Intelligent control system for a dahuripae radix harvester dust falling device

Abstract

The application discloses a kind of intelligent control systems of Baizhi harvester dust falling equipment, it is related to the field of intelligent control system, including: the multi-source perception layer of multiple-source data, the intelligent decision-making layer of intelligent decision-making based on multiple-source data and the precise execution layer of execution control based on decision instruction, dust parameter can be accurately obtained based on multiple-source data, dust intensity index and diffusion trend index are fused with real-time feedback parameter, generate adaptive PID parameter calculation each partition control quantity, realize the fusion of prediction and real-time feedback data Generation accurate control instruction of each zone, realize the accurate dust control of each zone, realize the effective and accurate adaptability dust falling optimization of each partition, can realize the trinity of prediction-response-optimization Intelligent dust control, the response speed of the application is improved: pre-starting makes dust peak concentration greatly reduce, anti-interference ability enhances: prediction parameter buffer impact working condition impact, control fluctuation is greatly reduced, energy efficiency optimization can effectively save water.

Description

An intelligent control system for dust suppression equipment in an angelica harvester Technical Field

[0001] This invention relates to the field of intelligent control systems, and more specifically, to an intelligent control system for dust suppression equipment in an angelica harvester. Background Technology

[0002] The harvesting process of Angelica dahurica (digging, removing soil, and transporting) generates a large amount of mixed dust (soil particles, root debris, and dried stem and leaf powder), which is highly concentrated and fine in size (PM10, PM2.5). This seriously endangers the respiratory health of operators (easily causing pneumoconiosis, etc.), pollutes the environment, and reduces visibility during operations.

[0003] The existing solution is to use spray / spray equipment as dust suppression equipment during the harvesting of Angelica dahurica. However, the existing spray dust suppression equipment uses a fixed amount of water, which leads to water waste and excessive moisture content in the rhizomes, which can easily cause mold growth in Angelica dahurica.

[0004] The existing dust suppression spraying system is controlled by turning it on at the start of harvest and turning it off after harvesting is complete.

[0005] The above control method has the following technical problems:

[0006] Dust control lag: Traditional single dust sensors have a certain response delay and cannot suppress the dust bursts during excavation. Fixed spraying cannot match the dynamic changes in dust concentration.

[0007] Damage to the quality of medicinal materials: Traditional high-pressure water mist impact leads to an increased rate of root and stem breakage. Furthermore, existing spray dust suppression equipment does not control the water temperature. In addition, Angelica dahurica is generally harvested in summer, and higher water temperatures can easily increase the loss of coumarin in Angelica dahurica. Continuous and uninterrupted fixed spraying can easily cause the water content of Angelica dahurica roots and stems to exceed the standard.

[0008] Poor environmental adaptability: Existing spray dust suppression equipment has a single control method that ignores the influence of wind speed / direction on dust diffusion, resulting in poor dust suppression effect due to weather conditions.

[0009] Low energy efficiency: Existing dust suppression spraying equipment uses continuous large-volume water spraying, which wastes water resources and energy. Summary of the Invention

[0010] The purpose of this invention is to provide an intelligent control system for dust suppression equipment in angelica harvesters. This intelligent control system can effectively and accurately control the dust generated during the angelica harvesting process, effectively protect the angelica, adapt well to the environment, and reduce the amount of water used for dust suppression.

[0011] To achieve the above-mentioned objectives, this invention provides an intelligent control system for dust suppression equipment in angelica harvesters, the system comprising:

[0012] The multi-source sensing layer includes:

[0013] The environmental parameter acquisition unit is used to obtain environmental parameters in real time, including wind speed, wind direction, and air temperature and humidity.

[0014] The equipment status monitoring unit is used to obtain the equipment status parameters of the harvester in real time, including the harvester's travel speed and digging depth;

[0015] The crop status recognition unit is used to identify the density of Angelica dahurica plants and the exposure rate of rhizomes through machine vision;

[0016] Dust monitoring unit, used for real-time multi-point detection of dust concentration and dust organic carbon content;

[0017] The medicinal herb quality unit is used to obtain crop condition data, including the moisture content of Angelica dahurica rhizome.

[0018] The intelligent decision-making layer includes:

[0019] The dust prediction module is used to output prediction parameters based on the environmental parameters, equipment status parameters, crop status data, and dust diffusion dynamic model, including: dust intensity index and diffusion trend index;

[0020] The fusion control module is used to fuse predicted parameters with real-time feedback parameters to generate adaptive PID parameters; the real-time feedback parameters include dust concentration, wind speed, and the harvester's travel speed.

[0021] The partitioning module is used to divide the area to be dusted into multiple partitions, including: the excavator shovel area, the conveyor belt area, and the control panel breathing belt area;

[0022] The partition control module is used to calculate the control quantity for each partition based on the adaptive PID parameters;

[0023] The collaborative optimization module is used to convert the control variables of each partition into physical execution instructions to generate control instructions for each partition.

[0024] The precise execution layer includes:

[0025] The zoned spray unit is used to adjust the spray flow rate, droplet size, and spray angle of each zone according to control commands.

[0026] Wind curtain barrier unit, used to generate directional airflow barrier that is linked to wind direction;

[0027] The bio-enzyme addition unit is used to inject a dust suppressant containing cellulase into the spray system when the organic carbon content of the dust is greater than the organic dust concentration threshold.

[0028] The equipment linkage unit is used to adjust the harvester's travel speed based on the dust intensity index.

[0029] The principle of the intelligent control system in this invention is as follows: Unlike traditional single-sensor on / off control methods, this invention constructs a dust prediction module that can predict the dust intensity index and diffusion trend index based on environmental parameters, equipment status parameters, crop status data, and a dust diffusion dynamic model. This solves the problem of lag in traditional dust control. Furthermore, this invention considers multi-source data, including environmental parameters, equipment status parameters, and crop status data. Based on this multi-source data, dust parameters can be accurately obtained. The dust intensity index and diffusion trend index are fused with real-time feedback parameters to generate adaptive PID parameters. These adaptive PID parameters calculate the control quantities for each zone, achieving accurate generation of control parameters through the fusion of prediction and real-time feedback data. This invention provides control commands for each zone, enabling accurate dust control in each zone. Traditional methods do not consider the influence of various environmental factors or perform zoned control. This invention takes into account the different environmental conditions of each zone and combines predicted and real-time feedback data to generate accurate dust control commands for each zone. This achieves effective and accurate adaptive dust control optimization for each zone, realizing intelligent dust control integrating prediction, response, and optimization. Compared with traditional control methods, this invention improves response speed: pre-start significantly reduces peak dust concentration (compared to pure feedback control), enhances disturbance rejection capability: predicted parameters buffer sudden operating condition impacts, significantly reducing control fluctuations, and optimizes energy efficiency: fused control is more effective in saving water than traditional PID control.

[0030] This invention utilizes a partitioning module to divide the area to be dusted into multiple partitions, thereby achieving partition control and improving the dust reduction effect of each partition.

[0031] This invention uses fluid mechanics and machine learning to construct a diffusion model to predict dust, utilizes time-varying weighted fusion prediction and real-time feedback to achieve fusion control, uses water content-constrained resource allocation to achieve collaborative optimization, and achieves dynamic anti-disturbance capability through wind curtain angle adaptation and travel speed linkage.

[0032] When Angelica dahurica is harvested, it easily produces root debris containing a large amount of cellulose fibers. Traditional spraying only wets the surface. To solve this problem, this invention designs a bio-enzyme addition unit. This unit targets organic dust through enzymatic penetration: cellulase reaches the micropores inside the fibers, causing them to become brittle and break. The core of this invention lies in altering the physical properties of the dust through bio-enzymatic hydrolysis, thus inhibiting dust dispersion at its source. Compared with traditional spraying using only water, this method significantly improves the dust suppression effect on organic dust.

[0033] Preferably, the intelligent decision-making layer is also used to construct a dynamic model of dust diffusion, specifically including:

[0034] Establish the mass conservation equation:

[0035] ;

[0036] Establish the momentum conservation equation:

[0037] ;

[0038] Where ρ is the air density and t is time. For the gradient operator, v 矢 Let F be the air velocity vector, p be the pressure, μ be the aerodynamic viscosity, and F be the air velocity vector. 矢 The external force per unit volume For partial derivative operators, The divergence of the gradient;

[0039] Establish the motion equations of dust particles:

[0040] dv p矢 / dt=(v 矢 -v p矢 3μC d ·Re / 4ρ p d p +g 矢 ;

[0041] Among them, v p矢 ρ is the velocity of the dust particles. p d represents the density of dust particles. p C is the diameter of the dust particles. d Where Re is the drag coefficient, Re is the Reynolds number, and g is the drag coefficient. 矢 It is the vector of gravitational acceleration;

[0042] Establish a dust source intensity model:

[0043] Q s =k1·(1-θ) k2 ·v m k3 ·e -k4Ф ;

[0044] Among them, Q s Let θ be the dust source intensity, θ be the soil moisture content, and v be the soil moisture content. m The speed of the harvester is Ф, the density of Angelica dahurica plants is Ф, and k1, k2, k3 and k4 are model calibration coefficients.

[0045] Establishing a turbulence model includes:

[0046] Turbulent kinetic energy equation:

[0047] ;

[0048] Turbulent dissipation rate equation:

[0049] ;

[0050] Where k is the turbulent kinetic energy, ε is the turbulent dissipation rate, and μ t G is the turbulent viscosity. k For the turbulent kinetic energy generation term, σ k C 1ε C 2ε and σ ε All are model constants;

[0051] A set of equations was established based on the mass conservation equation, momentum conservation equation, dust particle motion equation, dust source intensity model, and turbulence model;

[0052] Solving the above system of equations, the dust diffusion dynamic model outputs: dust intensity index, diffusion trend index, and dominant dust type.

[0053] Preferably, the predicted parameters are fused with the real-time feedback parameters to generate adaptive PID parameters, specifically including:

[0054] The predicted parameters are fused with the real-time feedback parameters using the following formula:

[0055] ;

[0056] ;

[0057] ;

[0058] Where γ(t) is the time-varying weighting function, γ(t) = 0.8·e −0.5t t is time, K p (t) is the time-varying proportional coefficient, K i (t) represents the time-varying integral coefficient, K d (t) represents the time-varying differential coefficients. The proportional parameter is obtained through fuzzy inference based on real-time feedback parameters. These are the integral parameters obtained through fuzzy inference based on real-time feedback parameters. K is the differential parameter obtained through fuzzy inference based on real-time feedback parameters. p0 K is the reference value for the proportional parameter. i0 K is the baseline value for the integration parameter. d0 I is the reference value for the differential parameter. p (t) represents the dust intensity index at the current moment. D represents the maximum value of the dust intensity index. p (t) is the diffusion trend index, and β is the diffusion influence coefficient.

[0059] Preferably, the calculation method for the control quantities of each zone is as follows:

[0060] ;

[0061] e i (t)=r i -C i (t);

[0062] Among them, u i (t) represents the partition control variable, e i (t) represents the concentration deviation. For the cumulative deviation integral, τ is the integration time variable, e i (τ) is the time-dependent concentration deviation function, dτ is the time differential component, and de i (t) / dt is the rate of change of deviation, r i For the target concentration, C i (t) represents the measured concentration.

[0063] Preferably, the dust suppression equipment for the angelica harvester includes a spray system. The spray system uses gas-liquid two-phase nozzles and is equipped with a root position detection sensor. When the sensor detects the root passing through, the zoned spray unit is also used to control and reduce the water flow rate of the corresponding nozzle and increase the gas ratio. Angelica roots are easily broken, and traditional high-pressure water mist impact may cause mechanical damage. This invention achieves flexible gas-mist mixing spray by using gas-liquid two-phase nozzles, achieving dust suppression with low impact force and high coverage.

[0064] Preferably, the spray system includes a high-voltage electrostatic generator for charging the droplets, and the zoned spray unit is also used to control the start and stop of the high-voltage electrostatic generator. The high-voltage electrostatic generator is integrated at the nozzle tip to give the droplets a positive charge, which strongly adsorbs and agglomerates the negatively charged organic dust.

[0065] Preferably, the spray system includes a water tank, which contains a water temperature control device. The intelligent control system further generates water temperature control commands and droplet volume control commands. Based on the water temperature control commands, the system controls the water temperature control device to maintain the water temperature within a range greater than or equal to 8°C and less than or equal to 12°C. Based on the droplet volume control commands, the system controls the droplet volume median diameter within a range greater than or equal to 80 μm and less than or equal to 120 μm. Water-soluble active ingredients (such as coumarin) in Angelica dahurica may be lost during spraying. This invention uses an intelligent control system to control the water temperature control device, maintaining the water temperature at 8-12°C and reducing the solubility of these components.

[0066] Preferably, the spraying system includes a body tilt sensor. When the body tilt angle exceeds a threshold, the intelligent control system controls to increase the spray angle and flow rate of the nozzles on the downhill side. Angelica dahurica is mostly grown in mountainous areas, and dust spreads downwards during slope operations. Installing a tilt sensor automatically increases the spray coverage of the nozzles on the downhill side when a large slope is detected.

[0067] Preferably, since the amount of dust generated in the excavation area is greater than that in the conveying area, the partitioned spray unit is also used to control the droplet volume in the excavation shovel area to be greater than that in the conveyor belt area. Since angelica in the conveying area is prone to generating organic dust, it is also used to control the droplets in the conveyor belt area to be charged. Since there are corresponding operators on the operating platform, in order to protect the health of the operators, it is also used to generate a directional airflow barrier linked to the wind direction in the breathing zone area of ​​the operating platform.

[0068] Preferably, the dust suppression equipment for the Angelica dahurica harvester includes a spray system. The nozzles corresponding to the conveyor belt area in the spray system are coaxial dual-cavity nozzles. Each coaxial dual-cavity nozzle includes: an inner spray cavity, an outer annular gap, a guide cover, and a shell. The guide cover is fixed to the nozzle's front end. The outer annular gap is between the shell and the inner spray cavity. The inner spray cavity and the outer annular gap are coaxial. The inner spray cavity is connected to the outlet of a plasma activator. The inlet of the plasma activator is connected to the outlet of a sulfur agent storage tank via a metering pump, used to dissociate liquid sulfur agent into SO2 free radicals. The outer annular gap is connected to the outlet of a nanobubble generator. The inlet of the nanobubble generator is connected to the outlet of a drug-loaded water tank, which contains microencapsulated sulfur and an anti-blackening synergist. The intelligent control system also includes a detection unit. The detection unit detects whether Angelica dahurica is present in the conveyor belt area. If present, the intelligent control system controls the opening of the coaxial dual-cavity nozzle; if absent, the intelligent control system controls the closing of the coaxial dual-cavity nozzle.

[0069] Traditional dust suppression equipment for Angelica dahurica harvesters uses a spray system, which inevitably leaves a significant amount of water mist residue on the dahurica, making it prone to mold growth. To achieve both dust suppression and mold removal, this application designs a coaxial dual-chamber nozzle. This solution achieves a breakthrough by using coaxial dual atomization (integrated dust suppression and sulfur fumigation), microcapsule slow release (precise mold prevention), and plasma activation (enhanced efficiency and reduced residue). It solves the problem of root blackening caused by water exposure during interrupted harvesting of Angelica dahurica, transforming passive dust suppression into active protection. It simultaneously completes the initial processing and mold prevention of the medicinal material during the dust control stage. Atomization-sulfur fumigation synergy: The dual-channel nozzle achieves micron-level sulfur agent encapsulation of water mist, effectively improving the root penetration rate.

[0070] One or more technical solutions provided by this invention have at least the following technical effects or advantages:

[0071] The intelligent control system in this invention can effectively and accurately control the dust generated during the harvesting process of Angelica dahurica, effectively protect Angelica dahurica, adapt well to the environment, and reduce the amount of water used for dust suppression. Attached Figure Description

[0072] The accompanying drawings, which are provided to further illustrate embodiments of the invention and constitute a part of this invention, are not intended to limit the scope of the invention.

[0073] Figure 1 is a schematic diagram of the composition of an intelligent control system for a dust suppression device used in an Angelica dahurica harvester;

[0074] Figure 2 is a cross-sectional schematic diagram of a coaxial dual-chamber nozzle;

[0075] Among them, 1-inner spray cavity, 2-outer annular gap, 3-outer shell. Detailed Implementation

[0076] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, where there is no conflict, the embodiments of the present invention and the features thereof can be combined with each other.

[0077] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0078] Those skilled in the art should understand that, in the disclosure of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limiting this invention.

[0079] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0080] Example 1;

[0081] Please refer to Figure 1, which is a schematic diagram of the composition of an intelligent control system for a dust suppression device for an angelica harvester. This invention provides an intelligent control system for a dust suppression device for an angelica harvester, the system comprising:

[0082] The multi-source sensing layer includes:

[0083] The environmental parameter acquisition unit is used to obtain environmental parameters in real time, including wind speed, wind direction, and air temperature and humidity.

[0084] The equipment status monitoring unit is used to obtain the equipment status parameters of the harvester in real time, including the harvester's travel speed and digging depth;

[0085] The crop status recognition unit is used to identify the density of Angelica dahurica plants and the exposure rate of rhizomes through machine vision;

[0086] Dust monitoring unit, used for real-time multi-point detection of dust concentration and dust organic carbon content;

[0087] The medicinal herb quality unit is used to obtain crop condition data, including the moisture content of Angelica dahurica rhizome.

[0088] The intelligent decision-making layer includes:

[0089] The dust prediction module is used to output prediction parameters based on the environmental parameters, equipment status parameters, crop status data, and dust diffusion dynamic model, including: dust intensity index and diffusion trend index;

[0090] The fusion control module is used to fuse predicted parameters with real-time feedback parameters to generate adaptive PID parameters; the real-time feedback parameters include dust concentration, wind speed, and the harvester's travel speed.

[0091] The partitioning module is used to divide the area to be dusted into multiple partitions, including: the excavator shovel area, the conveyor belt area, and the control panel breathing belt area;

[0092] The partition control module is used to calculate the control quantity for each partition based on the adaptive PID parameters;

[0093] The collaborative optimization module is used to convert the control variables of each partition into physical execution instructions to generate control instructions for each partition.

[0094] The precise execution layer includes:

[0095] The zoned spray unit is used to adjust the spray flow rate, droplet size, and spray angle of each zone according to control commands.

[0096] Wind curtain barrier unit, used to generate directional airflow barrier that is linked to wind direction;

[0097] The bio-enzyme addition unit is used to inject a dust suppressant containing cellulase into the spray system when the organic carbon content of the dust is greater than the organic dust concentration threshold.

[0098] The equipment linkage unit is used to adjust the harvester's travel speed based on the dust intensity index.

[0099] The environmental parameter acquisition unit can employ environmental parameter sensors: wind speed and direction sensors: placed on top of the harvester or upwind of the working area to monitor real-time wind speed and direction, used to predict the direction and speed of dust diffusion. Temperature and humidity sensors: monitor ambient temperature and relative humidity. Humidity affects the hygroscopicity and settling effect of dust, while temperature may affect spray evaporation.

[0100] The dust monitoring unit can employ a dust concentration sensor array: using a laser scattering dust sensor or a beta-ray dust meter (high precision, fast response), with multiple points deployed at key dust sources (such as behind the excavator shovel, at the inlet / outlet of the conveyor belt, near the cleaning brush) and along the dust diffusion path (the operator's breathing zone, downwind area) to monitor PM2.5 / PM10 concentrations.

[0101] The equipment status monitoring unit can use machine status sensors, such as GPS / speed sensors (to acquire operating speed), digging depth sensors, etc., to correlate with dust generation intensity.

[0102] The crop status recognition unit can sense the growth status of Angelica dahurica by using a field image recognition system. A camera (visible light / near-infrared) is installed at the front of the harvester, combined with image recognition algorithms (CNN, etc.) to estimate plant density (sparse planting indicates more exposed soil and easier dust generation; excessive density indicates tangled roots and stems, resulting in more debris during soil clearing). It also identifies the degree of root and stem exposure (partial root and stem exposure minimizes soil disturbance during excavation).

[0103] Among them, the medicinal material quality unit can sample near-infrared spectroscopy sensors (NIR), such as industrial-grade online NIR probes. The installation position is 30cm directly above the conveyor belt, without contact with the rhizome. The working principle is: emit infrared light in the 1200-2400nm band → the OH bonds in the Angelica dahurica rhizome absorb specific wavelengths → analyze the reflectance spectrum to establish a water content model and obtain the water content of the Angelica dahurica rhizome.

[0104] The intelligent control system in this invention can be applied in the following scenarios: When the harvester begins digging, a dust concentration sensor behind the digging shovel detects a sharp increase in dust concentration, accompanied by moderate wind speed. The intelligent control system calculates the maximum spray volume required for that area based on rules, operates the water pump at high speed, and may adjust the nozzle angle in that area to aim at the center of the dust. Other areas maintain lower spray levels based on their respective sensor data. Another scenario is when a crosswind suddenly blows during operation. The intelligent control system increases the spray intensity in the downwind area based on the wind direction and may adjust the air curtain angle to block dust from blowing towards the control platform. Simultaneously, the spray intensity in the upwind area can be appropriately reduced. Yet another scenario is when the ambient humidity is high; the intelligent control system will tend to reduce the output when calculating control parameters because natural dust settling is more effective.

[0105] In this embodiment of the invention, the intelligent decision-making layer is further used to construct a dynamic model of dust diffusion, specifically including:

[0106] Establish the mass conservation equation:

[0107] ;

[0108] Establish the momentum conservation equation:

[0109] ;

[0110] Where ρ is the air density and t is time. For the gradient operator, v 矢 Let F be the air velocity vector, p be the pressure, μ be the aerodynamic viscosity, and F be the air velocity vector. 矢 The external force per unit volume For partial derivative operators, The divergence of the gradient;

[0111] Establish the motion equations of dust particles:

[0112] dv p矢 / dt=(v 矢 -v p矢 3μC d ·Re / 4ρ p d p +g 矢 ;

[0113] Among them, v p矢 ρ is the velocity of the dust particles. p d represents the density of dust particles. p C is the diameter of the dust particles. d Where Re is the drag coefficient, Re is the Reynolds number, and g is the drag coefficient. 矢 It is the vector of gravitational acceleration;

[0114] Establish a dust source intensity model:

[0115] Q s =k1·(1-θ) k2 ·v m k3 ·e -k4Ф ;

[0116] Among them, Q s Let θ be the dust source intensity, θ be the soil moisture content, and v be the soil moisture content. m The speed of the harvester is Ф, the density of Angelica dahurica plants is Ф, and k1, k2, k3 and k4 are model calibration coefficients.

[0117] Establishing a turbulence model includes:

[0118] Turbulent kinetic energy equation:

[0119] ;

[0120] Turbulent dissipation rate equation:

[0121] ;

[0122] Where k is the turbulent kinetic energy, ε is the turbulent dissipation rate, and μ t G is the turbulent viscosity. k For the turbulent kinetic energy generation term, σ k C 1ε C 2ε and σ ε All are model constants;

[0123] A set of equations was established based on the mass conservation equation, momentum conservation equation, dust particle motion equation, dust source intensity model, and turbulence model;

[0124] Solving the above system of equations, the dust diffusion dynamic model outputs: dust intensity index, diffusion trend index, and dominant dust type.

[0125] In this embodiment of the invention, the predicted parameters and real-time feedback parameters are fused to generate adaptive PID parameters, specifically including:

[0126] The predicted parameters are fused with the real-time feedback parameters using the following formula:

[0127] ;

[0128] ;

[0129] ;

[0130] Where γ(t) is a time-varying weight function used to control the weights of feedback and prediction, γ(t) = 0.8·e −0.5t t is time, K p(t) is the time-varying proportional coefficient, K i (t) represents the time-varying integral coefficient, K d (t) represents the time-varying differential coefficients. The proportional parameter is obtained through fuzzy inference based on real-time feedback parameters. These are the integral parameters obtained through fuzzy inference based on real-time feedback parameters. The aforementioned three fuzzy PID parameters are differential parameters obtained through fuzzy inference based on real-time feedback parameters. These parameters are based on the output of the fuzzy controller with real-time feedback, K. p0 K is the reference value for the proportional parameter. i0 K is the baseline value for the integration parameter. d0 The reference values ​​for the differential parameters are the initial system parameters, obtained through experimental calibration. p (t) represents the dust intensity index at the current moment. D represents the maximum value of the dust intensity index and is the normalized baseline value. p (t) is the diffusion trend index, and β is the diffusion influence coefficient, which is used to adjust the influence of diffusion on the integral term.

[0131] In this embodiment of the invention, the calculation method for each partition control quantity is as follows:

[0132] ;

[0133] e i (t)=r i -C i (t);

[0134] Among them, u i (t) represents the partition control variable, e i (t) represents the concentration deviation. For the cumulative deviation integral, τ is the integration time variable, e i (τ) is the time-dependent concentration deviation function, dτ is the time differential component, and de i (t) / dt is the rate of change of deviation, r i For the target concentration, C i (t) represents the measured concentration.

[0135] In this embodiment, the time-varying fusion mechanism can be understood as follows: Initial stage (t=0): → mainly based on feedback control; during operation: exponential decay → prediction weight gradually increases; stable state → completely dominated by prediction.

[0136] In this embodiment of the invention, the dust suppression equipment for the Angelica dahurica harvester includes a spray system. The spray system uses gas-liquid two-phase nozzles and is equipped with a root and stem position detection sensor. When the sensor detects the passage of roots and stems, the zoned spray unit is also used to control the reduction of water flow and increase the gas ratio of the corresponding nozzles. The applicant's research found that Angelica dahurica roots and stems are prone to breakage, and traditional high-pressure water mist impact may cause mechanical damage. Solution: Flexible air-mist mixing nozzles: Using low-pressure air-assisted atomization technology (such as the Lechler AIRTEC series), compressed air tears the water flow into ultra-fine droplets (particle size <50μm) to achieve low-impact, high-encapsulation dust suppression. Root and stem avoidance spray logic: An infrared photoelectric sensor array is installed above the conveyor belt to detect the root and stem position in real time. When roots and stems pass densely, the intelligent controller automatically reduces the water pressure of the corresponding area nozzles (or switches to pure air mode) to avoid direct impact; high-pressure spray resumes during the interval between roots and stems.

[0137] In this embodiment of the invention, the spray system includes a high-voltage electrostatic generator for charging the droplets. The zoned spray unit also controls the start and stop of the high-voltage electrostatic generator. The applicant's research found that Angelica dahurica dust contains a large amount of dried root / stem / leaf debris (high PM2.5 content), which is difficult to capture with conventional water mist. Solution: Charged water mist enhancement technology: Integrating a high-voltage electrostatic generator (output voltage 5-10kV) at the nozzle tip to charge the droplets with a positive charge, which strongly adsorbs and agglomerates with negatively charged organic dust. Targeted addition of bio-enzyme dust suppressant: Adding a biological agent containing cellulase (concentration 0.1%-0.5%) to accelerate the wetting and decomposition of plant debris. The controller dynamically adjusts the amount of enzyme added based on the organic carbon content (analyzed by laser scattering spectroscopy) fed back from the dust optical sensor.

[0138] In this embodiment of the invention, the spray system includes a water tank, which is equipped with a water temperature control device. The intelligent control system is also used to generate water temperature control commands and droplet volume control commands. Based on the water temperature control commands, the water temperature control device is controlled to maintain the water temperature within a range of greater than or equal to 8°C and less than or equal to 12°C. Based on the droplet volume control commands, the droplet volume median diameter is controlled within a range of greater than or equal to 80μm and less than or equal to 120μm.

[0139] When the water temperature is above 12℃, the impact on the medicinal components is an increased loss rate. Coumarin solubility increases by 2.3% with each 1℃ increase in temperature. When the water temperature is below 8℃, increased water viscosity leads to decreased atomization efficiency, impurity precipitation increases the risk of nozzle clogging, and decreased wettability reduces dust suppression efficiency. Therefore, the optimal water temperature range is 8-12℃, at which point the best balance is achieved: coumarin solubility ≤0.08g / L, and spray system efficiency ≥95%. Experimental data shows that the coumarin loss rate is only 3.2% at 12℃ compared to 21.7% at 25℃.

[0140] When the particle size is <80μm, the settling characteristics are: suspension time >30s, indicating easy dispersion; dust capture efficiency: PM2.5 capture rate <40%; Angelica dahurica adapts to fine mist penetration into root and stem fissures, leading to increased component loss. When the particle size is between 80-120μm, the settling characteristics are: settling time approximately 5-8s, indicating optimal retention; dust capture efficiency: PM10 capture rate >85%; Angelica dahurica adapts to mist droplets adhering to the epidermis, effectively blocking dust without penetration. When the particle size is >120μm, the settling characteristics are: settling time <2s, indicating premature settling; dust capture efficiency: dust escape rate >60%; Angelica dahurica adapts to impact force >0.15N, leading to increased root and stem damage rate.

[0141] The water temperature was maintained within the range of 8-12℃: above 12℃, the solubility of the effective components of Angelica dahurica (such as coumarin) increased significantly, with experiments showing a loss rate of up to 21.7% at 25℃; below 8℃, increased water viscosity led to a 56% decrease in atomization efficiency and easily caused nozzle clogging. The droplet size was controlled within the volume median diameter (Dv50) range of 80-120μm: when Dv50 < 80μm, the droplet suspension time was too long, resulting in a PM2.5 capture rate of <40%, and the fine mist easily penetrated into the roots and stems, causing component loss; when Dv50 > 120μm, premature droplet settling resulted in a dust escape rate > 60%, and an impact force > 0.15N could damage the roots and stems; a droplet size of 80-120μm achieved a PM10 capture rate of 85-96%, while ensuring a root and stem damage rate of <2%. Synergistic control effect: Under the combination of 10℃ water temperature and 100μm mist droplets, the system achieves a dust suppression efficiency of 96% and a component loss rate of <2%, reaching the optimal technical balance point.

[0142] Droplet control can be achieved by adjusting the droplet diameter through pressure.

[0143] In this embodiment of the invention, the spraying system includes a fuselage tilt sensor. When the fuselage tilt angle exceeds a threshold, the intelligent control system controls to increase the spray angle and flow rate of the nozzles on the downhill side. A dual-axis tilt sensor is installed on the frame. When a slope > 5° is detected, the spray coverage of the nozzles on the downhill side is automatically increased (by deflecting the nozzle angle and increasing the flow rate).

[0144] In this embodiment of the invention, the zoned spray unit is further configured to control the droplet volume in the excavator shovel area to be greater than the droplet volume in the conveyor belt area, control the droplets in the conveyor belt area to carry a charge, and generate a directional airflow barrier in the breathing zone area of ​​the operating platform that is linked to the wind direction.

[0145] In this embodiment of the invention, the dust suppression equipment for the angelica harvester includes a spray system. The nozzles corresponding to the conveyor belt area in the spray system are coaxial dual-cavity nozzles. Please refer to Figure 2, which is a cross-sectional schematic diagram of the coaxial dual-cavity nozzle. The coaxial dual-cavity nozzle includes: an inner spray cavity 1, an outer annular gap 2, a guide cover, and a shell 3. The guide cover is fixed to the front end of the nozzle. The outer annular gap is between the shell and the inner spray cavity. The inner spray cavity and the outer annular gap are coaxial. The inner spray cavity is connected to the outlet of the plasma activator, and the inlet of the plasma activator is... The metering pump is connected to the outlet of the sulfur agent storage tank to dissociate the liquid sulfur agent into SO2 free radicals; the outer annular gap is connected to the outlet of the nanobubble generator, and the inlet of the nanobubble generator is connected to the outlet of the drug-loaded water tank, which contains microencapsulated sulfur and an anti-blackening synergist; the intelligent control system also includes a detection unit, which is used to detect whether there is Angelica dahurica in the conveyor belt area. If there is, the intelligent control system controls the opening of the coaxial dual-cavity nozzle; if not, the intelligent control system controls the closing of the coaxial dual-cavity nozzle.

[0146] Structure of a coaxial dual-chamber nozzle:

[0147] Inner spray chamber: central channel, delivering sulfur-containing active substances (SO2 free radicals) after plasma activation treatment.

[0148] Outer annular gap: An annular channel surrounding the inner spray cavity, delivering nano-bubble water containing microencapsulated sulfur and synergists.

[0149] Guide cap: Located at the front end of the nozzle, it guides and optimizes the mixing and atomization pattern of the two fluids (such as forming a hollow cone or solid cone mist) to ensure coverage area and uniformity.

[0150] Outer shell: Encloses the entire structure, providing mechanical support and sealing.

[0151] Function: To achieve precise coaxial injection and preliminary mixing of two functional fluids, ensuring that the active substance and the sustained-release agent can act on the dust and angelica surface simultaneously and at the same point.

[0152] Inner fluid system (generating SO2 free radicals):

[0153] Path: Sulfur agent storage tank - metering pump - plasma activator - inner spray chamber of coaxial dual-cavity nozzle. Sulfur agent storage tank: Stores liquid sulfur-containing solutions (such as sulfite solutions, specific organic sulfur solutions, etc.). Metering pump: Precisely controls the flow rate of sulfur agent entering the plasma activator, ensuring controllable reaction efficiency and dosage. Plasma activator: Principle: Utilizes low-temperature plasma (rich in high-energy electrons, ions, and free radicals) generated by high-voltage discharge (such as corona discharge, dielectric barrier discharge, DBD). Function: High-energy particles bombard sulfur agent molecules (such as H2SO3, Na2SO3), causing them to dissociate and generate highly reactive SO2 free radicals and other reactive oxygen / nitrogen species (ROS / RNS). This is more efficient and has stronger reactivity than simply spraying SO2 gas or solution. Achieved effects: Generates instantly strong oxidizing SO2 free radicals, efficiently oxidizing and decomposing organic matter on the surface of dust particles and inhibiting microbial activity.

[0154] Outer fluid system (drug-loaded nanobubble water):

[0155] Pathway: Drug-loaded water tank - Nanobubble generator - Coaxial dual-cavity nozzle outer annular gap. Drug-loaded water tank: Composition: Microencapsulated sulfur: Core is elemental sulfur (S), outer layer encapsulated with slow-release materials (such as modified starch, chitosan, liposomes, etc.). Function: To form a continuously and slowly released sulfur protective layer on the surface of Angelica dahurica, providing long-lasting antibacterial and antioxidant effects, preventing blackening.

[0156] Anti-browning synergists typically include: Antioxidants: such as ascorbic acid (vitamin C), sodium isoascorbate, citric acid, etc., which inhibit enzymatic browning (e.g., polyphenol oxidase PPO activity). Chelating agents: such as disodium EDTA, phytic acid, etc., which chelate metal ions, which are catalysts for oxidation reactions. pH adjusters: maintaining a slightly acidic environment (pH ~4-5) to inhibit browning enzyme activity and microbial growth. Film-forming agents / wetting agents: such as chitosan, sodium alginate, food-grade surfactants, which enhance the adhesion and spreading of the medicinal solution on the surface of Angelica dahurica.

[0157] Nanobubble Generator: Principle: Through hydraulic cavitation, pressurized dissolution-depressurization release, electrolysis, etc., bubbles with diameters typically ranging from tens to hundreds of nanometers are generated in water (commonly air or specific gases such as nitrogen and carbon dioxide). Function: High-efficiency dust suppression: Nanobubbles possess characteristics such as a huge specific surface area, long residence time in water, high gas-liquid mass transfer efficiency, and negative charge (Zeta potential), enabling them to efficiently adsorb, coagulate, and encapsulate fine dust particles, significantly improving settling efficiency. The hydroxyl radicals (OH) generated during their rupture also have an auxiliary bactericidal and disinfecting effect. Carrier function: As a carrier, it uniformly delivers and attaches microencapsulated sulfur and synergists to the surface of angelica and dust particles. The bursting of nanobubbles promotes agent penetration. Achieved effects: Provides the main force for physical dust suppression and delivers slow-release bactericides (sulfur) and anti-browning compound agents, providing continuous protection.

[0158] The workflow is roughly as follows:

[0159] Angelica dahurica is harvested by a harvester and, after initial cleaning, falls onto a conveyor belt. A detection unit (such as an infrared sensor) monitors a designated area on the conveyor belt in real time. When Angelica dahurica is detected passing through this area, the sensor sends a signal to the intelligent control system (PLC). Based on logical judgment, the PLC immediately outputs control signals: It starts the metering pump to deliver sulfur to the plasma activator at a set flow rate. The plasma activator is then activated, activating the sulfur into an active fluid rich in SO2 free radicals. The water pump of the nanobubble generation system is activated, generating nanobubble water from the mixture in the drug-loaded water tank. Relevant valves are opened. The active fluid (containing SO2) is sprayed out through the inner spray chamber of the nozzle. The drug-loaded nanobubble water is sprayed out through the outer annular gap of the nozzle. The two fluids mix and atomize near the nozzle outlet (guided by the guide cap), spraying onto the Angelica dahurica on the conveyor belt and the raised dust. SO2 free radicals: Instantly and efficiently oxidize dust organic matter, inhibit microorganisms, and provide preliminary sterilization and antioxidant effects. Nanobubbles: Efficiently adsorb, condense, and settle dust. Microencapsulated sulfur / synergist: Adheres to the surface of Angelica dahurica, providing continuous and long-lasting antibacterial (prevents rot) and antioxidant (prevents enzymatic browning) protection. The sensor signal disappears once the Angelica dahurica leaves the detection area. The PLC outputs a signal to shut down the metering pump, plasma activator, nanobubble water system (water pump / air pump), and valves after a short delay (ensuring tail coverage). The system then enters standby mode, awaiting the next batch of Angelica dahurica to trigger the system.

[0160] Traditional water spraying for dust suppression is ineffective against fine dust and consumes a lot of water. This invention utilizes the huge specific surface area and surface charge of nanobubbles to significantly improve the adsorption, coagulation, and sedimentation efficiency of fine dust (especially PM10 and PM2.5), greatly reducing dust concentration in the work area, improving the working environment, and reducing environmental pollution and the risk of pneumoconiosis among workers.

[0161] This invention can effectively prevent the blackening (browning) of Angelica dahurica rhizomes: Angelica dahurica and other rhizome medicinal materials are prone to enzymatic browning (catalyzed by polyphenol oxidase) due to mechanical damage during harvesting and transportation, as well as rotting and browning due to microbial infection. This is a key problem causing post-harvest losses and quality decline.

[0162] This invention employs a dual sulfur protection mechanism:

[0163] Instant protection: Highly active SO2 free radicals can quickly inactivate browning enzymes (PPO), kill surface microorganisms, and provide antioxidant protection.

[0164] Long-lasting protection: Microencapsulated sulfur forms a protective layer on the surface of Angelica dahurica, continuously and slowly releasing elemental sulfur or SO2, providing a long-lasting antibacterial and antioxidant barrier to prevent blackening during storage and transportation.

[0165] Synergistic effects of synergists (antioxidants, chelating agents, pH adjusters, and film-forming agents) inhibit browning through multiple biochemical pathways, thereby enhancing the preservation effect.

[0166] This invention overcomes the drawbacks of traditional sulfur treatment:

[0167] Directly fumigating with sulfur or spraying with sulfite solution can easily lead to excessive sulfur residue, unpleasant odors, and equipment corrosion.

[0168] This invention utilizes plasma activation to generate highly reactive but relatively unstable free radicals (SO2), which decompose easily and leave minimal residue after action. Microencapsulation technology is used to control the release rate and dosage of sulfur, reducing the risk of residue. Combined with synergists, better preservation effects can be achieved with lower sulfur dosages.

[0169] Achieved effect:

[0170] Significantly improves the working environment: Dust concentration in the conveyor belt area is greatly reduced (estimated to be more than 50% lower than simple water spraying), visibility is improved, and worker health is better protected. Significantly improves the post-harvest quality of Angelica dahurica: Effectively inhibits the blackening (browning) of Angelica dahurica rhizomes, maintaining its white appearance. Reduces the rate of decay and minimizes post-harvest losses. Through plasma activation and microencapsulation technology, the dosage and mode of action of active sulfur can be more precisely controlled, helping to reduce the risk of excessive sulfur residue in the final product and meeting stricter food safety / medicinal herb safety standards.

[0171] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0172] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent control system for dust suppression equipment in an angelica harvester, characterized in that, The intelligent control system includes: a multi-source sensing layer, comprising: an environmental parameter acquisition unit for real-time acquisition of environmental parameters, including wind speed, wind direction, and air temperature and humidity; an equipment status monitoring unit for real-time acquisition of harvester equipment status parameters, including harvester travel speed and digging depth; a crop status recognition unit for machine vision recognition of Angelica dahurica plant density and root and stem exposure rate; a dust monitoring unit for real-time multi-point detection of dust concentration and dust organic carbon content; a medicinal material quality unit for acquiring crop status data, including Angelica dahurica root and stem moisture content; and an intelligent decision-making layer, comprising: a dust prediction module for outputting prediction parameters based on the environmental parameters, equipment status parameters, crop status data, and dust diffusion dynamic model, including dust intensity index and diffusion trend index; and a fusion control module for fusing the prediction parameters with real-time feedback parameters to generate adaptive PID parameters; the real-time feedback parameters include: The system includes: a dust concentration, wind speed, and harvester travel speed; a zoning module for dividing the dust suppression area into multiple zones, including: a digging shovel zone, a conveyor belt zone, and a control panel breathing zone; a zoning control module for calculating control quantities for each zone based on the adaptive PID parameters; a collaborative optimization module for converting the control quantities of each zone into physical execution instructions to generate control instructions for each zone; a precision execution layer, including: a zoning spray unit for adjusting the spray flow rate, droplet size, and spray angle of each zone according to the control instructions; an air curtain barrier unit for generating a directional airflow barrier linked to the wind direction; a bio-enzyme addition unit for injecting a dust suppressant containing cellulase into the spray system when the organic carbon content of the dust exceeds the organic dust concentration threshold; and an equipment linkage unit for adjusting the harvester travel speed based on the dust intensity index. The intelligent decision-making layer is also used to construct a dynamic dust diffusion model, specifically including: establishing a mass conservation equation. Establish the momentum conservation equation: Where ρ is the air density and t is time. For the gradient operator, v 矢 Let F be the air velocity vector, p be the pressure, μ be the aerodynamic viscosity, and F be the air velocity vector. 矢 The external force per unit volume For partial derivative operators, Let be the divergence of the gradient; establish the motion equation of the dust particles: dvp 矢 / dt=(v 矢 -vp 矢 3μC d ·Re / (4ρ p d p 2 )+g 矢 Among them, vp 矢 ρ is the velocity of the dust particles. p d represents the density of dust particles. p C is the diameter of the dust particles. d Where Re is the drag coefficient, Re is the Reynolds number, and g is the drag coefficient. 矢 Let Q be the gravitational acceleration vector; establish the dust source intensity model: Q s =k1·(1-θ) k2 ·v m k3 ·e -k4Ф ; where Q s Let θ be the dust source intensity, θ be the soil moisture content, and v be the soil moisture content. m Let Φ represent the harvester's travel speed, Ф represent the density of Angelica dahurica plants, and k1, k2, k3, and k4 be model calibration coefficients; k1, k2, k3, and k4 are all positive numbers. A turbulence model is established, including: turbulent kinetic energy equation: Turbulent dissipation rate equation: Where k is the turbulent kinetic energy, ε is the turbulent dissipation rate, and μ t G is the turbulent viscosity. k For the turbulent kinetic energy generation term, σ k C 1ε C 2ε and σ ε All are model constants, and μ is the aerodynamic viscosity. A set of equations is established based on the mass conservation equation, momentum conservation equation, dust particle motion equation, dust source intensity model, and turbulence model. Solving these equations yields the following outputs from the dust diffusion dynamic model: dust intensity index, diffusion trend index, and dominant dust type. The dust intensity index is based on Q calculated using the dust source intensity model. s The diffusion trend index is obtained by calculating the ratio of turbulent kinetic energy to turbulent dissipation rate based on the turbulence model; the dominant dust type is determined by combining the dust particle diameter calculated by the dust particle motion equation with the dust organic carbon content detected by the dust monitoring unit.

2. The intelligent control system for dust suppression equipment in an angelica harvester according to claim 1, characterized in that, The predicted parameters are fused with the real-time feedback parameters to generate adaptive PID parameters. Specifically, this involves fusing the predicted parameters with the real-time feedback parameters using the following formula: Where γ(t) is the time-varying weighting function, γ(t) = 0.8·e -0.5t t is time, K p (t) is the time-varying proportional coefficient, K i (t) represents the time-varying integral coefficient, K d (t) represents the time-varying differential coefficients. The proportional parameter is obtained through fuzzy inference based on real-time feedback parameters. These are the integral parameters obtained through fuzzy inference based on real-time feedback parameters. K is the differential parameter obtained through fuzzy inference based on real-time feedback parameters. p0 K is the reference value for the proportional parameter. i0 K is the baseline value for the integration parameter. d0 I is the reference value for the differential parameter. p (t) represents the dust intensity index at the current moment. D represents the maximum value of the dust intensity index. p (t) is the diffusion trend index, and β is the diffusion influence coefficient.

3. The intelligent control system for dust suppression equipment in an angelica harvester according to claim 2, characterized in that, The calculation method for the control quantities of each zone is as follows: e i (t)=r i -C i (t); where u i (t) represents the partition control variable, e i (t) represents the concentration deviation. For the cumulative deviation integral, τ is the integration time variable, e i (τ) is the time-dependent concentration deviation function, dτ is the time differential component, and de i (t) / dt is the rate of change of deviation, r i For the target concentration, C i (t) represents the measured concentration.

4. The intelligent control system for dust suppression equipment in an angelica harvester according to claim 1, characterized in that, The dust suppression equipment for angelica harvesters includes a spray system, which uses gas-liquid two-phase nozzles and is equipped with a root and stem position detection sensor. When the sensor detects the root and stem passing through, the zoned spray unit is also used to control the reduction of water flow and increase of gas ratio in the corresponding nozzle.

5. The intelligent control system for dust suppression equipment in an angelica harvester according to claim 4, characterized in that, The spray system includes a high-voltage electrostatic generator for charging the droplets, and the zoned spray unit is also used to control the start and stop of the high-voltage electrostatic generator.

6. The intelligent control system for dust suppression equipment in an angelica harvester according to claim 4, characterized in that, The spray system includes a water tank, which is equipped with a water temperature control device. The intelligent control system is also used to generate water temperature control commands and droplet volume control commands. Based on the water temperature control commands, the system controls the water temperature control device to maintain the water temperature within a range of greater than or equal to 8°C and less than or equal to 12°C. Based on the droplet volume control commands, the system controls the droplet volume median diameter within a range of greater than or equal to 80μm and less than or equal to 120μm.

7. The intelligent control system for dust suppression equipment in an angelica harvester according to claim 4, characterized in that, The spraying system includes a fuselage tilt sensor. When the fuselage tilt angle exceeds a threshold, the intelligent control system controls to increase the spray angle and flow rate of the nozzles in the downhill direction.

8. The intelligent control system for dust suppression equipment in an angelica harvester according to claim 5, characterized in that, The zoned spray unit is also used to control the droplet volume in the excavator shovel area to be greater than the droplet volume in the conveyor belt area, to control the droplets in the conveyor belt area to carry a charge, and to generate a directional airflow barrier linked to the wind direction in the breathing zone area of ​​the operating platform.

9. The intelligent control system for dust suppression equipment in an angelica harvester according to claim 1, characterized in that, The dust suppression equipment for the Angelica dahurica harvester includes a spray system. The nozzles corresponding to the conveyor belt area in the spray system are coaxial dual-cavity nozzles. Each coaxial dual-cavity nozzle includes: an inner spray cavity, an outer annular gap, a guide cover, and a shell. The guide cover is fixed to the nozzle's front end. The outer annular gap is between the shell and the inner spray cavity, and the inner spray cavity and the outer annular gap are coaxial. The inner spray cavity is connected to the outlet of a plasma activator. The inlet of the plasma activator is connected to the outlet of a sulfur agent storage tank via a metering pump, used to dissociate liquid sulfur agent into SO2 free radicals. The outer annular gap is connected to the outlet of a nanobubble generator. The inlet of the nanobubble generator is connected to the outlet of a dedicated drug-loaded water tank, which contains microencapsulated sulfur and an anti-blackening synergist. The intelligent control system also includes a detection unit. This detection unit detects the presence of Angelica dahurica in the conveyor belt area. If present, the intelligent control system controls the opening of the coaxial dual-cavity nozzle; otherwise, it controls the closing of the nozzle.

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