency optimization system 401;

[0168] The energy efficiency optimization system 401 is connected to the Internet of Things system 105 and the electric field control module 103, and is used to generate an optimization control strategy based on the received operating data to perform adaptive optimization and regulation on the electric field power.

[0169] Specifically, while ensuring sorting accuracy, energy efficiency optimization system 401 dynamically optimizes electric field power output, reduces energy consumption, and improves overall operational efficiency. Energy efficiency optimization system 401 is connected to IoT system 105 and electric field control module 103. By continuously monitoring and analyzing the system's operating status, it autonomously generates and distributes optimized control strategies, enabling adaptive adjustment of electric field power and intelligent energy-saving mode switching.

[0170] In one embodiment, the energy efficiency optimization system 401 is specifically configured to:

[0171] Receiving the operating data from the Internet of Things system 105;

[0172] generating an optimized control strategy through dynamic analysis based on the operating data;

[0173] The optimization control strategy is sent to the electric field control module 103 to perform adaptive optimization adjustment on the electric field power.

[0174] Specifically, energy efficiency optimization system 401 receives operational data from IoT system 105, primarily including current material flow, electric field load, real-time energy consumption, equipment temperature, charge changes, and electrode response efficiency. Energy efficiency optimization system 401 utilizes a combination of fuzzy logic control (FLC) and deep reinforcement learning (DRL) to adaptively adjust the electric field output power in real time. During high-load sorting, the electric field intensity is increased to ensure sufficient charging and sorting accuracy. During low load or material shortage phases, the voltage and energy consumption are automatically reduced to prevent excessive discharge. Fuzzy control rules are established based on material flow, charge, and electrode load to adjust the electric field voltage and frequency.

[0175] Energy efficiency optimization system 401 generates an optimized control strategy based on the current material load and energy consumption status. This includes adjusting the high-voltage power supply output voltage, controlling the frequency of electric field polarity switching, and switching to low-power operation or intermittent power supply mode. These control instructions are sent to electric field control module 103 via the communication bus to implement real-time power regulation.

[0176] In one embodiment, a DRL algorithm (such as DQN or PPO) uses a pre-trained reinforcement learning strategy to learn the energy-accuracy-stability balance in a simulation environment. The reward function is set as follows: improved sorting accuracy (positive reward); improved electric field uniformity (positive reward); reduced energy consumption (positive reward); and energy waste or decreased efficiency (negative penalty). The optimization objectives are: when the load is high, the electric field strength is automatically increased to ensure sorting accuracy; when the load is low, the electric field power is reduced to avoid excessive energy loss; and the adaptive adjustment cycle, using historical data to optimize the adjustment frequency, reduces the impact of severe electric field fluctuations on sorting results.

[0177] In one embodiment, the energy efficiency optimization system 401 is further configured with an automatic energy-saving mode switching function, which is used to automatically determine whether to enter the energy-saving operation state when the equipment is in a low-load operation state, and dynamically adjust the electric field power strategy according to the prediction results, thereby effectively reducing energy consumption and improving equipment operation efficiency.

[0178] The energy efficiency optimization system 401 continuously monitors key operating parameters during equipment operation and triggers energy-saving mode evaluation when the following conditions are met:

[0179] The material inflow rate is lower than a preset threshold, for example, the actual material flow rate is less than 30% of the system's full load capacity; the electrode power consumption remains low, indicating that the system is not operating at high load; and the short-term forecast results based on historical data show no trend towards high load conditions in the future. When all of the above conditions are met, the energy efficiency optimization system 401 will evaluate whether to enter energy-saving operation.

[0180] To improve the accuracy and responsiveness of energy-saving mode switching, the energy efficiency optimization system 401 introduces an LSTM (Long Short-Term Memory) prediction model to predict and analyze material load trends over a period of time. Based on the prediction results, the energy efficiency optimization system 401 can implement the following strategies:

[0181] If the forecast results show that the load will continue to be low in the short term, the system will enter "low-power energy-saving mode", significantly reducing the electric field voltage and reducing energy loss; if it is predicted that the load may recover or fluctuate in a short period of time, the system will maintain "dynamic standby mode" and the electric field will maintain a low-intensity operating state so that it can respond quickly and resume efficient sorting when the load recovers.

[0182] After entering the energy-saving mode, the energy efficiency optimization system 401 will implement the following electric field control strategies based on different operating conditions:

[0183] Power optimization and regulation: automatically reduce the high-voltage power output level to avoid unnecessary electrode discharge and energy waste;

[0184] Periodic working mode: When the equipment is in a low-load state for a long time, the system can switch to "intermittent power supply" mode, intermittently activating the electric field according to the set period, further reducing the energy consumption caused by continuous operation;

[0185] Edge computing optimization scheduling: Energy-saving mode judgment and policy switching can be completed locally through the edge computing module without frequent reliance on the cloud platform 203, improving policy response speed and reducing communication delay.

[0186] The present application provides an electrostatic sorting system, method and device based on the Internet of Things and artificial intelligence, which includes: a feeding system, a material receiving system, a charging processing module, an electric field control module, an artificial intelligence optimization system and an Internet of Things system; the feeding system is connected to the charging processing module to uniformly feed the material into the electrostatic sorting system; the Internet of Things system is connected to the artificial intelligence optimization system through network communication to collect characteristic data of the material and operating data of the electrostatic sorting system and perform data processing and analysis on the characteristic data and the operating data; the artificial intelligence optimization system is used to generate optimal electric field adjustment instructions and charging control strategies based on the received characteristic data and the operating data; the charging processing module is connected to the electric field control module and is connected to the artificial intelligence optimization system through a communication bus to adjust the charging mode and charging parameters of the material according to the received charging control strategy; the electric field control module is connected to the artificial intelligence optimization system through a communication bus to optimize the electric field distribution according to the received electric field adjustment instructions; the material receiving system is connected to the electric field control module to receive the material after electrostatic sorting. This application realizes intelligent optimization and adaptive adjustment of electrostatic sorting, improving sorting accuracy and system stability.

[0187] Among them, the feeding system realizes uniform material transportation, ensuring the stability and sorting accuracy of the subsequent charging and sorting process; the Internet of Things system realizes real-time perception and data collection of material characteristics and equipment operating status, providing data support for intelligent optimization; the artificial intelligence optimization system realizes adaptive optimization based on material characteristics and equipment status, automatically generates control strategies for electric fields and charging parameters, and improves the intelligence level and energy efficiency level of the equipment; the charging processing module realizes intelligent selection and adjustment of charging modes and charging parameters for different materials, and improves the uniformity of material charging and subsequent sorting efficiency; the electric field control module realizes dynamic optimization of electrode voltage, electric field strength and distribution, ensuring that the electric field adapts to the material status and reduces energy consumption; the collecting system realizes efficient classification and collection of sorted materials, ensuring the continuity and practicality of the entire electrostatic sorting process.

[0188] Figure 5 This is a flow chart of an electrostatic separation method based on the Internet of Things and artificial intelligence provided by an embodiment of the present application, such as Figure 5 As shown, the electrostatic separation method based on the Internet of Things and artificial intelligence provided by this application includes:

[0189] S501: Receive the feature data from the Internet of Things system;

[0190] S502: Determine a charging mode based on the characteristic data and a pre-trained decision tree model;

[0191] S503: generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode;

[0192] S504: Generate a charging control strategy using the charging mode and the charging parameters and send the strategy to the charging processing module.

[0193] from Figure 5 As can be seen from the shown process, the electrostatic sorting method based on the Internet of Things and artificial intelligence provided by the present application receives the characteristic data from the Internet of Things system; determines the charging mode based on the characteristic data and a pre-trained decision tree model; generates charging parameters based on the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode; generates a charging control strategy using the charging mode and the charging parameters and sends it to the charging processing module, thereby realizing intelligent optimization and adaptive adjustment of electrostatic sorting, and improving sorting accuracy and system stability.

[0194] Among them, by receiving the characteristic data from the Internet of Things system, real-time acquisition of material characteristic data is achieved, providing an accurate data basis for subsequent intelligent decision-making; the charging mode is determined according to the characteristic data and the pre-trained decision tree model, and the adaptive charging mode can be intelligently identified to improve the pertinence and sorting adaptability of the charging method; the charging parameters are generated according to the characteristic data and the pre-trained reinforcement learning model corresponding to the charging mode, and the optimal charging parameters matching the material characteristics can be generated to ensure stable charging effect and optimal energy efficiency; by using the charging mode and the charging parameters to generate a charging control strategy and sending it to the charging processing module, a complete charging control strategy can be formed and issued for execution, thereby realizing automatic control and fine adjustment of the charging process.

[0195] Through the above-mentioned automatic energy-saving mode switching function, the system can not only effectively reduce power consumption by 20%-30% in low-load operation scenarios, but also ensure that the sorting effect is not weakened, realizing the coordinated operation of high precision and high energy efficiency.

[0196] The following uses the artificial intelligence optimization system as the execution body to explain each step in detail.

[0197] S501: Receive the feature data from the Internet of Things system;

[0198] Specifically, the artificial intelligence optimization system receives characteristic data of materials from the Internet of Things system. The characteristic data includes the physical and electrical properties of the materials such as particle size, resistivity, humidity, and dielectric constant. The data are collected in real time by the sensor module and uploaded to the artificial intelligence optimization system to serve as the input basis for subsequent electrification strategy decisions.

[0199] S502: Determine a charging mode based on the characteristic data and a pre-trained decision tree model;

[0200] Specifically, the AI optimization system incorporates a charging mode classification model, preferably a hybrid model combining decision trees (such as XGBoost and Random Forest) with deep learning. Based on historical data, it establishes optimal matching rules for charging methods for different material types. The AI optimization system automatically determines the optimal charging method based on currently collected feature data, improving adaptability and sorting efficiency.

[0201] In one embodiment, the step of pre-training the decision tree model includes:

[0202] During actual operation, the AI optimization system conducts multiple batches of experiments on different types of mixed waste (such as plastics, metals, rubber, and minerals), collecting corresponding material characteristic data, including but not limited to: particle size, moisture, resistivity, dielectric constant, material composition labels, etc. At the same time, it records sorting effect indicators under different charging modes, such as charging uniformity, sorting rate, and residue rate.

[0203] The aforementioned feature data is used as the input vector, and the "optimal charging mode" determined by manual annotation or an expert system is used as the output label to construct a training sample set. Charging mode labels include contact charging, friction charging, and corona discharge. A highly interpretable ensemble decision tree model, such as XGBoost (Extreme Gradient Boosting Trees) or Random Forest, is preferably used. Through supervised learning training, the model structure is continuously iterated within the training set, so that after inputting material characteristics, it can output a probability distribution or classification result for the optimal charging mode.

[0204] The trained model is evaluated in multiple dimensions, including accuracy, recall, and F1 score, using independent test sets. Cross-validation and hyperparameter adjustment can be used to improve generalization capabilities and ensure stable prediction performance on new materials.

[0205] In one embodiment, plastic materials are suitable for tribocharging, metal materials are suitable for contact charging, and high-humidity materials are preferably subjected to corona discharge mode.

[0206] S503: generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode;

[0207] Specifically, for a given charging mode, the AI optimization system invokes the corresponding deep reinforcement learning model, combining the current material characteristics and target sorting accuracy to output a set of optimized charging parameters. The parameters adjusted vary for different charging modes.

[0208] In one embodiment, in contact charging mode, the parameters adjusted are friction time, pressure, and friction material type; in tribocharging mode, the parameters adjusted are friction speed, material flow rate, and friction surface resistance; and in corona discharge mode, the parameters adjusted are electrode voltage, electrode spacing, and corona frequency. A charging parameter optimization strategy constructed through reinforcement learning can dynamically balance charging efficiency and energy consumption, significantly improving system operational efficiency. A reward function is established to provide positive or negative feedback on parameter adjustments based on material charging uniformity, energy consumption, and sorting accuracy, enabling the AI optimization system to continuously optimize and adjust the strategy.

[0209] In one embodiment, generating charging parameters according to the feature data and a pre-trained reinforcement learning model corresponding to the charging mode includes:

[0210] After determining the charging mode, the artificial intelligence optimization system needs to further determine the corresponding charging parameter combination (such as voltage, contact pressure, rotation speed, electrode spacing, etc.) to meet the following goals: maximize charging efficiency and uniformity; minimize energy consumption; ensure stable operation and avoid electrical breakdown or charge imbalance.

[0211] A deep neural network (DQN (Deep Q-Network) or PPO (Proximal Policy Optimization)) is used. The model input is material feature data; the model output is the optimal combination of charging parameters; the model training goal is to minimize the charging deviation between the predicted value and the historical optimal sample, while meeting the power consumption threshold and safety constraints.

[0212] Because material moisture and charge retention fluctuate dynamically over time, the AI optimization system incorporates a time series prediction module based on a long short-term memory (LSTM) network to provide short-term forecasts of material charge status and sorting efficiency trends. Based on feedback data, the AI optimization system assesses the deviation between current charging parameters and actual charging results. When efficiency drops or charging drifts are detected, the system quickly fine-tunes parameters, achieving closed-loop adjustments and adaptive corrections.

[0213] In one embodiment, the artificial intelligence optimization system uses LSTM (long short-term memory network) to predict the charge change trend of the material and implement pre-adjustment to prevent the sorting efficiency from decreasing due to environmental changes (such as increased humidity and charge attenuation).

[0214] S504: Generate a charging control strategy using the charging mode and the charging parameters and send the strategy to the charging processing module.

[0215] Specifically, the AI optimization system encapsulates the generated live mode and parameters into a complete live control strategy, which is then sent to the live processing module for execution, achieving intelligent closed-loop control of the live mode and operating parameters. The AI optimization system also performs online fine-tuning and self-learning based on feedback data to continuously optimize the control strategy.

[0216] Figure 6 This is a flow chart of an electrostatic separation method based on the Internet of Things and artificial intelligence provided by an embodiment of the present application, such as Figure 6 As shown, the electrostatic separation method based on the Internet of Things and artificial intelligence provided by this application also includes:

[0217] S601: Receive the feature data and the operation data from the Internet of Things system;

[0218] Specifically, the AI optimization system receives material characteristic data and system operation data from the IoT system, including electric field strength, voltage, electrode spacing, electrode temperature, electric field fluctuation, residual charge, material type, resistivity, humidity, and charge status feedback. This data is collected in real time by the sensor module and uploaded to the AI optimization system via the IoT communication module for unified processing.

[0219] S602: Performing simulation modeling on the electric field of the electrostatic separation system according to the operation data to obtain a numerical model of the electric field distribution;

[0220] Specifically, the AI optimization system uses the finite element method to simulate the electrostatic field of the electrode structure and voltage configuration in the electrostatic separation zone, constructing a highly accurate numerical model of the electric field distribution. This model reflects spatial characteristics such as the potential distribution, the direction of electric field lines, and the field intensity gradient under different input parameters. The simulation model is fitted and corrected based on historical operating data and measured electric field sensor data to ensure high consistency between the numerical model and the physical system.

[0221] S603: generating an electric field distribution state according to the electric field distribution numerical model;

[0222] Specifically, during operation, the artificial intelligence optimization system calls the electric field distribution numerical model to generate the electric field distribution state under the current configuration (including uniformity indicators and hot spot identification, etc.) as an input reference for the reinforcement learning agent.

[0223] S604: Generate electric field distribution control parameters according to the characteristic data, the electric field distribution state, and a pre-built reinforcement learning agent that calls the electric field distribution numerical model;

[0224] Specifically, the AI optimization system incorporates a reinforcement learning agent (RLA), which continuously learns and optimizes the electric field control strategy in the electrostatic sorting system, supported by an established numerical model of the electric field distribution. During operation, the system inputs a state vector in real time. The RL agent outputs electric field distribution control parameters, including electrode voltage, electrode spacing, and electrode morphology, based on the strategy network. This output generates the final electric field adjustment instructions, which are then sent to the electric field control module to optimize the electric field distribution.

[0225] By introducing reinforcement learning agents, the artificial intelligence optimization system can not only achieve dynamic adaptation of electric field configuration under complex materials and operating conditions, but also continuously learn feedback during the sorting process, improve decision-making flexibility, optimize energy efficiency control, and have high environmental adaptability and scalability.

[0226] In one embodiment, a reinforcement learning agent includes: an environment interface, a state space, an action space, a policy network and a learning algorithm, a reward function design, and a strategy output and online call mechanism.

[0227] A reinforcement learning agent is integrated with a numerical model of the electric field distribution. Finite element simulation (FEM) is used to simulate the electrostatic field response under different electrode configurations. This simulation environment provides state feedback and reward signals, forming the reinforcement learning training environment. The state received by the reinforcement learning agent at each decision includes the current electric field distribution and material properties. Adjustable parameters for the reinforcement learning agent include electrode voltage, electrode spacing, electrode polarity switching strategy, discharge frequency, or trigger period. The reinforcement learning agent is constructed using the Deep Deterministic Policy Gradient (DDPG) or Proximal Policy Optimization (PPO) algorithm. Its policy network maps state to action, and the training process continuously iterates to optimize the control strategy. The reward function is designed as a multi-objective function to guide the reinforcement learning agent in generating optimized electric field configuration parameters. Specific metrics include: improved electric field uniformity (positive reward); improved electrostatic separation efficiency (positive reward); reduced system energy consumption (positive reward); and negative penalties for electric field instability, excessive energy consumption, local overload, or breakdown (negative penalty).

[0228] In one embodiment, the artificial intelligence optimization system identifies uneven areas in the electric field and interference factors that may affect the sorting effect (such as electrode loss, environmental humidity effects, etc.) through potential distribution and field intensity gradient analysis.

[0229] In one embodiment, the artificial intelligence optimization system uses RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) to predict the short-term trend of electric field changes and adjust electrode parameters in advance to reduce sudden abnormal situations during the sorting process.

[0230] In one embodiment, an AI optimization system adjusts the voltage source, grounding configuration, and electrode layout in real time within an industrial operating environment to achieve adaptive electric field optimization and improve sorting stability. Integrating edge computing with AI models deployed on-device reduces latency and improves optimization response speed.

[0231] S605: Generate an electric field adjustment instruction using the electric field distribution control parameter and send the instruction to the electric field control module.

[0232] Specifically, the AI optimization system encapsulates the generated electric field distribution control parameters into electric field adjustment instructions and sends them to the electric field control module for execution, achieving intelligent closed-loop control of the electric field distribution control parameters. The AI optimization system can also perform online fine-tuning and self-learning based on feedback data to continuously optimize the control strategy.

[0233] The electrostatic sorting method based on the Internet of Things and artificial intelligence provided in the present application receives the characteristic data from the Internet of Things system; determines the charging mode based on the characteristic data and a pre-trained decision tree model; generates charging parameters based on the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode; uses the charging mode and the charging parameters to generate a charging control strategy and sends it to the charging processing module, thereby realizing intelligent optimization and adaptive adjustment of electrostatic sorting, and improving sorting accuracy and system stability.

[0234] Among them, by receiving the characteristic data from the Internet of Things system, real-time acquisition of material characteristic data is achieved, providing an accurate data basis for subsequent intelligent decision-making; the charging mode is determined according to the characteristic data and the pre-trained decision tree model, and the adaptive charging mode can be intelligently identified to improve the pertinence and sorting adaptability of the charging method; the charging parameters are generated according to the characteristic data and the pre-trained reinforcement learning model corresponding to the charging mode, and the optimal charging parameters matching the material characteristics can be generated to ensure stable charging effect and optimal energy efficiency; by using the charging mode and the charging parameters to generate a charging control strategy and sending it to the charging processing module, a complete charging control strategy can be formed and issued for execution, thereby realizing automatic control and fine adjustment of the charging process.

[0235] Based on the same inventive concept, the embodiments of the present application also provide an electrostatic sorting device based on the Internet of Things and artificial intelligence, which can be used to implement the methods described in the above embodiments, as described in the following embodiments. Since the principles of solving problems by the electrostatic sorting device based on the Internet of Things and artificial intelligence are similar to those of the electrostatic sorting method based on the Internet of Things and artificial intelligence, the implementation of the electrostatic sorting device based on the Internet of Things and artificial intelligence can refer to the implementation of the method based on software performance benchmark determination, and the repetitions will not be repeated. As used below, the terms "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceived.

[0236] Figure 7 This is a schematic diagram of the structure of an electrostatic separation device based on the Internet of Things and artificial intelligence provided by an embodiment of the present application. Figure 7 As shown, the electrostatic separation device based on the Internet of Things and artificial intelligence provided by this application includes:

[0237] A first data receiving unit 701 is configured to receive the feature data from the IoT system;

[0238] A charging mode determining unit 702 is configured to determine a charging mode based on the feature data and a pre-trained decision tree model;

[0239] A charging parameter generating unit 703 is configured to generate charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode;

[0240] The charging control strategy output unit 704 is configured to generate a charging control strategy using the charging mode and the charging parameters and send the strategy to the charging processing module.

[0241] Figure 8 This is a schematic diagram of the structure of an electrostatic separation device based on the Internet of Things and artificial intelligence provided by an embodiment of the present application. Figure 7 On the basis of the embodiment, further, as Figure 8 As shown, the electrostatic separation device based on the Internet of Things and artificial intelligence provided by this application also includes:

[0242] A second data receiving unit 801 is configured to receive the feature data and the operation data from the IoT system;

[0243] A model building unit 802 is configured to simulate and model the electric field of the electrostatic separation system according to the operating data to obtain a numerical model of the electric field distribution;

[0244] An electric field distribution state generating unit 803 is configured to generate an electric field distribution state according to the electric field distribution numerical model;

[0245] A control parameter generating unit 804 is configured to generate electric field distribution control parameters based on the characteristic data, the electric field distribution state, and a pre-built reinforcement learning agent that calls the electric field distribution numerical model;

[0246] The electric field adjustment instruction output unit 805 is configured to generate an electric field adjustment instruction using the electric field distribution control parameter and send the instruction to the electric field control module.

[0247] The electrostatic sorting method and device based on the Internet of Things and artificial intelligence provided in this application receive the characteristic data from the Internet of Things system; determine the charging mode based on the characteristic data and a pre-trained decision tree model; generate charging parameters based on the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode; use the charging mode and the charging parameters to generate a charging control strategy and send it to the charging processing module, thereby realizing intelligent optimization and adaptive adjustment of electrostatic sorting, and improving sorting accuracy and system stability.

[0248] Among them, by receiving the characteristic data from the Internet of Things system, real-time acquisition of material characteristic data is achieved, providing an accurate data basis for subsequent intelligent decision-making; the charging mode is determined according to the characteristic data and the pre-trained decision tree model, and the adaptive charging mode can be intelligently identified to improve the pertinence and sorting adaptability of the charging method; the charging parameters are generated according to the characteristic data and the pre-trained reinforcement learning model corresponding to the charging mode, and the optimal charging parameters matching the material characteristics can be generated to ensure stable charging effect and optimal energy efficiency; by using the charging mode and the charging parameters to generate a charging control strategy and sending it to the charging processing module, a complete charging control strategy can be formed and issued for execution, thereby realizing automatic control and fine adjustment of the charging process.

[0249] From a hardware perspective, in order to address the issues of traditional electrostatic sorting equipment being unable to intelligently adjust the electric field strength and charging parameters according to material changes and operating status, resulting in high energy consumption, low sorting accuracy, frequent equipment maintenance, and a lack of automatic optimization and remote management capabilities, the present application provides an embodiment of an electronic device for implementing all or part of the electrostatic sorting method based on the Internet of Things and artificial intelligence. The electronic device specifically includes the following:

[0250] Processor (Processor), memory (Memory), communication interface (Communications Interface) and bus; wherein the processor, memory, and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the electrostatic separation device based on the Internet of Things and artificial intelligence and related equipment such as the core business system, user terminal and related database; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but this embodiment is not limited to this. In this embodiment, the logic controller can be implemented with reference to the embodiment of the electrostatic separation method based on the Internet of Things and artificial intelligence, and the embodiment of the electrostatic separation device based on the Internet of Things and artificial intelligence in the embodiment, and their contents are merged here, and the repeated parts are not repeated.

[0251] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0252] In practical applications, portions of the electrostatic separation method based on the Internet of Things and artificial intelligence can be executed on the electronic device side as described above, or all operations can be completed on the client device. The specific selection can be based on the processing capabilities of the client device and the limitations of the user's usage scenario. This application does not impose any restrictions on this. If all operations are completed on the client device, the client device may also include a processor.

[0253] The aforementioned client device may include a communication module (i.e., a communication unit) capable of establishing a communication connection with a remote server to facilitate data transmission with the server. The server may include a server at the task scheduling center or, in other implementation scenarios, a server on an intermediate platform, such as a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may comprise a single computer device, a server cluster consisting of multiple servers, or a distributed server configuration.

[0254] Figure 9 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 9 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0255] In one embodiment, the electrostatic separation method based on the Internet of Things and artificial intelligence can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0256] S501: Receive the feature data from the Internet of Things system;

[0257] S502: Determine a charging mode based on the characteristic data and a pre-trained decision tree model;

[0258] S503: generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode;

[0259] S504: Generate a charging control strategy using the charging mode and the charging parameters and send the strategy to the charging processing module.

[0260] From the above description, it can be seen that the electrostatic sorting method and device based on the Internet of Things and artificial intelligence provided by this application realizes the adaptive adjustment of electric field, voltage and charging mode, while ensuring the sorting accuracy, reducing energy consumption by 20%-30%, and improving the intelligence level of equipment, maintenance efficiency and long-term operation stability. By receiving the characteristic data from the Internet of Things system, the material characteristic data can be acquired in real time, providing an accurate data basis for subsequent intelligent decision-making; the charging mode is determined according to the characteristic data and the pre-trained decision tree model, and the adapted charging mode can be intelligently identified to improve the pertinence and sorting adaptability of the charging method; the charging parameters are generated according to the pre-trained reinforcement learning model corresponding to the characteristic data and the charging mode, and the optimal charging parameters matching the material characteristics can be generated to ensure that the charging effect is stable and the energy efficiency is optimal; by generating a charging control strategy using the charging mode and the charging parameters and sending it to the charging processing module, a complete charging control strategy can be formed and issued for execution, realizing automatic control and fine adjustment of the charging process.

[0261] In another embodiment, the electrostatic sorting device based on the Internet of Things and artificial intelligence can be configured separately from the central processing unit 9100. For example, the data composite transmission device based on the electrostatic sorting device based on the Internet of Things and artificial intelligence can be configured as a chip connected to the central processing unit 9100, and the functions of the electrostatic sorting method based on the Internet of Things and artificial intelligence can be realized through the control of the central processing unit.

[0262] like Figure 9 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 9 In addition, the electronic device 9600 may also include all components shown in Figure 9 For components not shown, reference may be made to the prior art.

[0263] like Figure 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0264] Memory 9140 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and also store programs that execute the relevant information. The CPU 9100 can execute the programs stored in memory 9140 to implement information storage or processing.

[0265] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 may be, for example, a keypad or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0266] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), or SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is capable of storing additional data. Examples of such memory are sometimes referred to as EPROMs. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs, or processes used by the central processing unit 9100 to execute operations of the electronic device 9600.

[0267] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, images, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0268] The communication module 9110 is a transmitter / receiver 9110 that sends and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in a conventional mobile communication terminal.

[0269] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130, providing audio output via the speaker 9131 and receiving audio input from the microphone 9132, thereby implementing common telecommunication functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is coupled to the central processing unit 9100, enabling local recording via the microphone 9132 and playback of stored audio via the speaker 9131.

[0270] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the electrostatic separation method based on the Internet of Things and artificial intelligence in the above-mentioned embodiments, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all steps of the electrostatic separation method based on the Internet of Things and artificial intelligence in the above-mentioned embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0271] S501: Receive the feature data from the Internet of Things system;

[0272] S502: Determine a charging mode based on the characteristic data and a pre-trained decision tree model;

[0273] S503: generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode;

[0274] S504: Generate a charging control strategy using the charging mode and the charging parameters and send the strategy to the charging processing module.

[0275] From the above description, it can be seen that the electrostatic sorting method and device based on the Internet of Things and artificial intelligence provided by this application realizes the adaptive adjustment of electric field, voltage and charging mode, while ensuring the sorting accuracy, reducing energy consumption by 20%-30%, and improving the intelligence level of equipment, maintenance efficiency and long-term operation stability. By receiving the characteristic data from the Internet of Things system, the material characteristic data can be acquired in real time, providing an accurate data basis for subsequent intelligent decision-making; the charging mode is determined according to the characteristic data and the pre-trained decision tree model, and the adapted charging mode can be intelligently identified to improve the pertinence and sorting adaptability of the charging method; the charging parameters are generated according to the pre-trained reinforcement learning model corresponding to the characteristic data and the charging mode, and the optimal charging parameters matching the material characteristics can be generated to ensure that the charging effect is stable and the energy efficiency is optimal; by generating a charging control strategy using the charging mode and the charging parameters and sending it to the charging processing module, a complete charging control strategy can be formed and issued for execution, realizing automatic control and fine adjustment of the charging process.

[0276] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatuses, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0277] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0278] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0279] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0280] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. An electrostatic sorting system based on the Internet of Things and artificial intelligence, characterized in that: include: Feeding system, receiving system, live processing module, electric field control module, artificial intelligence optimization system and Internet of Things system; The feeding system is connected to the charged processing module and is used to evenly feed the material into the electrostatic separation system; The Internet of Things system is connected to the artificial intelligence optimization system via network communication, and is used to collect characteristic data of the material and operating data of the electrostatic separation system and perform data processing and analysis on the characteristic data and the operating data; The artificial intelligence optimization system is used to generate optimal electric field adjustment instructions and electrification control strategies based on the received characteristic data and the operating data; The charging processing module is connected to the electric field control module and is connected to the artificial intelligence optimization system via a communication bus, and is used to adjust the charging mode and charging parameters of the material according to the received charging control strategy; The electric field control module is connected to the artificial intelligence optimization system via a communication bus and is used to optimize the electric field distribution according to the received electric field adjustment instructions; The material receiving system is connected to the electric field control module and is used to receive the material after electrostatic separation; The artificial intelligence optimization system is specifically used for: receiving the characteristic data from the Internet of Things system; Determining a charging mode based on the characteristic data and a pre-trained decision tree model; generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode; Generating a charging control strategy using the charging mode and the charging parameters and sending the strategy to the charging processing module; The artificial intelligence optimization system is also specifically used for: receiving the characteristic data and the operation data from the Internet of Things system; Performing simulation modeling on the electric field of the electrostatic separation system according to the operating data to obtain a numerical model of the electric field distribution; generating an electric field distribution state according to the electric field distribution numerical model; generating electric field distribution control parameters according to the characteristic data, the electric field distribution state, and a pre-built reinforcement learning agent that calls the electric field distribution numerical model; An electric field adjustment instruction is generated using the electric field distribution control parameter and sent to the electric field control module.

2. The electrostatic separation system based on Internet of Things and artificial intelligence according to claim 1 is characterized in that: The Internet of Things system includes a sensor module, an Internet of Things communication module and a cloud platform; The sensor module is used to collect characteristic data of the material and operating data of the electrostatic separation system; The Internet of Things communication module is used to receive the feature data and the operating data and send them to the artificial intelligence optimization system and the cloud platform; The cloud platform is used to process and analyze the received feature data and operation data.

3. The electrostatic separation system based on Internet of Things and artificial intelligence according to claim 1 is characterized in that: Also includes: Safety intelligent management system; The safety intelligent management system is connected to the Internet of Things system and the artificial intelligence optimization system, and is used to identify the abnormal state and failure trend of the electrostatic sorting system based on the received operation data, and send the identification results to the artificial intelligence optimization system to adjust the operation strategy.

4. The electrostatic separation system based on Internet of Things and artificial intelligence according to claim 3 is characterized in that: The security intelligent management system is specifically used for: receiving the operating data from the Internet of Things system; Identifying an abnormal state of the electrostatic separation system based on the operating data and a pre-trained anomaly detection model; The anomaly detection model is obtained by performing unsupervised training on the historical operating data of the electrostatic separation system under normal operating conditions; Identifying a failure trend of the electrostatic separation system based on the operating data and a pre-trained fault prediction model; The fault prediction model is obtained by conducting supervised training on the historical operating data and fault records of the electrostatic separation system in different operating cycles; The recognition results are sent to the artificial intelligence optimization system to adjust the operation strategy.

5. The electrostatic separation system based on Internet of Things and artificial intelligence according to claim 1 is characterized in that: Also includes: Energy efficiency optimization system; The energy efficiency optimization system is connected to the Internet of Things system and the electric field control module, and is used to generate an optimization control strategy based on the received operating data to perform adaptive optimization and regulation on the electric field power.

6. The electrostatic separation system based on Internet of Things and artificial intelligence according to claim 5 is characterized in that: The energy efficiency optimization system is specifically used for: receiving the operating data from the Internet of Things system; generating an optimized control strategy through dynamic analysis based on the operating data; The optimization control strategy is sent to the electric field control module to perform adaptive optimization adjustment on the electric field power.

7. An electrostatic separation method based on the Internet of Things and artificial intelligence, applied to the electrostatic separation system based on the Internet of Things and artificial intelligence according to any one of claims 1 to 6, characterized in that: include: receiving the characteristic data from the Internet of Things system; Determining a charging mode based on the characteristic data and a pre-trained decision tree model; generating charging parameters according to the characteristic data and a pre-trained reinforcement learning model corresponding to the charging mode; A charging control strategy is generated using the charging mode and the charging parameters and sent to the charging processing module.

8. The electrostatic separation method based on Internet of Things and artificial intelligence according to claim 7, characterized in that: Also includes: receiving the characteristic data and the operation data from the Internet of Things system; Performing simulation modeling on the electric field of the electrostatic separation system according to the operating data to obtain a numerical model of the electric field distribution; generating an electric field distribution state according to the electric field distribution numerical model; generating electric field distribution control parameters according to the characteristic data, the electric field distribution state, and a pre-built reinforcement learning agent that calls the electric field distribution numerical model; An electric field adjustment instruction is generated using the electric field distribution control parameter and sent to the electric field control module.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 7 to 8 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 7 to 8 is implemented.

11. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 7 to 8 is implemented.

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