Method and platform for designing internal structure of low-temperature airflow nano crushing equipment for medicinal and edible plants

Abstract

The invention relates to the technical field of structural design, and discloses a method and platform for designing the internal structure of medical and edible plant low-temperature airflow nano crushing equipment, and the method comprises the following steps: firstly, obtaining characteristic parameters of a target crushing object, and generating a first design scheme; then optimizing the first design scheme to obtain a second design scheme; iteratively optimizing the second design scheme to obtain a final equipment internal structure design scheme; and finally, on the basis of the final scheme, an electrostatic monitoring and dynamic adjustment mechanism is integrated, real-time adaptive control is performed on the equipment, and an equipment protection mechanism is established. According to the method provided by the invention, the internal structure of the equipment is optimized by means of airflow field analysis, electrostatic aggregation prediction and the like, and an electrostatic monitoring and dynamic adjusting mechanism is introduced to realize intelligent control, so that the crushing efficiency and the powder quality of the medicinal and edible plants are improved, and meanwhile, the safety and the reliability of equipment operation are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of structural design, and more specifically, to a method and platform for designing the internal structure of a low-temperature airflow nano-crushing device for medicinal and edible plants. Background Art

[0002] In the in-depth development and utilization of medicinal and edible plants, ultrafine grinding technology, especially low-temperature airflow nano-grinding technology, plays a vital role. This technology can grind medicinal and edible plants to the nano level, greatly improving their bioavailability and efficacy. However, the existing internal structure design methods of grinding equipment often have problems such as lack of pertinence and insufficient refinement, and it is difficult to meet the requirements of efficient and stable grinding under different material properties and process requirements. Therefore, it is of great practical significance and application value to develop a method and platform for the internal structure design of grinding equipment that can be adaptively optimized according to material properties.

[0003] In the prior art, a patent application with publication number US20220398351A1 discloses a method and system for reverse design of micro-nano structures based on deep neural networks. This method uses deep neural networks to predict the electromagnetic response of micro-nano structures, and obtains the optimal structural parameters that meet the target through iterative optimization based on preset optical target parameters. Although this method has shown significant advantages in the field of micro-nano structure design and shortened the design time, it is aimed at micro-nano structures in the optical field and cannot be directly applied to edible and medicinal plant crushing equipment. More importantly, this technical solution does not take into account the characteristics of the material, especially the electrostatic characteristics of the material. During the airflow crushing process, the accumulation of static electricity will cause powder adhesion and agglomeration, affecting the crushing efficiency and product quality. However, this technical solution does not take into account the influence of static electricity at all, so it cannot solve the static electricity problem in the process of crushing edible and medicinal plants.

[0004] The Chinese patent application with the authorization announcement number CN214749601U proposes a dust generating device, including a supply mechanism, a mixing mechanism, a spreading mechanism and a containing bin, and also includes a negative pressure component. The device realizes quantitative and uniform spreading of dust by controlling the feeding and airflow, thereby improving the efficiency of the dust monitor performance test. However, the device is mainly used in the field of environmental protection equipment to simulate dust environments, and its design goal is to generate dust rather than crush materials. The core of the device is to control the generation and distribution of dust rather than to achieve an efficient crushing process. Its structural design also does not consider how to optimize the airflow field to achieve efficient crushing, and therefore cannot meet the needs of low-temperature airflow nano-crushing of medicinal and edible plants.

[0005] In summary, the existing technology cannot solve the problem of how to finely design the internal structure of the pulverizing chamber according to different material properties, especially electrostatic properties, during the low-temperature airflow nano-pulverization process of medicinal and edible plants, so as to avoid adhesion and agglomeration of powder due to static electricity, thereby improving the pulverization efficiency and product quality. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and platform for the internal structure design of a low-temperature airflow nano-crushing equipment for medicinal and edible plants. By obtaining the characteristic parameters of the crushing object, optimizing the equipment structure and introducing an electrostatic monitoring and dynamic adjustment mechanism, the present invention aims to solve the shortcomings of the prior art in terms of equipment intelligence and powder quality stability.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants includes:

[0009] Acquire characteristic parameters of the target crushing object, determine initial structural parameters of the equipment based on the characteristic parameters, and use the initial structural parameters of the equipment as first structural parameters; generate a first design scheme according to the first structural parameters; analyze and optimize the airflow field of the first design scheme to obtain a second airflow path; predict electrostatic aggregation and optimize the first design scheme to obtain a second conductive material covering scheme and a second negative ion placement scheme; combine the second airflow path, the second conductive material covering scheme and the second negative ion placement scheme to generate a second design scheme; iteratively optimize the second design scheme to obtain a final device internal structure design scheme;

[0010] On the basis of the final design of the internal structure of the equipment, an electrostatic monitoring and dynamic adjustment mechanism is integrated to perform real-time adaptive control of the equipment and establish an equipment protection mechanism.

[0011] Furthermore, the characteristic parameters of the target crushing object include target crushing particle size, target yield and material characteristics;

[0012] The obtaining of characteristic parameters of the target crushing object comprises:

[0013] Obtain the target crushing particle size and target yield of the target crushing object, and store them in the process requirement table in the equipment optimization database;

[0014] Obtaining material properties of the target crushing object, wherein the material properties include material density, material moisture content and material electrostatic properties, and storing the material properties in a material property table in an equipment optimization database;

[0015] Determining the initial structural parameters of the device based on the characteristic parameters includes:

[0016] Based on the process requirement table and the material property table, combined with the preset equipment structure parameter range, the initial structure parameters of the equipment are determined; the initial structure parameters include the crushing chamber space layout parameters, the airflow channel space layout parameters, the conductive material coverage ratio and the negative ion placement density; the crushing chamber space layout parameters include the size and geometry of the crushing chamber; the airflow channel space layout parameters include the cross-sectional shape, cross-sectional size, length, spatial position of the airflow channel, connection method of the airflow channel and bending angle of the airflow channel.

[0017] Further, generating a first design scheme according to the first structural parameter includes:

[0018] Determining a first airflow path of the pulverizing cavity according to the pulverizing cavity space layout parameters and the airflow channel space layout parameters in the initial structural parameters of the equipment;

[0019] Determining a first conductive material covering scheme according to a conductive material covering ratio in the initial structural parameters of the device;

[0020] Determine a first negative ion placement plan according to the negative ion placement density in the initial structural parameters of the device;

[0021] The first airflow path, the first conductive material covering scheme and the first negative ion placement scheme are combined to generate a first design scheme, and the first design scheme is stored in a device optimization database.

[0022] Further, determining the first airflow path of the pulverizing cavity includes:

[0023] Generate a three-dimensional solid model of the crushing cavity according to the spatial layout parameters of the crushing cavity, and perform geometric parameter annotation and size constraint setting;

[0024] According to the spatial layout parameters of the airflow channel, a three-dimensional solid model of the airflow channel is constructed, and assembled and integrated with the three-dimensional solid model of the pulverizing cavity to generate a three-dimensional assembly model of the pulverizing cavity;

[0025] In the three-dimensional assembly model of the grinding chamber, based on the gas-solid two-phase flow theory, by adjusting the spatial layout parameters of the airflow channel, the flow trajectory and velocity distribution of the airflow inside the grinding chamber are simulated, and the flow field uniformity is evaluated. The airflow channel layout scheme with the optimal gas-solid two-phase flow effect is selected to form the first airflow path.

[0026] Further, determining the first conductive material covering scheme includes:

[0027] The inner wall surface of the three-dimensional assembly model of the crushing cavity is divided into n1 regular geometric partitions, and the size of each geometric partition is determined according to the coverage ratio of the conductive material;

[0028] Determine the coverage position and coverage density of the conductive material layer, generate a three-dimensional solid model of the conductive material layer in each geometric partition, and constrain the three-dimensional solid model of the conductive material layer and the three-dimensional assembly model of the crushing cavity to obtain the three-dimensional assembly model of the conductive crushing cavity;

[0029] Based on the three-dimensional assembly model of the conductive pulverizing cavity, the electrostatic field simulation is carried out to calculate the electric field uniformity index inside the pulverizing cavity;

[0030] According to the electric field uniformity index inside the pulverizing cavity, the covering position and covering density of the conductive material layer are adjusted, and when the electric field uniformity index reaches a preset convergence condition, a first conductive material covering scheme is obtained.

[0031] Further, determining the first negative ion placement scheme includes:

[0032] According to the negative ion placement density, the number and placement spacing of the negative ion generators required inside the pulverizing chamber are calculated; according to the number and placement spacing of the negative ion generators, a placement position matrix of the negative ion generators is generated on the inner wall surface of the three-dimensional assembly model of the conductive pulverizing chamber;

[0033] At each placement point of the placement position dot matrix, a three-dimensional solid model of the negative ion generator is inserted, and the three-dimensional solid model of the negative ion generator is constrained and assembled with the three-dimensional assembly model of the conductive pulverizing cavity to obtain a three-dimensional assembly model of the negative ion integrated pulverizing cavity;

[0034] The optimal working parameter combination of the negative ion generator under different placement positions is obtained, and the optimal working parameter combination of the negative ion generator is assigned to each three-dimensional solid model of the negative ion generator in the three-dimensional assembly model of the negative ion integrated crushing cavity to form a first negative ion placement plan.

[0035] Furthermore, the airflow field analysis and optimization of the first design scheme to obtain the second airflow path includes:

[0036] According to the first design scheme, an airflow field analysis model is built;

[0037] According to the airflow field analysis model, first airflow field parameters are obtained, wherein the first airflow field parameters include velocity field, pressure field and turbulence intensity, and the first airflow field parameters are stored in a device optimization database;

[0038] Comparing the first airflow field parameter with the preset airflow field parameter to obtain a first airflow field deviation, wherein the first airflow field deviation includes a speed deviation, a pressure deviation, and a turbulence intensity deviation;

[0039] The first airflow path is optimized according to the first airflow field deviation to obtain a second airflow path.

[0040] Furthermore, the second conductive material covering scheme and the second negative ion placement scheme are obtained, including:

[0041] Based on the electrostatic characteristics of materials in the material property table, an electrostatic aggregation prediction model is constructed;

[0042] According to the first conductive material covering scheme, the first negative ion placement scheme and the electrostatic aggregation prediction model, a first powder prediction loss rate is obtained;

[0043] Comparing the first powder predicted loss rate with a preset target loss rate to obtain a first loss rate deviation;

[0044] The first conductive material covering scheme and the first negative ion placement scheme are optimized based on the first loss rate deviation to obtain the second conductive material covering scheme and the second negative ion placement scheme.

[0045] Furthermore, based on the final design of the internal structure of the equipment, the integrated electrostatic monitoring and dynamic adjustment mechanism includes:

[0046] On the basis of the final design of the internal structure of the equipment, an electrostatic monitoring unit and an electrostatic regulating unit are added; the electrostatic monitoring unit includes a data acquisition module, a data processing module and a data analysis module; the electrostatic regulating unit includes a controller and an actuator;

[0047] The real-time adaptive control of the device comprises:

[0048] During the operation of the equipment, the static electricity monitoring unit receives the data collected by the static electricity sensor in real time, processes and analyzes it, and identifies abnormal states of static electricity accumulation;

[0049] The electrostatic adjustment unit dynamically adjusts the working parameters of the negative ion generator according to the analysis results of the electrostatic monitoring unit;

[0050] The electrostatic adjustment unit feeds back the adjusted working parameters of the negative ion generator to the electrostatic monitoring unit to form a closed-loop control.

[0051] A platform for designing the internal structure of a low-temperature airflow nano-crushing device for medicinal and edible plants, which is used to implement the method for designing the internal structure of a low-temperature airflow nano-crushing device for medicinal and edible plants, and the platform includes:

[0052] A first design scheme generating module: used to obtain characteristic parameters of a target crushing object, determine initial structural parameters of the equipment based on the characteristic parameters, and use the initial structural parameters of the equipment as first structural parameters; and generate a first design scheme according to the first structural parameters;

[0053] Solution optimization module: used to analyze and optimize the airflow field of the first design solution to obtain the second airflow path; predict the electrostatic accumulation and optimize the solution of the first design solution to obtain the second conductive material covering solution and the second negative ion placement solution; combine the second airflow path, the second conductive material covering solution and the second negative ion placement solution to generate the second design solution; iteratively optimize the second design solution to obtain the final device internal structure design solution;

[0054] Adaptive control module: It is used to integrate electrostatic monitoring and dynamic adjustment mechanisms based on the final equipment internal structure design plan, perform real-time adaptive control of the equipment, and establish an equipment protection mechanism.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention obtains characteristic parameters of a target pulverized object, determines initial structural parameters of the device based on the characteristic parameters, generates a first design scheme, achieves matching of the internal structure of the device with the properties of the pulverized object, and improves pulverizing efficiency and powder quality.

[0057] The present invention adopts airflow field analysis and optimization technology, optimizes the airflow channel layout plan by simulating the flow trajectory and velocity distribution of the airflow inside the pulverizing cavity and evaluating the flow field uniformity, realizes the optimization of the gas-solid two-phase flow effect, and improves the pulverizing efficiency and energy utilization.

[0058] The present invention introduces an electrostatic aggregation prediction model and achieves uniform distribution of the electrostatic field inside the pulverizing chamber by optimizing the conductive material covering scheme and the negative ion placement scheme, effectively suppresses the electrostatic aggregation of powder, reduces the powder loss rate, and improves the powder dispersibility and preparation quality.

[0059] The present invention adopts an iterative optimization strategy, and through multiple rounds of airflow field analysis, electrostatic accumulation prediction and scheme optimization, continuously improves the internal structure design of the equipment, and finally obtains the design scheme with the best performance, thereby improving the design efficiency and success rate.

[0060] On the basis of the final design scheme, the present invention integrates electrostatic monitoring and dynamic adjustment mechanisms. By real-time monitoring of the electrostatic aggregation state during the crushing process and dynamically adjusting the working parameters of the negative ion generator according to the monitoring results, real-time adaptive control of the equipment operation state is achieved, thereby improving the intelligence level and operation reliability of the equipment.

[0061] The present invention constructs an abnormal state recognition model for electrostatic accumulation, continuously optimizes the model performance through an adaptive learning mechanism, realizes rapid diagnosis and early warning of abnormal state of electrostatic accumulation during equipment operation, and provides strong support for equipment maintenance and fault prevention.

[0062] The present invention establishes a complete equipment protection mechanism. By setting the safety threshold range of the negative ion generator's operating parameters and monitoring the equipment's operating parameters in real time, once an abnormal situation occurs, the preset protection measures are activated, effectively avoiding equipment damage and safety accidents caused by static electricity accumulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A flow chart of the method for designing the internal structure of the low-temperature airflow nano-crushing equipment for Chinese medicinal and edible plants of the present invention;

[0065] Figure 2 A flow chart of a method for determining initial structural parameters of a device in the present invention;

[0066] Figure 3 A flow chart of a method for generating a first design solution according to a first structural parameter in the present invention;

[0067] Figure 4 is a flow chart of a method for determining a first airflow path in the present invention;

[0068] Figure 5 A flow chart of a method for determining a first conductive material covering scheme in the present invention;

[0069] Figure 6 A flow chart of a method for analyzing and optimizing the airflow field of the first design solution to obtain a second airflow path in the present invention;

[0070] Figure 7 A functional module diagram of the platform designed for the internal structure of the low-temperature airflow nano-crushing equipment for Chinese medicinal and edible plants of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Example 1

[0073] See also Figure 1As shown, this embodiment provides a method for designing the internal structure of a low-temperature airflow nano-crushing device for medicinal and edible plants, including:

[0074] Step S1000, obtaining characteristic parameters of the target crushing object, determining initial structural parameters of the device based on the characteristic parameters, and using them as first structural parameters; generating a first design scheme according to the first structural parameters; analyzing and optimizing the airflow field of the first design scheme to obtain a second airflow path; predicting electrostatic aggregation and optimizing the scheme of the first design scheme to obtain a second conductive material covering scheme and a second negative ion placement scheme; combining the second airflow path, the second conductive material covering scheme and the second negative ion placement scheme to generate a second design scheme; iteratively optimizing the second design scheme to obtain a final device internal structure design scheme;

[0075] Furthermore, step S1000 includes:

[0076] Step S1100, acquiring characteristic parameters of the target crushing object, determining initial structural parameters of the equipment based on the characteristic parameters, and taking them as first structural parameters; the characteristic parameters include target crushing particle size, target output and material characteristics;

[0077] Furthermore, if Figure 2 As shown, step S1100 includes:

[0078] Step S1110, obtaining the target crushing particle size and target yield of the target crushing object, and storing them in the process requirement table in the equipment optimization database;

[0079] Specifically, the target pulverized particle size refers to the particle size distribution range of the material after pulverization, usually in microns (μm) or nanometers (nm). For example, for a certain medicinal and edible plant, the target particle size may be set to within 100 nanometers to ensure the efficient release of its active ingredients. The target output refers to the amount of material that the equipment can process per unit time, usually in kilograms per hour (kg / h) or tons per day (t / day). For example, the target output is set at 50 kg / hour to meet the needs of industrial production. The target particle size and output are the basic parameters for the design of pulverizing equipment, which directly affect the structural size, airflow design and power configuration of the equipment. Accurately obtaining these parameters helps to formulate a scientific design plan, avoid equipment that is too large or too small, and ensure production efficiency and product quality. By clarifying the target particle size and output, the appropriate pulverizing chamber size, airflow intensity and processing speed can be selected more specifically to avoid waste of resources. At the same time, the clarification of these parameters helps to optimize the airflow path and conductive material covering scheme in subsequent steps, and improve the overall performance of the equipment. For example, suppose the target crushing object is a certain Chinese herbal medicine, whose active ingredients have the best efficacy when they are below 100 nanometers. The target output is set at 50 kg / hour, which means that the equipment needs to maintain a stable processing speed and high particle size uniformity while crushing efficiently. These data are stored in the process requirements table to provide a clear basis for subsequent design.

[0080] Step S1120, obtaining material properties of the target crushing object, wherein the material properties include material density, material moisture content and material electrostatic properties, and storing the material properties in a material property table in the equipment optimization database;

[0081] Specifically, material density refers to the ratio of the mass to the volume of the material, usually expressed in grams per cubic centimeter (g / cm³). Materials with high density require greater energy input to overcome their internal structure during the pulverization process. Material moisture content refers to the proportion of water in the material, usually expressed as a percentage (%). Materials with high moisture content are prone to adhesion during the pulverization process, affecting the pulverization efficiency and equipment life. Material electrostatic properties refer to the ability of the material to generate and accumulate static electricity during the pulverization process, including parameters such as charge and conductivity. Static electricity accumulation may cause powder agglomeration, wall adhesion, and even discharge, affecting the pulverization effect and equipment safety. Material density and moisture content directly affect the energy demand and pulverization efficiency of the pulverization equipment. The electrostatic properties of the material involve the dispersion of the powder in the airflow and the safety of the equipment, and are an important basis for designing conductive material covering and negative ion placement solutions. Understanding the density and moisture content of the material can optimize the airflow design and power configuration of the equipment, ensuring the stability and efficiency of the pulverization process. Mastering the electrostatic properties of the material will help design a reasonable conductive material coverage ratio and negative ion placement density, reduce static electricity accumulation, prevent powder agglomeration and equipment damage, and improve product quality and equipment reliability. For example, a plant material has a density of 0.5 g / cm³, a moisture content of 10%, and an electrostatic charge of 5 μC / g. Based on these characteristics, the airflow velocity and conductive material coverage ratio of the equipment can be adjusted to ensure uniform dispersion of the powder in the airflow and prevent powder adhesion and discharge caused by static electricity accumulation.

[0082] Step S1130, based on the process requirement table and the material attribute table, combined with the preset equipment structure parameter range, determine the initial structure parameters of the equipment; the initial structure parameters include the crushing cavity space layout parameters, the airflow channel space layout parameters, the conductive material coverage ratio and the negative ion placement density; the crushing cavity space layout parameters include the size and geometric shape of the crushing cavity; the airflow channel space layout parameters include the cross-sectional shape, cross-sectional size, length, spatial position of the airflow channel, connection mode of the airflow channel and bending angle of the airflow channel;

[0083] Specifically, the size of the pulverizing chamber includes the length, width, and height of the pulverizing chamber, which are determined according to the target output and particle size. The geometric shape of the pulverizing chamber, such as cylindrical, cubic, etc., affects the airflow distribution and pulverizing effect. These parameters are closely related to the target output, particle size and material characteristics. The spatial layout parameters of the airflow channel include the cross-sectional shape, cross-sectional size, length, spatial position of the airflow channel, connection method of the airflow channel and bending angle of the airflow channel; the cross-sectional shape is circular, rectangular, elliptical, etc., to meet different flow requirements and structural strength requirements. The cross-sectional size defines the width, height and depth of the airflow channel to ensure that the airflow has sufficient kinetic energy and appropriate flow rate during the pulverizing process. The length refers to the total length of the airflow channel, which affects the distribution and energy loss of the airflow in the pulverizing chamber. The spatial position of the airflow channel includes the inlet and outlet positions, as well as the branch and confluence points; the branch and confluence points refer to the number of branches and the confluence method of the airflow channel, ensuring that the airflow can be evenly distributed to the entire pulverizing chamber to avoid excessively high or low local flow rates. The connection methods of the airflow channel include straight connection and curved connection, which affect the flow path and energy loss of the airflow. The bending angle of the airflow channel, such as 90 degrees, 45 degrees, etc., affects the turning and flow efficiency of the airflow. The conductive material coverage ratio refers to the ratio of the conductive material coverage area on the inner wall of the crushing chamber to the total inner wall area, which affects the distribution uniformity of the electrostatic field. The negative ion placement density refers to the placement density of the negative ion generator in the chamber, which affects the distribution of negative ions and the electrostatic suppression effect.

[0084] The determination of equipment structural parameters requires comprehensive consideration of material properties and process requirements to ensure that the equipment has good electrostatic control capabilities and crushing efficiency while meeting the production and particle size requirements. The preset structural parameter range provides a feasible boundary for the design to avoid blindness in the design process. By scientifically determining the spatial layout parameters of the crushing chamber and the airflow channel, the distribution of the airflow in the chamber can be optimized, and the crushing efficiency and particle size uniformity can be improved. At the same time, a reasonable conductive material coverage ratio and negative ion placement density can effectively inhibit static electricity accumulation, prevent powder agglomeration and equipment damage, and improve the stability and reliability of the equipment.

[0085] For example, assuming the target particle size is 100 nanometers and the output is 50 kg / hour. Based on the material density and moisture content, it is preliminarily determined that the crushing chamber is a cylinder with a diameter of 1 meter and a height of 2 meters. The airflow channel adopts a circular cross-section with a diameter of 0.2 meters and a length of 2 meters, which is arranged at the bottom and top of the cavity to ensure uniform distribution of the airflow. The conductive material coverage ratio is set to 70%, and the negative ion placement density is 10 negative ion generators per square meter. Such a design can effectively control static electricity accumulation and improve crushing efficiency while meeting the requirements of output and particle size.

[0086] Step S1140, storing the initial structural parameters of the device in the device optimization database as the first structural parameters.

[0087] Specifically, step S1140 systematically stores the initial structural parameters determined in step S1130, including the crushing chamber space layout parameters, the airflow channel space layout parameters, the conductive material coverage ratio and the negative ion placement density, in the equipment optimization database. The process requirement table and the material attribute table in the database are interrelated with the structural parameters to provide data support for subsequent design and optimization. The equipment optimization database is the data management core of the entire design process, ensuring that all design parameters and process requirements are effectively recorded and traced. The structured storage of the database facilitates the rapid retrieval and update of information, and supports subsequent design optimization and iteration processes. By systematically storing the initial structural parameters, the design parameters can be accessed and modified at any time, promoting the collaborative work of the design process. At the same time, the existence of the database provides a reliable data basis for airflow field analysis, electrostatic aggregation prediction and scheme optimization in subsequent steps, ensuring the scientificity and systematicness of the design process.

[0088] For example, after inputting the above initial structural parameters into the equipment optimization database, you can query the process requirement table to understand whether the current design meets the output and particle size requirements, and use the material property table to understand the density, moisture content and electrostatic properties of the material, and then adjust the airflow channel layout and conductive material coverage ratio to optimize equipment performance.

[0089] Through step S1100, the characteristic parameters of the target crushing object can be systematically acquired and analyzed, and the initial structural parameters of the equipment can be scientifically determined in combination with the material characteristics and process requirements. This process not only ensures the pertinence and effectiveness of the equipment design, but also improves the crushing efficiency, product quality and operational stability of the equipment by optimizing the airflow channel layout and electrostatic control measures. Systematic data management and scientific design methods lay a solid foundation for subsequent airflow field analysis and optimization, electrostatic aggregation prediction and solution optimization, and iterative optimization of the final equipment structure design, achieving a comprehensive improvement in equipment performance and reliability assurance.

[0090] Step S1200, generating a first design scheme according to the first structural parameter;

[0091] Furthermore, if Figure 3 As shown, step S1200 includes:

[0092] Step S1210, determining a first airflow path of the pulverizing chamber according to the pulverizing chamber space layout parameters and the airflow channel space layout parameters in the initial structural parameters of the device;

[0093] In the design process of low-temperature airflow nano-crushing equipment for medicinal and edible plants, determining the airflow path is a key step to ensure the crushing efficiency and product quality. The optimization of the airflow path not only affects the energy utilization efficiency during the crushing process, but is also directly related to the uniformity and fineness of the powder.

[0094] Furthermore, if Figure 4 As shown, step S1210 includes:

[0095] Step S1211, generating a three-dimensional solid model of the pulverizing cavity according to the spatial layout parameters of the pulverizing cavity, and marking geometric parameters and setting size constraints;

[0096] Specifically, computer-aided design (CAD) software is used to create a detailed three-dimensional model based on the spatial layout parameters (such as size and geometry) of the pulverizing chamber. The model should accurately reflect the actual structure of the chamber, including the inlet, outlet and internal structural features. Key geometric parameters such as the length, width, height and wall thickness of the chamber are marked in the three-dimensional model. These parameters are the basis for subsequent airflow simulation and optimization, ensuring the accuracy and operability of the model. According to the design requirements and manufacturing process, the key dimensions in the model are constrained to ensure that the dimensions of each part meet the actual manufacturing capabilities and design specifications during the design process. Generating an accurate three-dimensional solid model is a prerequisite for airflow path optimization. Geometric parameter annotation and dimensional constraints not only improve the accuracy of the model, but also provide a reliable data basis for subsequent fluid dynamics simulation. Through detailed annotation and constraints, airflow simulation deviations caused by inaccurate dimensions during the design process can be avoided. By accurately generating and annotating the three-dimensional solid model of the pulverizing chamber, the flow of airflow in the chamber can be more accurately simulated, ensuring the scientificity and effectiveness of subsequent optimization steps. This process reduces design errors, improves the efficiency of airflow path optimization and the reliability of results, thereby improving the pulverization performance and product quality of the entire equipment.

[0097] For example, suppose you need to design a cylindrical crushing chamber with a diameter of 1 meter and a height of 2 meters. After generating a 3D model through CAD software, mark out the various dimensional parameters of the chamber, such as diameter, height, wall thickness (for example, 5 mm), and set dimensional constraints to ensure that the model will not have problems during the design process due to the size exceeding the manufacturing capacity. Such a model provides an accurate data basis for subsequent airflow simulation.

[0098] Step S1212, constructing a three-dimensional solid model of the airflow channel according to the spatial layout parameters of the airflow channel, and assembling and integrating it with the three-dimensional solid model of the pulverizing cavity to generate a three-dimensional assembly model of the pulverizing cavity;

[0099] Specifically, according to the spatial layout parameters of the airflow channel (such as cross-sectional shape, size, length, spatial position, connection method and bending angle), a detailed three-dimensional model of the airflow channel is constructed using CAD software. The design of the airflow channel needs to take into account the flow characteristics and crushing efficiency of the airflow to ensure that the airflow can be evenly distributed throughout the crushing chamber. The three-dimensional model of the airflow channel is assembled and integrated with the three-dimensional model of the crushing chamber to form a complete three-dimensional assembly model of the crushing chamber. This step requires ensuring that the interface between the airflow channel and the chamber is accurately docked to avoid airflow leakage or blockage. After assembly and integration, the three-dimensional assembly model is preliminarily checked to ensure that the layout of the airflow channel is reasonable and meets the design requirements, and to eliminate possible geometric conflicts or structural defects. The design of the airflow channel directly affects the distribution and flow characteristics of the airflow in the crushing chamber. By constructing a three-dimensional solid model of the airflow channel and accurately assembling it with the crushing chamber model, the rationality and effectiveness of the airflow path can be ensured. In addition, the assembled and integrated model provides a complete structural basis for subsequent fluid dynamics simulations. By constructing and integrating the three-dimensional model of the airflow channel, the design team can intuitively observe and analyze the distribution of the airflow in the crushing chamber. This process helps to identify and resolve potential design issues, such as airflow obstructions or uneven distribution, thereby improving the optimization of the airflow path and ultimately improving the overall performance and reliability of the pulverizing equipment.

[0100] For example, based on the cylindrical crushing chamber mentioned above, a circular airflow channel model with a diameter of 0.2 meters and a length of 2 meters was constructed according to the design requirements. The channel model was accurately assembled to the bottom and top inlets of the chamber through CAD software to ensure that the airflow can enter and exit the chamber evenly. After assembly, check whether the interface between the airflow channel and the chamber is tightly connected to ensure that there is no risk of leakage.

[0101] Step S1213, in the three-dimensional assembly model of the crushing chamber, based on the gas-solid two-phase flow theory, by adjusting the spatial layout parameters of the airflow channel, the flow trajectory and velocity distribution of the airflow inside the crushing chamber are simulated, and the uniformity of the flow field is evaluated, and the airflow channel layout scheme with the best gas-solid two-phase flow effect is selected to form a first airflow path.

[0102] Specifically, gas-solid two-phase flow refers to the behavior and interaction of gas and solid particles in the flow at the same time. In the pulverizing equipment, the airflow carries the powder particles for efficient pulverization and dispersion, and the interaction between the airflow and the powder determines the pulverization effect and the uniformity of the powder. Using fluid dynamics (CFD) software (such as ANSYS Fluent, COMSOL Multiphysics, etc.), the assembled integrated three-dimensional model is imported for airflow simulation. By setting appropriate boundary conditions (such as the flow rate and pressure of the air inlet and outlet) and material property parameters (such as powder density, particle size distribution, etc.), the flow trajectory and velocity distribution of the airflow in the cavity are simulated. Through the simulation results, the uniformity indicators of the airflow in the cavity are evaluated, such as the uniformity of the velocity distribution and the distribution of turbulence intensity. A uniform flow field helps to achieve uniform dispersion and fineness control of the powder, and avoid powder aggregation or poor flow caused by local excessive or low airflow velocities. According to the flow field evaluation results, the spatial layout parameters of the airflow channel (such as the bending angle of the channel, the connection method, the inlet and outlet positions, etc.) are adjusted, and the airflow simulation is performed again until the flow field uniformity reaches the preset optimization standard. Through multiple iterations and optimizations, the airflow channel layout scheme with the best gas-solid two-phase flow effect is selected to form the final first airflow path. This path should ensure the uniform distribution and efficient flow of the airflow in the entire pulverizing chamber, thereby improving the pulverizing effect and product quality.

[0103] Gas-solid two-phase flow theory plays a core role in the design of pulverizing equipment. Through detailed flow field simulation and evaluation, the design team is able to scientifically optimize the airflow path to ensure uniform distribution and efficient flow of airflow in the cavity. This not only improves the pulverizing efficiency, but also reduces energy waste and powder loss, ensuring product consistency and high quality. Through airflow simulation and layout optimization based on gas-solid two-phase flow theory, the pulverizing efficiency and product quality of the equipment can be significantly improved. Uniform flow field distribution reduces powder agglomeration and wall sticking, and improves the dispersion and fineness control ability of the powder. In addition, the optimized airflow path reduces energy loss, improves the energy utilization efficiency of the equipment, and reduces operating costs.

[0104] For example, during the simulation process, it was found that the initial airflow channel layout resulted in a higher airflow velocity in the middle of the cavity, while the airflow was weaker at the edge. To solve this problem, the bending angle and inlet position of the airflow channel were adjusted so that the airflow could be more evenly distributed throughout the cavity. After multiple simulations and evaluations, an airflow channel layout scheme with multiple curved connections was finally determined to ensure a more uniform airflow velocity distribution in the cavity, significantly improving the crushing efficiency and consistency of powder fineness.

[0105] Step S1220, determining a first conductive material covering scheme according to the conductive material covering ratio in the initial structural parameters of the device;

[0106] The conductive material covering scheme is designed to optimize the electrostatic field distribution inside the chamber by reasonably covering the inner wall of the pulverizing chamber, thereby preventing powder from agglomerating or sticking to the wall due to static electricity accumulation.

[0107] Furthermore, if Figure 5 As shown, step S1220 includes:

[0108] Step S1221, dividing the inner wall surface of the three-dimensional assembly model of the crushing cavity into n1 regular geometric partitions, and the size of each geometric partition is determined according to the coverage ratio of the conductive material;

[0109] Specifically, the inner wall of the pulverizing chamber is divided into a number of regular geometric partitions (such as rectangles, circles, etc.) according to the conductive material coverage ratio. The size and shape of each partition should meet the conductive material coverage requirements to achieve optimal electrostatic field control. The number of partitions is determined according to the geometric complexity of the inner wall and the design requirements. For simple-shaped chambers, fewer partitions can be used; for chambers with complex geometric shapes, more partitions are required to achieve precise coverage. According to the conductive material coverage ratio, the required coverage area of ​​each partition is calculated. For example, if the conductive material coverage ratio is 70% and the total inner wall area is 100 square meters, the coverage area of ​​each partition should be allocated as 70 square meters. The conductive material coverage ratio refers to the ratio of the conductive material coverage area on the inner wall of the pulverizing chamber relative to the total inner wall area. Reasonable division of the inner wall area and determination of the size of each partition can help achieve uniform conductive material coverage, optimize the electrostatic field distribution, and prevent powder agglomeration and wall sticking caused by static electricity accumulation. By dividing the inner wall surface into multiple geometric partitions and accurately calculating the size of each partition according to the coverage ratio, the design team can ensure uniform coverage of the conductive material. This method not only improves the uniformity of the electrostatic field, but also enhances the safety and stability of the equipment, and reduces equipment failures and powder quality problems caused by static electricity problems.

[0110] For example, assuming the total inner wall area of ​​the crushing chamber is 100 square meters, the conductive material coverage ratio is set to 70%. The design team decided to divide the inner wall into 10 regular geometric partitions, each covering 7 square meters. According to the shape of the chamber and design requirements, rectangular partitions were selected, with the length and width of each partition being 2 meters and 3.5 meters respectively, to ensure uniform coverage of the conductive material.

[0111] Step S1222, determining the coverage position and coverage density of the conductive material layer, generating a three-dimensional solid model of the conductive material layer in each geometric partition, and constraining and assembling it with the three-dimensional assembly model of the pulverizing cavity to obtain the three-dimensional assembly model of the conductive pulverizing cavity;

[0112] Specifically, the coverage position refers to the specific distribution area of ​​the conductive material on the inner wall of the pulverizing chamber. These positions are usually determined based on the results of electric field simulation and design requirements, aiming to optimize the distribution of the electrostatic field and prevent the powder from agglomerating or sticking to the wall due to static electricity accumulation. According to the functional requirements and structural design of the equipment, determine which areas need more conductive material coverage. For example, due to the high air flow velocity at the air inlet and outlet, a strong electrostatic field is easily generated, and priority coverage is required. Coverage density refers to the coverage ratio or thickness of the conductive material inside each geometric partition. A high coverage density means that a larger proportion of the inner wall is covered with conductive material, which helps to neutralize and disperse static charges more effectively. Consider the conductivity, mechanical strength and durability of the conductive material and select a suitable coverage density. A higher coverage density can provide better conductivity, but it will also increase material costs and manufacturing complexity.

[0113] Create a detailed 3D model of the conductive material layer using advanced computer-aided design (CAD) software such as SolidWorks, AutoCAD or CATIA. These software can accurately simulate the geometry, thickness and distribution of the conductive material. Design the shape of the conductive material layer according to the shape of each geometric partition (such as rectangle, circle or ellipse), ensuring that it is consistent with the partition geometry. Set the thickness of the conductive material layer according to the required coverage density. For example, the thickness of the conductive material layer may be set to 0.7 mm in areas of high electric field strength and 0.5 mm in other areas. Ensure that the location of the conductive material layer is exactly consistent with the boundaries of the geometric partitions to avoid any geometric deviation or misalignment.

[0114] Constrained assembly refers to the precise embedding of the 3D solid model of the conductive material layer into the corresponding geometric partitions in the 3D assembly model of the pulverizing chamber. This process ensures the perfect fit of the conductive material layer with the inner wall of the chamber to avoid airflow leakage or loosening of the conductive layer. In the 3D assembly model of the pulverizing chamber, the position of the conductive material layer in each geometric partition is accurately located according to the predetermined coverage position and coverage density. Ensure that the geometry of the conductive material layer model fully matches the geometry of the inner wall partition of the pulverizing chamber to avoid any geometric conflicts or mismatches. Apply geometric constraints (such as face contact, edge alignment, etc.) to fix the conductive material layer in the corresponding position to ensure the stability and durability of the conductive material layer during equipment operation. Check the assembled model to ensure that all conductive material layers have been correctly assembled without omissions or errors.

[0115] The three-dimensional assembly model of the conductive pulverizing chamber refers to the complete structure after the conductive material layers in all geometric partitions are successfully assembled into the three-dimensional model of the pulverizing chamber. The model fully reflects the distribution of the conductive material on the inner wall of the entire chamber, providing a basis for electrostatic field simulation and further optimization. Commonly used conductive materials include copper plating, aluminum plating, carbon fiber composite materials, etc. These materials have high conductivity and good mechanical strength, can effectively neutralize static charges, and prevent powder agglomeration and wall sticking. The thickness of the conductive material layer needs to be optimized according to the electrostatic field simulation results and actual operation requirements. Thicker conductive layers can provide higher conductivity, but at the same time increase material cost and manufacturing complexity. Therefore, it is necessary to find the best balance between conductivity and cost. In areas with complex geometries or drastic changes in airflow, refining geometric partitions can achieve more accurate conductive material coverage. This helps to improve the uniformity of the electric field, further improving the pulverization effect and equipment stability. The shape of the partition should be designed according to the overall geometry of the pulverizing chamber and the characteristics of the airflow distribution. For example, a more detailed partition shape can be used at the curved joint to adapt to the complex electric field distribution when the airflow turns.

[0116] By accurately determining the coverage position and coverage density of the conductive material layer, the three-dimensional assembly model of the conductive pulverizing chamber can achieve a more uniform electrostatic field distribution. This effectively reduces the accumulation of static electricity in the powder during the pulverizing process, prevents powder agglomeration and wall sticking, and improves pulverizing efficiency and product quality. A uniform electrostatic field helps to stably neutralize the charge on the powder particles and prevent the powder particles from attracting and adhering to each other due to static electricity. By optimizing the coverage ratio and position of the conductive material, a uniform distribution of the electrostatic field can be achieved throughout the chamber, ensuring that each powder particle is evenly affected by the airflow and negative ions.

[0117] Step S1223, based on the three-dimensional assembly model of the conductive pulverizing cavity, an electrostatic field simulation is performed to calculate the electric field uniformity index inside the pulverizing cavity;

[0118] Specifically, the electrostatic field simulation of the three-dimensional assembly model of the conductive pulverizing cavity is performed using electromagnetic field simulation software (such as ANSYS Maxwell, COMSOL Multiphysics, etc.). Appropriate electric field sources and boundary conditions are set to simulate the electric field distribution in the cavity. The uniformity indicators of the electric field inside the cavity, such as the standard deviation of the electric field intensity and the electric field gradient, are calculated and evaluated. The higher the electric field uniformity index, the more uniform the electric field distribution, which helps to reduce the static electricity accumulation and agglomeration of the powder. Electrostatic field simulation is a key step in evaluating the effectiveness of the conductive material covering scheme. Through accurate simulation, the uneven areas in the electric field distribution can be identified and data support can be provided for subsequent optimization. The electric field uniformity index is an important indicator for measuring the quality of the electrostatic field distribution, which is directly related to the performance of the pulverizing equipment and the product quality. Through electrostatic field simulation and calculation of uniformity indicators, the design team can scientifically evaluate the effectiveness of the conductive material covering scheme. This process helps to discover deficiencies in the design, guide the subsequent adjustment of the covering position and density, ensure the uniform distribution of the electrostatic field, thereby improving the pulverizing efficiency and powder quality, and reducing equipment failures and maintenance costs.

[0119] For example, ANSYS Maxwell was used to simulate the electrostatic field of the three-dimensional assembly model of the conductive pulverization cavity. The simulation results showed that the electric field strength was slightly higher in the center area of ​​the cavity and slightly lower in the edge area, with a standard deviation of 5%. In order to improve the uniformity of the electric field, it was decided to increase the coverage density of the conductive material in the edge area to reduce the standard deviation of the electric field strength and improve the overall uniformity.

[0120] The calculation of the electric field uniformity index inside the pulverizing cavity includes:

[0121]

[0122] in:

[0123] It represents the electric field uniformity index, and its value range is from 0 to 1. The closer the value is to 1, the more uniform the electric field distribution is.

[0124] Indicates a point inside the crushing chamber The electric field strength vector at .

[0125] It represents the average value of the electric field strength inside the crushing cavity, which can be obtained by taking the volume average of the electric field strength in the entire cavity space.

[0126] Indicates the total volume of the crushing chamber.

[0127] It represents the triple integral of the entire space of the pulverizing chamber.

[0128] It represents the absolute value of the difference between the electric field strength at a certain point and the average electric field strength.

[0129] It can be obtained by solving the electrostatic field equations. It is necessary to first apply appropriate potential boundary conditions inside the crushing cavity according to the distribution of the conductive material layer, and then use numerical methods (such as the finite element method) to discretize and solve the equations.

[0130] Can be obtained Then, it is obtained by numerical integration of the crushing cavity.

[0131] It can be obtained by geometric analysis of the three-dimensional solid model of the crushing cavity.

[0132] The size of the electric field uniformity index ζ mainly depends on two factors: the coverage area of ​​the conductive material layer and the spatial distribution of the conductive material layer. When the total amount of conductive material is constant, increasing the coverage area of ​​the conductive material layer can make the charge more evenly distributed on the inner wall of the pulverizing chamber, thereby improving the uniformity of the electric field; at the same time, optimizing the spatial distribution of the conductive material layer so that it forms a uniform potential gradient inside the pulverizing chamber also helps to improve the uniformity of the electric field. Therefore, by reasonably adjusting the coverage ratio and coverage position of the conductive material, the value of the electric field uniformity index ζ can be maximized.

[0133] This formula is used to quantitatively evaluate the uniformity of the electric field distribution inside the crushing chamber. Indicates the total electric field strength that the electric field inside the crushing chamber should have under ideal conditions. It indicates the total deviation between the actual electric field distribution and the ideal uniform electric field. Dividing the two and then subtracting the quotient from 1, we get the electric field uniformity index. . The closer it is to 1, the smaller the deviation between the actual electric field distribution and the ideal uniform electric field, and the better the uniformity of the electric field distribution.

[0134] By calculating the electric field uniformity index ζ, we can quantitatively evaluate the impact of different conductive material coverage schemes on the electric field uniformity inside the pulverizing chamber, and thus select the conductive material coverage scheme with the best electric field uniformity. Uniform electric field distribution is beneficial to suppress static electricity accumulation during the pulverizing process, improve pulverizing efficiency and powder dispersibility.

[0135] Step S1224, adjusting the covering position and covering density of the conductive material layer according to the electric field uniformity index inside the pulverizing cavity, and obtaining the first conductive material covering scheme when the electric field uniformity index reaches a preset convergence condition.

[0136] Specifically, based on the electric field uniformity index calculated in step S1223, the uneven area of ​​electric field distribution is analyzed, and the coverage position and coverage density of the conductive material layer are adjusted in a targeted manner. For example, the conductive material coverage density is increased in areas with higher electric field strength to reduce the electric field strength in the area and improve the overall uniformity. Through multiple adjustments and re-simulations, the coverage scheme of the conductive material is gradually optimized. After each adjustment, the electrostatic field simulation is performed again, a new electric field uniformity index is calculated, and the optimization effect is evaluated. Set preset convergence conditions, such as the standard deviation of the electric field strength is reduced to less than 3%, or the electric field uniformity reaches more than 90%. When the electric field uniformity index meets these conditions, the optimization process is terminated to determine the final conductive material coverage scheme. The final conductive material coverage scheme should ensure the uniform distribution of the electrostatic field in the cavity to prevent powder agglomeration and wall sticking due to static electricity accumulation.

[0137] The optimization process of the conductive material coverage scheme is an iterative feedback loop. By continuously adjusting the coverage position and density and conducting simulation verification, the design team was able to gradually approach the optimal coverage scheme. This process not only improved the uniformity of the electrostatic field, but also enhanced the operating stability and crushing efficiency of the equipment. By iteratively adjusting and optimizing the conductive material coverage scheme, the design team was able to scientifically control the electrostatic field distribution inside the cavity to ensure its uniformity. This not only reduced the electrostatic accumulation and agglomeration of the powder, improved the crushing efficiency and product quality, but also extended the service life of the equipment and reduced maintenance costs. At the same time, the optimized coverage scheme improved the safety of the equipment and avoided discharge and equipment damage caused by static electricity.

[0138] For example, after the initial adjustment of the conductive material coverage plan, the electrostatic field simulation showed that the standard deviation of the electric field strength dropped from 5% to 3%. The thickness of the conductive material was further increased from 0.5 mm to 0.7 mm in the area with higher electric field strength, and the electrostatic field simulation was performed again. The new simulation results showed that the electric field uniformity index was improved to 95%, meeting the preset convergence conditions. In the end, the first conductive material coverage plan was determined to increase the conductive material coverage density in the area with higher electric field strength to ensure uniform distribution of the electrostatic field in the entire pulverizing chamber.

[0139] By executing step S1210 and step S1220, the first airflow path and the first conductive material covering scheme of the pulverizing chamber can be scientifically and systematically determined. This process not only ensures the uniform distribution of the airflow in the chamber, improves the pulverizing efficiency and powder quality, but also effectively controls the uniformity of the electrostatic field by optimizing the covering scheme of the conductive material, and prevents the electrostatic accumulation and agglomeration of the powder. Ultimately, these optimization measures significantly improve the overall performance, stability and reliability of the equipment, and lay a solid foundation for the efficient operation and high-quality product output of the low-temperature airflow nano-pulverizing equipment for medicinal and edible plants.

[0140] Step S1230, determining a first negative ion placement scheme according to the negative ion placement density in the initial structural parameters of the device;

[0141] Further, step S1230 includes:

[0142] Step S1231, according to the negative ion placement density, calculate the number and placement spacing of the negative ion generators required inside the pulverizing chamber; according to the number and placement spacing of the negative ion generators, generate a placement position matrix of the negative ion generators on the inner wall surface of the three-dimensional assembly model of the conductive pulverizing chamber;

[0143] Specifically, the negative ion density refers to the distribution density of the negative ion generators on the inner wall of the grinding chamber, which is usually expressed as the number of negative ion generators placed per unit area (such as 10 negative ion generators per square meter). Reasonable negative ion density can ensure the uniform distribution of negative ions inside the chamber, effectively inhibit static electricity accumulation, prevent powder agglomeration and wall sticking, and improve grinding efficiency and product quality.

[0144] Calculate the number of negative ion generators: number of negative ion generators = total inner wall area × negative ion placement density; for example, if the total inner wall area is 6.28 square meters and the negative ion placement density is 10 / m², 63 negative ion generators need to be placed.

[0145] Calculation method for determining the placement spacing: Calculate the uniform spacing between units based on the placement density of the negative ion generators. The calculation formula for the placement spacing is:

[0146] spacing rice;

[0147] In high electric field areas, the spacing between the devices should be appropriately reduced to increase the negative ion density; in low electric field areas, the spacing between the devices should be maintained or appropriately increased to save resources.

[0148] The placement position dot matrix refers to the positions of the negative ion generators arranged on the inner wall surface according to the predetermined spacing and distribution pattern. Use CAD software (such as SolidWorks) to generate the placement position dot matrix of the negative ion generators on the inner wall surface of the three-dimensional assembly model of the conductive pulverizing chamber according to the calculated placement spacing and quantity. Ensure that the dot matrix is ​​evenly distributed and covers the entire inner wall surface, especially the area with high electric field strength.

[0149] According to the simulation results of the airflow path and electrostatic field in the cavity, the placement of the negative ion generator is optimized to ensure that the negative ions can effectively cover the crushing area and evenly neutralize the static charge. The placement lattice can be flexibly adjusted according to actual needs, such as using a grid lattice, a random lattice or a centralized placement strategy to adapt to different design requirements and operating conditions. Through precise calculation and lattice generation, the negative ion generator is ensured to be evenly distributed in the cavity, the electrostatic neutralization effect is improved, and the powder agglomeration and wall sticking phenomenon are reduced. The uniform distribution of negative ions helps to maintain the good dispersion of the powder, ensure that the powder particles can be evenly stressed during the crushing process, and improve the crushing efficiency and product quality. Reasonably place the negative ion generator according to the electric field strength in different areas to avoid waste of resources and optimize equipment costs.

[0150] Step S1232, inserting a three-dimensional solid model of the negative ion generator at each placement point of the placement position array, and constraining and assembling it with the three-dimensional assembly model of the conductive pulverizing cavity to obtain a three-dimensional assembly model of the negative ion integrated pulverizing cavity;

[0151] Specifically, a detailed three-dimensional model of the negative ion generator, including its shape, size and functional components, is created using CAD software (such as SolidWorks). The geometric parameters of the negative ion generator are set according to its operating parameters (such as operating voltage and operating frequency) to ensure that it can operate effectively in the pulverizing chamber. According to the placement position lattice, the three-dimensional solid model of the negative ion generator is accurately inserted into the corresponding placement points of the three-dimensional assembly model of the conductive pulverizing chamber. The negative ion generator is fixed at the placement point using geometric constraints (such as surface contact, position fixation, etc.) to ensure that it is stable and reliable during the operation of the equipment. The three-dimensional assembly model of the negative ion integrated pulverizing chamber refers to the complete structure after all negative ion generators are successfully assembled into the three-dimensional assembly model of the conductive pulverizing chamber. The model demonstrates the synergy between conductive materials and negative ion generators, which is helpful for further functional verification and optimization. The placement position and assembly status of the negative ion generator are checked through visualization tools to ensure that it meets the design requirements and avoids placement errors or assembly defects.

[0152] Different types of negative ion generators (such as corona discharge type, photocatalyst type, etc.) may have different structural and functional characteristics. It is necessary to select the appropriate type and conduct detailed design according to specific needs. High-precision assembly ensures that the negative ion generator can work stably during the operation of the equipment to avoid displacement or damage caused by vibration or airflow disturbance. By accurately placing and firmly assembling the negative ion generator, it is ensured that the negative ions can effectively cover the inside of the crushing chamber, improve the efficiency of static neutralization, and reduce the static accumulation and agglomeration of the powder. The stable assembly method enhances the operating stability of the negative ion generator, reduces the failure rate during equipment operation, and improves the reliability of the overall equipment. The uniform and efficient distribution of negative ions ensures the uniform force and dispersion of the powder particles during the crushing process, significantly improving the fineness and consistency of the final product.

[0153] Step S1233, obtaining the optimal working parameter combination of the negative ion generator under different placement positions, and assigning it to the three-dimensional entity model of each negative ion generator in the three-dimensional assembly model of the negative ion integrated crushing cavity, to form a first negative ion placement plan.

[0154] Specifically, the working parameters of the negative ion generator mainly include working voltage, working current and working frequency. These parameters determine the generation efficiency and distribution effect of negative ions. The optimal working parameter combination refers to the parameter setting that enables the negative ion generator to achieve the most efficient and uniform generation and distribution of negative ions at a specific placement position. According to the working principle of the negative ion generator, analyze the impact of different parameter combinations on the generation efficiency and distribution of negative ions. For example, higher working voltage and frequency may generate more negative ions, but may also increase energy consumption and equipment burden. Consult the performance parameter database of the negative ion generator to obtain the optimal working parameter combination under different placement positions and placement densities. These data usually come from experimental tests and technical information provided by the manufacturer. Through experimental or simulation tests, verify the effects of different working parameter combinations and select the parameter combination that best suits the specific placement position.

[0155] The best working parameter combination (such as working voltage 5 kV, working frequency 50 Hz) is assigned to the three-dimensional solid model of the negative ion generator. In the CAD software, the working parameter attributes of the negative ion generator are updated to ensure that the model reflects the actual working status. The working parameters of all negative ion generators are integrated to form a complete first negative ion placement plan.

[0156] The optimal operating parameters of different negative ion generators may vary depending on the placement location. It is necessary to perform personalized parameter optimization based on the specific electrostatic field distribution and the airflow characteristics in the crushing chamber. During the operation of the equipment, it may be necessary to dynamically adjust the operating parameters of the negative ion generator based on real-time monitoring data to cope with process changes and external interference. By determining the optimal combination of operating parameters, the negative ion generator can efficiently generate negative ions at different placement locations to ensure the uniformity and effectiveness of the negative ion distribution. The application of optimal operating parameters improves the overall performance of the negative ion generator, enhances the electrostatic suppression capability of the equipment, and further improves the crushing efficiency and product quality. By optimizing the operating parameters, the negative ion generator can reduce energy consumption and equipment burden while ensuring efficient negative ion generation, thereby improving energy utilization efficiency.

[0157] For example, the negative ion generator placed at the airflow inlet has a high electric field strength in this area. The design team consulted the performance parameter database and conducted experimental tests to determine the optimal operating parameter combination of 6 kV operating voltage and 60 Hz operating frequency. These parameters were assigned to the three-dimensional solid model of the negative ion generator to ensure that it can efficiently generate negative ions in the high electric field area and optimize the uniformity of the electrostatic field.

[0158] Step S1240, combining the first airflow path, the first conductive material covering scheme and the first negative ion placement scheme to generate a first design scheme, and storing it in a device optimization database.

[0159] Specifically, the first airflow path includes parameters such as the shape, size, length, inlet and outlet positions of the airflow channel to ensure that the airflow can cover the entire crushing chamber to achieve efficient crushing and uniform dispersion. The first conductive material coverage scheme includes the type, coverage ratio, thickness of the conductive material and its specific distribution method in different geometric partitions to ensure that the conductive layer can effectively neutralize static charges and prevent powder agglomeration. The first negative ion placement scheme includes parameters such as the number of negative ion generators, placement positions, operating voltages, and operating frequencies to ensure that negative ions are evenly distributed in the cavity and enhance the electrostatic suppression effect. The first airflow path, the first conductive material coverage scheme, and the first negative ion placement scheme are integrated into a unified design scheme to ensure coordination and compatibility between the various parts. The overall performance of the integrated design scheme is verified through simulation software, including airflow distribution, electrostatic field uniformity, and negative ion distribution effects, to ensure the scientificity and feasibility of the design scheme.

[0160] The equipment optimization database includes process requirement tables, material attribute tables, equipment structure parameter tables, etc. All design scheme data are stored in order and are interrelated. All parameters and configurations of the first design scheme are recorded in detail, including specific values ​​and layout diagrams of airflow paths, conductive material coverage schemes, and negative ion placement schemes. Ensure that the design team can easily retrieve and access the design schemes stored in the database to support subsequent optimization and adjustments.

[0161] The first design scheme covers multiple key factors such as airflow, electrostatic field and negative ion distribution to ensure that the equipment can achieve optimal performance in all aspects. The airflow path, conductive material coverage and negative ion placement scheme complement each other and work together to improve the overall performance and operating efficiency of the equipment. The optimization database serves as the data management core of the design process to ensure that all design parameters and schemes are effectively recorded and traced. The structured storage of the database facilitates the rapid retrieval and update of information, supports the subsequent design optimization and iteration process, and improves design efficiency and accuracy.

[0162] By integrating the airflow path, conductive material covering scheme and negative ion placement scheme into the first design scheme, the coordination and complementarity of each design part are ensured, and the rationality and effectiveness of the overall design are improved. The various design schemes interact with each other to jointly optimize the airflow distribution and electrostatic field uniformity, avoiding the problem of local optimization and overall incoordination that may be caused by a single design scheme. The first design scheme is systematically stored in the equipment optimization database to ensure the integrity and traceability of the design data, which is convenient for subsequent optimization and iteration. Effective data management supports the design team to quickly access and modify design parameters in subsequent steps, improve the efficiency and flexibility of the design process, and ensure continuous optimization and improvement of equipment design. The comprehensive optimization design scheme improves the uniformity of airflow distribution, the balance of electrostatic field and the effectiveness of negative ion distribution, and significantly improves the crushing efficiency, product quality and operation stability of the equipment. Uniform airflow and electrostatic field distribution ensure that the powder is uniformly stressed during the crushing process, reduces agglomeration and wall sticking, and improves crushing efficiency and powder fineness. At the same time, the effective distribution of negative ions further inhibits static electricity accumulation and enhances the operational stability and reliability of the equipment. Structured data management and integrated processing of design solutions enable the design team to design and optimize more efficiently, shorten the design cycle, and reduce labor and material costs. Through the support of systematic design processes and optimized databases, duplication of work and errors in the design process are reduced, design efficiency is improved, and design costs are reduced.

[0163] Step S1300, analyzing and optimizing the airflow field of the first design solution to obtain a second airflow path;

[0164] The purpose of step S1300 is to identify and optimize the deficiencies in the airflow distribution by performing a detailed flow field analysis on the initially designed airflow path, thereby obtaining a more efficient and uniform second airflow path. This step ensures that the airflow can evenly cover the entire pulverizing chamber during the pulverizing process, thereby improving pulverizing efficiency and reducing energy loss.

[0165] Furthermore, if Figure 6 As shown, step S1300 includes:

[0166] Step S1310, building an airflow field analysis model according to the first design scheme;

[0167] Specifically, the airflow path, conductive material covering scheme and negative ion placement scheme determined in the first design scheme are imported into computer simulation analysis software, such as ANSYS Fluent or COMSOL Multiphysics. These software have powerful fluid dynamics (CFD) simulation capabilities and can accurately simulate the flow behavior of airflow in complex geometric structures. Set boundary conditions including air inlet and outlet and material property parameters; set corresponding velocity, pressure or mass flow boundary conditions according to the airflow inlet and outlet positions determined by the design scheme. For example, if the inlet is set to a constant velocity, a specific velocity value needs to be entered. Material property parameters include material density, viscosity, moisture content, etc., which affect the flow characteristics and crushing effect of the airflow. Use a higher mesh density in the crushing cavity and the internal area of ​​the airflow channel to improve the simulation accuracy; use a lower mesh density in areas with small flow changes to save computing resources. Select a suitable mesh type, such as a hexahedral mesh or an unstructured mesh, to adapt to complex geometric shapes. Select a suitable turbulence model, such as the k-ε model or the LES (large eddy simulation) model, to accurately describe the turbulence characteristics in the airflow. If the presence of powder is considered, the gas-solid two-phase flow is modeled and an appropriate multiphase flow model is selected, such as the Euler-Euler model or the discrete phase model (DPM).

[0168] Through detailed airflow field analysis, the flow trajectory and velocity distribution of the airflow in the grinding chamber can be accurately predicted, and potential flow dead corners or high-pressure areas can be found. This provides reliable data support for subsequent airflow path optimization, ensuring that the optimized airflow path has better performance in actual operation.

[0169] For example, suppose the airflow path in the preliminary design forms a local high-pressure area on one side of the pulverizing chamber, resulting in severe powder accumulation in this area. Through the airflow field analysis model, this uneven airflow distribution can be clearly seen, thus guiding the designer to adjust the layout of the airflow channel to achieve a more uniform airflow distribution.

[0170] Step S1320, obtaining first airflow field parameters according to the airflow field analysis model, wherein the first airflow field parameters include velocity field, pressure field and turbulence intensity, and storing them in a device optimization database;

[0171] Specifically, the airflow field analysis model is started, and numerical simulation calculations are performed to obtain detailed flow field information of the airflow in the grinding chamber. The velocity field describes the velocity distribution of the airflow at various positions, reflecting the kinetic energy and flow direction of the airflow. The pressure field reflects the pressure distribution of the airflow at different positions, which helps to identify possible pressure unevenness problems. Turbulence intensity quantifies the degree of turbulence in the airflow, affecting the powder grinding effect and energy loss. The above airflow field parameter data is stored in the equipment optimization database, specifically in the airflow field parameter table, for subsequent analysis and optimization.

[0172] Systematically accumulate airflow field parameter data to provide rich reference materials for further optimization of equipment. Through detailed flow field parameters, problems in airflow distribution, such as local flow velocity that is too high or too low, can be quickly identified, so as to optimize in a targeted manner. For example, in a simulation, it was found that the airflow velocity at the bottom of the crushing chamber was significantly lower than that at the top, resulting in uneven powder dispersion at the bottom. By extracting velocity field data, the problem area can be accurately located and corresponding optimization strategies can be formulated.

[0173] Step S1330, comparing the first airflow field parameter with the preset airflow field parameter to obtain a first airflow field deviation, where the first airflow field deviation includes a speed deviation, a pressure deviation, and a turbulence intensity deviation;

[0174] Specifically, in the early stage of design, according to empirical data or process requirements, set the ideal airflow field parameters, including the target velocity field, target pressure field and target turbulence intensity. Compare the difference between the actual simulated velocity field and the preset target velocity field, and calculate the velocity deviation value at each position. Similarly, compare the difference between the actual pressure field and the preset target pressure field, and calculate the pressure deviation value. Compare the difference between the actual turbulence intensity and the preset target turbulence intensity, and calculate the turbulence intensity deviation value. Take the velocity deviation, pressure deviation and turbulence intensity deviation into consideration to form the first airflow field deviation report, which is recorded in the airflow field deviation table in the equipment optimization database.

[0175] Through the quantified deviation value, the airflow field performance of the current design scheme can be objectively evaluated, and specific aspects that need to be improved can be identified. Clear deviation indicators provide specific improvement directions and goals for subsequent airflow path optimization, ensuring that the optimization process is targeted. For example, if the preset airflow speed should be maintained at 5 meters per second, and the actual simulation results show that a certain area is only 3 meters per second, the speed deviation is -2 meters per second. This specific deviation value indicates that it is necessary to increase the airflow supply in this area or adjust the airflow channel layout to increase the airflow speed.

[0176] Step S1340: optimizing the first airflow path according to the first airflow field deviation to obtain a second airflow path.

[0177] Specifically, step S1340 includes the following contents:

[0178] Optimization target setting: Based on the first airflow field deviation report, determine the optimization target, such as minimizing the velocity deviation, pressure deviation, and turbulence intensity deviation, to ensure a more uniform and efficient airflow distribution.

[0179] Optimization algorithm application:

[0180] Genetic Algorithm (GA): simulates natural selection and genetic mechanisms to find the optimal airflow path design through iterative evolution.

[0181] Particle Swarm Optimization (PSO): simulates the foraging behavior of bird flocks and optimizes airflow path parameters through group collaboration.

[0182] Other computer-aided optimization algorithms: such as simulated annealing, gradient descent, etc. Choose the most suitable algorithm according to specific needs.

[0183] Parameter adjustment:

[0184] Airflow channel layout: Adjust the shape, size, position and connection method of the airflow channel to optimize the flow path of the airflow.

[0185] Branch and confluence point design: Redesign the number of branches and confluence method of the airflow channel to ensure that the airflow can be evenly distributed to the entire crushing chamber.

[0186] Bending angle and radius: Optimize the bending angle and radius of the air flow channel to reduce flow resistance and energy loss and avoid the generation of eddy currents.

[0187] Re-simulation and verification: Apply the optimized airflow path parameters, re-simulate the airflow field, and verify whether the optimization effect achieves the expected goal.

[0188] Generate a second airflow path: If the optimization effect is satisfactory, the optimized airflow path is determined as the second airflow path and recorded in the "second airflow path" field in the device optimization database.

[0189] The optimized airflow path can cover the entire pulverizing chamber more evenly, avoid local airflow that is too strong or too weak, and improve pulverizing efficiency. By reducing energy losses in the airflow path, higher energy utilization can be achieved and equipment operating costs can be reduced. The optimized airflow path reduces eddy currents and abnormal flow phenomena, reduces the risk of equipment wear and failure, and extends the service life of the equipment. For example, during the preliminary optimization process, it was found that there was still a low-speed area on the right side of the pulverizing chamber. By applying the particle swarm optimization algorithm, the branching angle and bending radius of the airflow channel were adjusted so that the airflow could be more evenly distributed to the right area. After re-simulation, it was found that the airflow velocity in this area was significantly improved, close to the preset target value, indicating that the optimization effect was significant.

[0190] Step S1400, performing electrostatic accumulation prediction and scheme optimization on the first design scheme to obtain a second conductive material covering scheme and a second negative ion placement scheme;

[0191] Step S1400 aims to evaluate the influence of conductive material coverage and negative ion placement on the powder loss rate in the current design scheme through the electrostatic aggregation prediction model, and optimize based on the prediction results to finally obtain an optimized scheme to reduce the powder loss rate. This step ensures that the equipment effectively controls electrostatic aggregation during the pulverization process, reduces powder loss, and improves product quality.

[0192] Further, step S1400 includes:

[0193] Step S1410, constructing an electrostatic accumulation prediction model based on the electrostatic characteristics of the material in the material attribute table;

[0194] Specifically, step S1410 includes the following contents:

[0195] Collect data on the electrostatic properties of materials:

[0196] Electrostatic charge of materials: The electrostatic charge generated by the material during the crushing process affects the aggregation and adhesion of the powder.

[0197] Material dielectric constant: reflects the polarization ability of the material in the electric field and affects the distribution of the electrostatic field.

[0198] Material surface resistivity: affects the migration speed and distribution of charges.

[0199] Select a machine learning algorithm:

[0200] Support Vector Machine (SVM): Suitable for classification and regression problems, capable of handling high-dimensional data and nonlinear relationships.

[0201] Random Forest (RF): An ensemble learning method consisting of multiple decision trees, which has good anti-overfitting ability and high prediction accuracy.

[0202] Model construction:

[0203] Input characteristics: including material electrostatic properties, conductive material coverage ratio, negative ion placement density, etc.

[0204] Output target: powder loss rate, that is, the proportion of powder loss caused by static electricity accumulation during the crushing process.

[0205] Model training and optimization:

[0206] Data preprocessing: including data cleaning, missing value processing, feature normalization, etc., to ensure the effectiveness of model training.

[0207] Model training: Use historical data and test bench simulation data to train the electrostatic aggregation prediction model and establish the nonlinear relationship between powder loss rate and input features.

[0208] Model validation: Evaluate the predictive performance of the model through cross-validation and independent test sets, and adjust model parameters to improve accuracy and generalization ability.

[0209] Step S1410 can accurately predict the powder loss rate under different design parameter combinations through machine learning algorithms, providing a scientific basis for optimizing the design, reducing the subjectivity and errors of manual prediction, and improving the efficiency and reliability of the prediction process.

[0210] Step S1420, obtaining a first powder predicted loss rate according to the first conductive material covering scheme, the first negative ion placement scheme and the electrostatic aggregation prediction model;

[0211] Specifically, step S1420 includes the following contents:

[0212] Input parameters:

[0213] The first conductive material covering scheme includes the specific covering ratio, covering area and material type of the conductive material on the inner wall of the pulverizing chamber.

[0214] The first negative ion placement plan: including the placement density, location distribution and operating parameters (such as operating voltage and frequency) of the negative ion generators.

[0215] Model predictions:

[0216] The above input parameters are input into the electrostatic aggregation prediction model, and the model predicts the powder loss rate under the current design scheme based on the trained nonlinear relationship.

[0217] Result storage:

[0218] The predicted first powder loss rate is recorded in the powder loss rate table in the equipment optimization database for reference in subsequent optimization steps.

[0219] Step S1420 provides a quantitative indicator (powder loss rate) to help evaluate the electrostatic control effect of the current design. The predicted powder loss rate provides a clear direction for improvement for the next optimization, ensuring that the optimization target is clear and specific. For example, suppose that under the first design, the predicted powder loss rate is 5%. If the target loss rate is 2%, there is a 3% deviation, and optimization is required in the conductive material coverage and negative ion placement scheme.

[0220] Step S1430, comparing the first powder predicted loss rate with a preset target loss rate to obtain a first loss rate deviation;

[0221] Specifically, according to the process requirements or industry standards, an acceptable powder loss rate target value is set, such as 2%. The first loss rate deviation = the first powder predicted loss rate - the target loss rate; for example, if the predicted loss rate is 5% and the target loss rate is 2%, the deviation is +3%. The calculated first loss rate deviation is recorded in the loss rate deviation table in the equipment optimization database for use in the optimization step.

[0222] Step S1430 uses the deviation value to clearly understand the gap between the current design and the target, and to identify the areas that need improvement. It provides a specific quantitative indicator for improvement, ensuring that the optimization process has a clear direction and measurement standard. For example, in the above example, a deviation of +3% means that the powder loss rate of the current design is higher than the target, and measures need to be taken to reduce the loss rate. This specific deviation value provides a clear direction for improvement in the optimization process.

[0223] Step S1440, optimizing the first conductive material covering scheme and the first negative ion placement scheme based on the first loss rate deviation to obtain a second conductive material covering scheme and a second negative ion placement scheme.

[0224] Specifically, step S1440 includes the following contents:

[0225] Optimization goal setting:

[0226] Minimize deviation: The goal is to minimize the deviation in powder loss rate and keep it as close to or below the target loss rate as possible.

[0227] Considering both equipment cost and manufacturability: During the optimization process, not only the static control effect should be considered, but also the cost of conductive materials and negative ion generators and the feasibility of their placement.

[0228] Optimization algorithm application:

[0229] Genetic Algorithm (GA): It iteratively optimizes the conductive material coverage ratio and negative ion placement density by simulating natural selection and genetic variation.

[0230] Particle Swarm Optimization (PSO): By simulating group collaborative behavior, it finds the optimal combination of conductive material coverage and negative ion placement parameters.

[0231] Multi-objective optimization: Optimize multiple objectives simultaneously, such as minimizing powder loss rate deviation and equipment cost, using weight distribution or Pareto frontier methods.

[0232] Parameter adjustment:

[0233] Conductive material coverage ratio: According to the deviation, adjust the coverage ratio of the conductive material on the inner wall, such as increasing the coverage area to improve electrostatic conductivity and reduce powder loss.

[0234] Negative ion placement density: Adjust the placement density and location of negative ion generators, such as increasing the number of negative ion generators or optimizing their distribution locations to enhance the static neutralization effect.

[0235] Re-forecast and validation:

[0236] Predicted loss rate: The optimized conductive material coverage scheme and negative ion placement scheme are input into the electrostatic aggregation prediction model to predict the new powder loss rate.

[0237] Verify the effect: Ensure that the optimized solution can significantly reduce the powder loss rate and meet other design constraints.

[0238] Generate the second solution:

[0239] The optimized conductive material covering scheme and negative ion placement scheme are determined as the second scheme, and recorded in the "second conductive material covering scheme" and "second negative ion placement scheme" fields in the equipment optimization database.

[0240] Step S1440 significantly reduces the powder loss rate, improves the crushing efficiency and product quality by optimizing the conductive material coverage and negative ion placement scheme. While ensuring the electrostatic control effect, the use of conductive materials and negative ion generators is optimized to reduce equipment operation and maintenance costs. The optimized scheme not only meets the electrostatic control requirements, but also ensures its feasibility and stability in actual manufacturing and operation. For example, in the preliminary optimization process, after increasing the conductive material coverage ratio to 60%, the predicted powder loss rate dropped to 3%. At the same time, by increasing the density of the negative ion generator, the negative ion distribution is made more uniform, further reducing the predicted loss rate to 2.1%. This optimization process not only achieved a loss rate close to the target, but also completed the optimization within a reasonable cost range.

[0241] Step S1500, combining the second airflow path, the second conductive material covering scheme and the second negative ion placement scheme to generate a second design scheme; iteratively optimizing the second design scheme to obtain a final device internal structure design scheme.

[0242] The purpose of step S1500 is to generate a more optimized design by combining the optimized airflow path, conductive material covering scheme and negative ion placement scheme, and gradually improve the design through an iterative optimization process, and finally obtain a device internal structure design that meets all design requirements. This step ensures that the internal structure of the device is in the best state in terms of airflow distribution and electrostatic control, thereby improving crushing efficiency and product quality.

[0243] Further, step S1500 includes:

[0244] Step S1510, combining the second airflow path, the second conductive material covering scheme, and the second negative ion placement scheme to generate a second design scheme;

[0245] The second airflow path, that is, the optimized airflow path, is adopted to ensure a more uniform and efficient airflow distribution. The second conductive material covering scheme is applied, and the coverage ratio and distribution of the conductive material on the inner wall are optimized to improve the uniformity of the electric field and reduce powder loss. The second negative ion placement scheme is implemented, and the placement density and position of the negative ion generator are optimized to enhance the static neutralization effect. By integrating the optimization results of the airflow path, conductive material coverage and negative ion placement scheme, it is ensured that the optimization measures of various aspects are coordinated with each other to avoid local optimization leading to overall performance degradation. Ensure that the internal structure design scheme of the equipment remains consistent in different optimization links to improve the reliability and feasibility of the design.

[0246] Step S1520, repeatedly executing step S1300 and step S1400, performing airflow field analysis, electrostatic accumulation prediction and scheme optimization on the second design scheme, until the Nth airflow field deviation and the Nth loss rate deviation obtained in the Nth iteration both meet the preset convergence conditions;

[0247] Specifically, set the preset convergence conditions, such as the deviation of the airflow field and the deviation of the powder loss rate are both lower than a certain threshold (for example, the deviation of the airflow field is lower than 5%, and the deviation of the powder loss rate is lower than 1%). Through multiple iterations, continuously refine and optimize the design scheme to ensure that the final design scheme reaches the best state in terms of airflow distribution and electrostatic control. Gradually reduce the deviation of design parameters and improve the accuracy and reliability of the design scheme. According to the actual deviation, dynamically adjust the design scheme to adapt to different materials and process requirements, and improve the versatility and adaptability of the equipment.

[0248] For example, in the first iteration, the deviation of the airflow field was 6% and the deviation of the powder loss rate was 1.5%. By optimizing the airflow path and the conductive material coverage ratio, after the second iteration, the deviation of the airflow field was reduced to 4.8% and the deviation of the powder loss rate was reduced to 0.9%, both of which met the preset convergence conditions (the deviation of the airflow field was less than 5% and the deviation of the powder loss rate was less than 1%), so the iteration was stopped and the final design solution was determined.

[0249] Step S1530, determining the Nth design scheme obtained by the Nth iteration as the final device internal structure design scheme, and storing it in the device optimization database.

[0250] Specifically, it is confirmed that when the Nth iteration is made, the airflow field deviation and the powder loss rate deviation have met the preset convergence conditions. The final design scheme is fully verified to ensure that it can achieve the expected results in actual operation. The detailed parameters of the design scheme obtained in the Nth iteration, including the airflow path, conductive material coverage scheme, and negative ion placement scheme, are stored in the "Final Design Scheme" field in the equipment optimization database. Detailed design documents for the final design scheme are generated, including three-dimensional assembly model drawings, airflow field distribution diagrams, electric field uniformity index reports, etc., as records and displays of the design results. The final design scheme is converted into manufacturing and assembly guidance documents for actual equipment to ensure that the design scheme can be smoothly implemented.

[0251] Through multiple iterations, the accuracy and efficiency of the final design in terms of airflow distribution and electrostatic control are ensured. The iteration process and the final design are fully recorded to facilitate subsequent maintenance, optimization and technical exchanges. Through detailed design documents and parameter records, the feasibility and reliability of the final design in actual manufacturing and operation are ensured.

[0252] Step S2000, based on the final design of the internal structure of the equipment, integrate the electrostatic monitoring and dynamic adjustment mechanism, perform real-time adaptive control on the equipment, and establish an equipment protection mechanism.

[0253] Furthermore, step S2000 includes:

[0254] Step S2100, based on the final design of the internal structure of the equipment, an electrostatic monitoring unit and an electrostatic regulation unit are added; the electrostatic monitoring unit includes a data acquisition module, a data processing module and a data analysis module; the electrostatic regulation unit includes a controller and an actuator.

[0255] Specifically, electrostatic sensors are arranged at key positions inside the crushing chamber (such as airflow inlet, outlet, near the negative ion generator, etc.) to monitor the electrostatic voltage and charge in real time during the operation of the equipment. The data acquisition module is responsible for receiving the data collected by the electrostatic sensor; high-precision data acquisition equipment is used to ensure the accuracy and real-time nature of the collected data. The data processing module preprocesses the collected raw data, including filtering, denoising, normalization and other steps to improve the quality and availability of the data. Digital signal processing algorithms such as low-pass filtering and Gaussian filtering are used to remove interference signals and noise. The data analysis module conducts in-depth analysis of the preprocessed data, extracts key features, and identifies abnormal conditions. Feature extraction algorithms and pattern recognition techniques such as principal component analysis (PCA) and support vector machine (SVM) are used to extract electrostatic feature vectors and perform anomaly detection.

[0256] The controller receives the data analysis results from the electrostatic monitoring unit and generates adjustment instructions according to the preset control strategy. The embedded control system has high response speed and reliability, and can process the monitoring data in real time and generate corresponding control instructions. The actuator receives the adjustment instructions generated by the controller and adjusts the working parameters of the negative ion generator, such as working voltage and working frequency, to achieve dynamic adjustment of the electrostatic state. High-precision voltage and frequency adjustment devices are used to ensure that the working parameters of the negative ion generator can be accurately adjusted according to the instructions.

[0257] Ensure that the data transmission between the static monitoring unit and the static adjustment unit is stable and reliable, and use high-speed data transmission protocols such as Ethernet, wireless communication, etc. Realize real-time synchronization of monitoring and adjustment, ensure that the equipment can quickly respond to changes in the static state during operation, and maintain the stability and efficiency of the crushing process.

[0258] By adding an electrostatic monitoring unit, the electrostatic state of the equipment during operation can be monitored in real time, abnormal conditions can be discovered in time, and the normal operation of the equipment can be guaranteed. The electrostatic adjustment unit can adjust the working parameters of the negative ion generator in real time according to the monitoring data, dynamically control the degree of electrostatic aggregation, and improve the crushing efficiency and powder quality. Through real-time monitoring and dynamic adjustment, electrostatic over-aggregation or under-aggregation can be prevented, equipment failures and safety accidents can be reduced, and the service life of the equipment can be extended. The intelligent and automatic control of the equipment can be realized, manual intervention can be reduced, and production efficiency and product consistency can be improved. For example, in actual operation, the electrostatic sensor detects a sudden increase in the electrostatic voltage at a certain key position, and the data acquisition module records this change in real time. The data processing module removes noise by filtering, and the data analysis module identifies that this is an electrostatic over-aggregation state. After receiving this abnormal state, the controller immediately generates an adjustment instruction to instruct the actuator to increase the working voltage and frequency of the negative ion generator to increase the amount of negative ions generated and quickly neutralize excessive electrostatic charges. Through this dynamic adjustment process, the electrostatic voltage quickly returns to the normal range, avoiding the risk of powder agglomeration and equipment discharge, and ensuring the stability and efficiency of the crushing process.

[0259] Step S2200, performing real-time adaptive control on the device;

[0260] Further, step S2200 includes:

[0261] Step S2210, during the operation of the device, the static electricity monitoring unit receives data collected by the static electricity sensor in real time, processes and analyzes the data, and identifies abnormal static electricity accumulation;

[0262] Further, step S2210 includes:

[0263] Step S2211, arranging electrostatic sensors at key positions inside the pulverizing chamber to collect electrostatic voltage and charge data in real time during the operation of the equipment;

[0264] Specifically, select electrostatic sensors with high sensitivity and wide frequency response range, such as electrostatic voltage sensors and electrostatic charge sensors, to ensure that the electrostatic changes in the pulverizing chamber can be accurately captured. Electrostatic sensors are evenly arranged at key locations of the pulverizing chamber, such as the airflow inlet, airflow outlet, near the negative ion generator, and in the middle and bottom of the pulverizing chamber, to comprehensively monitor the electrostatic status of different areas. The sensor needs to be firmly installed on the inner wall or structural support of the pulverizing chamber to ensure that the sensor can work stably and avoid inaccurate data collection due to vibration or impact. Set an appropriate sampling frequency, such as 100 samples per second, to ensure that rapidly changing electrostatic data can be captured in real time. Set the sensor range according to the electrostatic working range of the equipment to avoid data overflow or distortion. By arranging multiple electrostatic sensors at key locations, comprehensive monitoring of the electrostatic status of different areas in the pulverizing chamber can be achieved to avoid missing potential electrostatic anomalies. Select highly sensitive sensors and reasonable sampling parameters to ensure that the collected data has high accuracy and high real-time performance, providing a reliable basis for subsequent data processing and analysis.

[0265] Step S2212, the data acquisition module of the static electricity monitoring unit receives static electricity voltage and charge data in real time, and transmits it to the data processing module;

[0266] Specifically, the data acquisition module receives data from the electrostatic sensor in real time through a high-speed data interface, such as USB, Ethernet or wireless transmission protocol. The received electrostatic voltage and charge data are seamlessly transmitted to the data processing module to ensure the integrity and continuity of the data. Each set of collected data is timestamped to ensure that the data from different sensors can be synchronously processed and analyzed in chronological order. Before the data processing module receives the data, the data acquisition module can perform short-term caching to avoid data loss due to instantaneous network delays or data processing lags. Through high-speed data interfaces and efficient data transmission protocols, the data collected by the electrostatic sensor can be quickly and accurately transmitted to the data processing module, reducing data delays and losses. Time synchronization and data caching mechanisms ensure the continuity and integrity of the data, providing reliable data support for subsequent analysis.

[0267] Step S2213, the data processing module pre-processes the collected electrostatic voltage and charge data, including data cleaning, data normalization and data filtering;

[0268] Specifically, filtering algorithms (such as median filtering and mean filtering) are used to remove random noise and interference signals in the data and improve the signal-to-noise ratio of the data. Outliers (such as instantaneous spikes) in the data are detected and processed to prevent them from misleading subsequent analysis. The data collected by different sensors are normalized according to the preset standard range (such as 0 to 1) to eliminate dimensional differences and improve the comparability of the data. The data curve is smoothed by methods such as moving average to reduce data fluctuations and improve data stability. Through data cleaning, normalization and filtering, the quality and reliability of the data are significantly improved, providing an accurate data basis for subsequent feature extraction and anomaly detection. High-quality data processing ensures that feature extraction and pattern recognition in the subsequent analysis process are more accurate and reduces the risk of misjudgment and missed judgment. For example, during the crushing process, a sensor recorded an instantaneous spike value due to external electromagnetic interference during a certain data collection. The data processing module identifies and removes this spike value through the outlier detection algorithm, and uses a low-pass filter to remove high-frequency noise, and finally obtains smooth and accurate electrostatic voltage and charge data to ensure the accuracy of subsequent analysis.

[0269] Step S2214, the data processing module performs feature extraction on the preprocessed electrostatic voltage and charge data to obtain an electrostatic feature vector, wherein the electrostatic feature vector includes an electrostatic voltage mean value, an electrostatic voltage peak value, an electrostatic voltage fluctuation frequency, an electrostatic charge mean value, and an electrostatic charge peak value;

[0270] Specifically, step S2214 includes the following contents:

[0271] Feature extraction method:

[0272] Statistical feature extraction:

[0273] Electrostatic voltage mean: Calculates the average value of the electrostatic voltage within a certain time window to reflect the overall electrostatic level.

[0274] Electrostatic voltage peak: Identifies the highest point of the electrostatic voltage, reflecting the extreme electrostatic state.

[0275] Mean electrostatic charge: Calculates the average value of the electrostatic charge within a certain time window to reflect the overall charge distribution.

[0276] Electrostatic Charge Peak: Identifies the highest point of electrostatic charge, reflecting the extreme charge state.

[0277] Frequency domain feature extraction:

[0278] Electrostatic voltage fluctuation frequency: Through frequency domain analysis methods such as Fourier transform, the main frequency components of electrostatic voltage changes are identified to reflect the stability and volatility of the electrostatic state.

[0279] Feature vector construction:

[0280] The above-extracted characteristic values ​​(electrostatic voltage mean value, electrostatic voltage peak value, electrostatic voltage fluctuation frequency, electrostatic charge mean value, electrostatic charge peak value) are combined into a multi-dimensional characteristic vector as a comprehensive description of the electrostatic state.

[0281] Normalize the feature vector:

[0282] The feature vectors are standardized to ensure that all features are in the same dimension, thereby improving the accuracy and stability of the subsequent classification model.

[0283] By extracting key features, complex time-series electrostatic data can be converted into concise feature vectors, simplifying subsequent pattern recognition and classification analysis. Selecting representative features can help improve the accuracy and robustness of the electrostatic aggregation abnormal state recognition model. Through feature extraction, data dimensionality reduction can be achieved, computational complexity can be reduced, and analysis efficiency can be improved.

[0284] For example, in a certain crushing process, the pre-processed electrostatic voltage data is subjected to feature extraction to obtain the following feature vector:

[0285] Average electrostatic voltage: 4.8 kV;

[0286] Electrostatic voltage peak: 6.2 kV;

[0287] Electrostatic voltage fluctuation frequency: 50 Hz;

[0288] Average electrostatic charge: 3.5 μC;

[0289] Peak value of electrostatic charge: 5.0 μC; this feature vector will be input into the electrostatic accumulation abnormal state recognition model to determine whether the current equipment operation state is normal.

[0290] Step S2215: The data analysis module uses a pre-built electrostatic accumulation abnormal state recognition model based on the electrostatic feature vector to perform real-time recognition and classification of the electrostatic accumulation state during the operation of the equipment, and sends the recognition result to the electrostatic regulation unit; the electrostatic accumulation abnormal state includes the following:

[0291] 1) Electrostatic over-aggregation state: The average electrostatic voltage and the average electrostatic charge exceed the preset safety threshold and last for longer than the preset time, indicating that the electrostatic aggregation level inside the grinding chamber is too high, which may cause problems such as powder agglomeration, wall sticking and discharge. It is necessary to adjust the negative ion generator working parameters in time to increase the amount of negative ions generated; the negative ion generator working parameters include working voltage and working frequency.

[0292] 2) Electrostatic under-aggregation state: The average electrostatic voltage and the average electrostatic charge are lower than the preset lower threshold, and the duration exceeds the preset time, indicating that the electrostatic aggregation level inside the pulverizing chamber is too low, which may affect the powder dispersibility and pulverizing efficiency, and the amount of negative ions generated needs to be appropriately reduced;

[0293] 3) Abnormal static electricity fluctuation: The static electricity voltage fluctuation frequency exceeds the preset frequency threshold, indicating that the static electricity accumulation state inside the crushing chamber is unstable, and there may be problems such as local discharge or charge mutation. It is necessary to optimize the negative ion placement plan and adjust the layout and working mode of the negative ion generator;

[0294] 4) Abnormal electrostatic polarization state: The electrostatic voltage peak and the electrostatic charge peak exceed the preset safety threshold, and the frequency of occurrence exceeds the preset frequency, indicating that there is serious electrostatic polarization inside the crushing chamber, which may cause problems such as damage to the equipment structure or change in powder properties. It is necessary to shut down the machine for maintenance in time, and optimize the internal structure design and material selection of the equipment.

[0295] The electrostatic aggregation abnormal state recognition model is trained and optimized using the support vector machine (SVM) algorithm. By performing offline analysis on a large amount of historical operating data, the characteristic patterns of the electrostatic aggregation abnormal state are extracted, and a multi-classifier model is established. During the model training process, parameters such as kernel function and penalty factor are introduced to improve the nonlinear fitting ability and generalization performance of the model. At the same time, methods such as cross-validation and grid search are used to optimize the model's hyperparameters to avoid overfitting and underfitting problems.

[0296] The training data set of the electrostatic aggregation abnormal state recognition model includes historical equipment operation data and test bench simulation data; the equipment historical operation data comes from crushing equipment of different models and specifications, covering a variety of typical material types and process parameter combinations; the test bench simulation data is obtained by numerical simulation and physical simulation of the electrostatic field inside the crushing chamber, simulating the electrostatic characteristic data under various electrostatic aggregation abnormal states. By comprehensively utilizing actual operation data and simulation data, the applicability and robustness of the electrostatic aggregation abnormal state recognition model are improved.

[0297] In order to adapt to the needs of electrostatic aggregation state identification under different crushing processes and material characteristics, the electrostatic aggregation abnormal state identification model adopts an adaptive learning mechanism, which can adjust and optimize the model parameters online according to the real-time operation data of the equipment; at the same time, when a new electrostatic aggregation abnormal state pattern is identified, the electrostatic aggregation abnormal state identification model can automatically perform incremental learning, add the new abnormal state pattern to the identification range, and continuously expand and improve the abnormal state knowledge base. Through the adaptive learning mechanism, the real-time and accuracy of the electrostatic aggregation abnormal state identification model are improved, and dynamic monitoring and diagnosis of the equipment operation status are realized.

[0298] Through the electrostatic aggregation abnormal state recognition model, the electrostatic abnormal state in the operation of the equipment can be accurately identified in real time, and countermeasures can be taken in time to prevent the problem from expanding. The multi-classifier model can distinguish different types of electrostatic abnormal states, take different adjustment measures in a targeted manner, and improve the intelligent control level of the equipment. By identifying and classifying electrostatic abnormal states, potential problems can be predicted in advance, preventive maintenance can be implemented, and equipment failures and downtime can be reduced. For example, in a certain operation, the electrostatic feature vector received by the data analysis module showed that the mean electrostatic voltage was 5.5 kV, exceeding the preset 4.5 kV safety threshold, and lasted for 10 minutes. The model identified it as an electrostatic over-aggregation state and immediately sent the recognition result to the electrostatic adjustment unit, instructing the controller to increase the operating voltage and frequency of the negative ion generator to reduce the electrostatic voltage and prevent powder agglomeration and equipment discharge.

[0299] Step S2220, the electrostatic adjustment unit dynamically adjusts the working parameters of the negative ion generator according to the analysis result of the electrostatic monitoring unit; the working parameters of the negative ion generator include working voltage and working frequency;

[0300] The purpose of step S2220 is to automatically adjust the working parameters (working voltage and working frequency) of the negative ion generator according to the real-time monitored static state, so as to effectively control and optimize the static accumulation state in the crushing chamber. This step ensures that the equipment can adaptively maintain the best static control effect under different operating conditions, improve crushing efficiency and product quality, and reduce equipment failures and maintenance costs.

[0301] Further, step S2220 includes:

[0302] Step S2221, the controller of the static electricity adjustment unit receives the static electricity accumulation state identification result sent by the static electricity monitoring unit, and generates an adjustment instruction according to a preset control strategy;

[0303] Specifically, the electrostatic monitoring unit transmits the electrostatic accumulation state identification result to the controller of the electrostatic adjustment unit through a data communication interface (such as Ethernet, wireless communication, etc.). The controller parses the received identification result and determines the type of electrostatic accumulation state of the current device (such as electrostatic over-accumulation, electrostatic under-accumulation, abnormal electrostatic fluctuation, and abnormal electrostatic polarization).

[0304] When the identification result is an electrostatic over-accumulation state, the controller determines whether the current operating voltage and operating frequency of the negative ion generator have reached the preset maximum value. If not, an adjustment instruction is generated to increase the operating voltage and operating frequency by a preset step size until the preset maximum value is reached or the electrostatic accumulation state returns to normal; if the maximum value has been reached, the current operating parameters are maintained unchanged, and a warning signal is sent to the equipment control system, indicating that the equipment needs to be shut down for maintenance or optimization of the internal structure design of the equipment.

[0305] When the identification result is an electrostatic under-aggregation state, the controller determines whether the current operating voltage and operating frequency of the negative ion generator have reached the preset minimum value. If not, an adjustment instruction is generated to reduce the operating voltage and operating frequency by a preset step size until the preset minimum value is reached or the electrostatic aggregation state returns to normal. If the minimum value has been reached, the current operating parameters are maintained unchanged, and a prompt signal is sent to the equipment control system, indicating that the degree of electrostatic aggregation is low under the current process parameters and the crushing process or material formula needs to be adjusted.

[0306] When the identification result is an abnormal electrostatic fluctuation state, the controller analyzes the relationship between the electrostatic voltage fluctuation frequency and the operating frequency of the negative ion generator to determine whether there is resonance or intermodulation interference; if so, an adjustment instruction is generated to adjust the operating frequency of the negative ion generator to a safe frequency band away from the electrostatic voltage fluctuation frequency, and increase the operating voltage to suppress electrostatic fluctuations; if not, an adjustment instruction is generated to adjust the operating voltage and operating frequency of the negative ion generator to the middle value within the preset safety range at the same time, and continuously monitor the changing trend of the electrostatic aggregation state, and make fine adjustments according to the changing trend.

[0307] When the identification result is an abnormal electrostatic polarization state, the controller immediately generates a shutdown command and sends a serious warning signal to the equipment control system, indicating the need for emergency maintenance and fault diagnosis; at the same time, the controller packages the current equipment structure parameters, process parameters and abnormal state data, and sends them to the equipment optimization database to trigger the equipment internal structure optimization process. During the shutdown maintenance and optimization period, the controller continuously monitors the electrostatic voltage and charge data inside the crushing chamber to determine whether the electrostatic polarization phenomenon has been eliminated, and decides whether to allow the equipment to restart based on the judgment result.

[0308] The controller generates precise adjustment instructions based on the real-time recognition results to achieve refined control of the negative ion generator and ensure that the electrostatic state is maintained within the ideal range. The automated control strategy enables the equipment to respond quickly to changes in the electrostatic state, reduce the lag of human intervention, and improve the dynamic adaptability of the equipment. Optimized working parameter adjustment can improve the neutralization effect of negative ions, enhance the powder dispersibility and crushing efficiency during the crushing process, and improve product quality. For example, suppose that during operation, the electrostatic monitoring unit identifies that the current state is electrostatic over-aggregation. After receiving this result, the controller decides to increase the operating voltage of the negative ion generator from 5 kV to 7 kV and the operating frequency from 50 Hz to 70 Hz according to the preset control strategy. The controller generates the corresponding adjustment instructions and transmits them to the actuator, which then adjusts the parameters of the negative ion generator to increase the amount of negative ions generated, effectively neutralize excessive electrostatic charges, and restore the normal operation of the equipment.

[0309] Step S2222: The actuator of the electrostatic adjustment unit receives the adjustment instruction generated by the controller, transmits the adjustment instruction to the negative ion generator, and adjusts the operating voltage and operating frequency of the negative ion generator.

[0310] The actuator receives the adjustment command from the controller through a wired or wireless communication interface. The actuator parses the adjustment command and determines the specific values ​​of the working voltage and working frequency that need to be adjusted. By adjusting the power module or the control circuit, the working voltage of the negative ion generator is accurately adjusted to the value specified by the command. Ensure that the adjusted working voltage can be output stably to avoid new static electricity problems caused by voltage fluctuations. By adjusting the frequency generator or the control circuit, the working frequency of the negative ion generator is accurately adjusted to the value specified by the command. Ensure that the adjusted working frequency does not resonate or intermodulate with the voltage fluctuation frequency inside the device to avoid causing new static electricity abnormal states. After the adjustment is completed, the actuator feeds back the actual working voltage and working frequency of the current negative ion generator to the controller to confirm the accuracy and effectiveness of the parameter adjustment. If the adjustment does not achieve the expected effect, the actuator can report to the controller to trigger further adjustment commands or fault handling procedures. The actuator can accurately adjust the working parameters of the negative ion generator according to the adjustment command to ensure the accuracy and effectiveness of the adjustment. Through precise parameter adjustment and stable output, the overall operation stability of the equipment is improved and the frequency of static electricity abnormal states is reduced. A fully automated parameter adjustment process is achieved, reducing manual intervention and improving the automation level and production efficiency of equipment operation. For example, after receiving the adjustment command, the actuator adjusts the operating voltage of the negative ion generator from 5 kV to 7 kV, and at the same time adjusts the operating frequency from 50 Hz to 70 Hz. After the adjustment is completed, the actuator feeds back the current voltage and frequency values ​​to the controller to confirm that the parameters have been accurately adjusted to the target values. At this time, the amount of negative ions generated by the negative ion generator increases, successfully neutralizing the excessive electrostatic charge, and the electrostatic state in the crushing chamber returns to normal, ensuring the stability and efficiency of the crushing process.

[0311] In step S2230, the electrostatic adjustment unit feeds back the adjusted negative ion generator operating parameters to the electrostatic monitoring unit to form a closed-loop control.

[0312] Specifically, the purpose of step S2230 is to establish a closed-loop control system, which continuously evaluates and optimizes the working parameters of the negative ion generator through a feedback mechanism, thereby achieving precise control of the electrostatic state in the crushing chamber. This process ensures that the equipment can continuously maintain the best electrostatic suppression effect in a dynamic operating environment, improve crushing efficiency and product quality, while extending the service life of the equipment and reducing maintenance costs.

[0313] After the electrostatic adjustment unit completes the adjustment of the working voltage and working frequency of the negative ion generator, it transmits the new working parameters (such as the adjusted working voltage and working frequency values) back to the electrostatic monitoring unit in real time. Closed-loop control is a feedback control system in which the output (here, the working parameters of the negative ion generator) is continuously monitored and fed back to the system to adjust and optimize the input (adjustment instructions) to ensure that the system output is stable within the expected range. Through closed-loop control, the system can respond to changes in the operation of the equipment in real time, automatically adjust the working parameters of the negative ion generator, and maintain the optimal operating state of the equipment. The closed-loop control system enables the equipment to autonomously adjust the working parameters of the negative ion generator according to the real-time monitored electrostatic state to adapt to different operating conditions and material characteristics. Continuous feedback and adjustment ensure that the electrostatic state of the equipment remains stable during operation, reducing problems such as powder agglomeration and wall sticking caused by excessive or insufficient electrostatic aggregation. Automated closed-loop control reduces dependence on operators, reduces the risk of human operating errors, and improves the reliability and consistency of equipment operation.

[0314] For example, during actual operation, the electrostatic adjustment unit adjusts the operating frequency of the negative ion generator from 70 Hz to 90 Hz to deal with the abnormal state of electrostatic fluctuations detected. After the adjustment is completed, the new operating frequency value (90 Hz) is immediately fed back to the electrostatic monitoring unit. After receiving the feedback, the electrostatic monitoring unit re-evaluates the current electrostatic state and finds that the electrostatic fluctuations have been significantly weakened and the electrostatic accumulation state has returned to normal. Based on this evaluation result, the system records the effect of this adjustment, and when encountering similar situations in the future, it refers to this adjustment strategy for optimization to ensure the continuous improvement of the electrostatic suppression effect.

[0315] Step S2300, establish a device protection mechanism to ensure safe operation of the device.

[0316] Step S2300 aims to establish a comprehensive equipment protection mechanism by setting safety thresholds and emergency response measures to ensure that the equipment can respond to various abnormal situations in a timely manner during operation, avoid safety accidents caused by static electricity accumulation, and ensure the safety of equipment and operators. At the same time, the mechanism also continuously improves the reliability and adaptability of the equipment through dynamic feedback and optimization.

[0317] Further, step S2300 includes:

[0318] Step S2310, setting the safety threshold range of the negative ion generator operating parameters, including the voltage threshold and the frequency threshold;

[0319] Determine the safety threshold of each parameter based on the design specifications of the negative ion generator and the data provided by the manufacturer. Adjust the threshold range in combination with the specific requirements of the crushing process to ensure the safe operation of the equipment under different process conditions. Optimize the threshold setting based on laboratory tests and actual operation data to ensure its scientificity and practicality. Set the highest safe value of the negative ion generator's working voltage (such as 8 kV). Exceeding this value may lead to excessive electrostatic neutralization, causing discharge or equipment damage. Set the lowest safe value of the working voltage (such as 3 kV). Below this value may lead to insufficient electrostatic accumulation, affecting crushing efficiency and product quality. Set the highest safe value of the negative ion generator's working frequency (such as 100 Hz). Exceeding this value may cause electromagnetic interference or resonance inside the equipment. Set the lowest safe value of the working frequency (such as 20 Hz). Below this value may lead to insufficient electrostatic neutralization effect and affect the stability of the crushing process. During the operation of the equipment, monitor the working voltage and working frequency of the negative ion generator in real time to ensure that they are within the set safety threshold range. When it is detected that the working parameters exceed the safety threshold range, the corresponding alarm mechanism is triggered to prompt the operator to take necessary emergency measures.

[0320] By setting reasonable safety thresholds, we can prevent safety accidents caused by abnormal operation of equipment parameters and protect the safety of equipment and operators. We can ensure that the negative ion generator works within a safe range, avoid equipment failures and unstable operation caused by abnormal parameters, and improve the overall reliability of the equipment. By setting thresholds scientifically, we can prevent potential safety risks in advance, reduce equipment downtime and maintenance costs, and improve production efficiency.

[0321] Step S2320: During the electrostatic adjustment process, if the operating parameters of the negative ion generator exceed the safety threshold range, the electrostatic adjustment unit promptly sends an alarm signal to the device control system to trigger the device protection mechanism;

[0322] Specifically, the electrostatic regulation unit continuously monitors the working voltage and working frequency of the negative ion generator to ensure that they are within the set safety threshold range. The real-time monitored working parameters are compared with the preset safety threshold to determine whether there is an out-of-range situation. Once the working voltage or working frequency is detected to exceed the safety threshold, the system immediately identifies it as an abnormal state. The electrostatic regulation unit generates a corresponding alarm signal, including alarm type (such as voltage over limit, frequency over limit), alarm level (such as serious, warning) and other information. The alarm signal is transmitted to the equipment control system through a reliable communication interface (such as industrial Ethernet, fiber optic communication, etc.). After receiving the alarm signal, the equipment control system confirms and records the alarm event. According to the alarm level, the preset protection measures, such as power off, shutdown, etc., are automatically executed to prevent the equipment from being damaged or safety accidents due to abnormal parameter operation. In some cases, the system may require confirmation or manual intervention by the operator to ensure the accuracy and necessity of the protection measures.

[0323] Once it is detected that the working parameters are beyond the safe range, the system can respond immediately and quickly trigger protective measures to reduce the possibility of accidents. Through timely alarm and protection mechanisms, serious accidents such as equipment damage, personal injury and production interruption caused by abnormal electrostatic parameters can be prevented. Recording each alarm event is convenient for subsequent fault analysis and equipment optimization, and improving the safety management level of the equipment. For example, during the operation of the equipment, the operating frequency of the negative ion generator suddenly rose to 120Hz, exceeding the preset safety upper limit of 100 Hz. The electrostatic regulation unit immediately generates a frequency over-limit alarm signal and transmits it to the equipment control system via industrial Ethernet. After receiving the alarm signal, the equipment control system automatically executes shutdown protection measures, disconnects the power supply of the negative ion generator, and prevents the equipment from being damaged due to excessive frequency. At the same time, the system records this alarm event for subsequent analysis and optimization design by technicians.

[0324] Step S2330: After receiving the alarm signal, the equipment control system immediately executes the preset protection measures.

[0325] Specifically, after receiving the alarm signal from the electrostatic adjustment unit, the equipment control system immediately responds and processes. The alarm information is displayed on the operation panel or monitoring interface to prompt the operator to pay attention. The preset protection measures include automatic protection measures (such as power off, shutdown, emergency braking, etc.) and manual protection measures (such as operator intervention, maintenance request, etc.). Power off immediately cuts off the power supply of the negative ion generator to prevent equipment damage or safety accidents caused by abnormal voltage or frequency. Shutdown automatically stops the operation process of the equipment to ensure that the equipment is in a safe state. Emergency braking, when necessary, activate the emergency braking system to quickly stop the mechanical movement of the equipment to prevent further damage. Operator intervention, based on the alarm information, the operator can manually perform further protection measures, such as shutting down specific circuits, adjusting equipment parameters, etc. Maintenance request, send a fault report to the maintenance team to start the equipment maintenance and repair process.

[0326] The equipment control system records the detailed information of the alarm event, including the alarm type, occurrence time, triggering reason, etc., and stores it in the "Alarm Record Table" in the equipment optimization database. The alarm information is sent to the remote monitoring center through the network to facilitate remote fault diagnosis and technical support. The execution status of the protection measures is monitored to ensure that measures such as power outages and shutdowns have been successfully implemented. The execution results of the protection measures are fed back to the operator and related systems to confirm that the equipment has entered a safe state.

[0327] By quickly executing preset protection measures, serious accidents caused by abnormal operation of equipment parameters can be prevented, ensuring the safety of equipment and personnel. Detailed event records and remote notification functions facilitate subsequent fault analysis and cause tracing, improving the efficiency and accuracy of fault handling. Enhance the reliability and response capability of the equipment control system and improve the overall safety management level of the equipment.

[0328] For example, after the electrostatic regulation unit triggers the frequency over-limit alarm, the equipment control system receives the alarm signal and immediately executes the power-off and shutdown protection measures, quickly cuts off the power supply of the negative ion generator and stops the equipment. The operation panel displays the alarm message "Frequency over-limit, equipment has been shut down", and the alarm event is recorded and sent to the remote monitoring center. After receiving the notification, the maintenance personnel went to the site to check the equipment and found that the frequency regulation module was faulty. They replaced the parts in time to ensure that the equipment resumed normal operation, avoiding further damage to the equipment and potential safety risks.

[0329] Example 2

[0330] This embodiment provides a platform for the internal structure design of the low-temperature airflow nano-crushing equipment for medicinal and edible plants on the basis of embodiment 1, such as Figure 7 As shown, including:

[0331] A first design scheme generating module: used to obtain characteristic parameters of a target crushing object, determine initial structural parameters of the equipment based on the characteristic parameters, and use the initial structural parameters of the equipment as first structural parameters; and generate a first design scheme according to the first structural parameters;

[0332] Solution optimization module: used to analyze and optimize the airflow field of the first design solution to obtain the second airflow path; predict the electrostatic accumulation and optimize the solution of the first design solution to obtain the second conductive material covering solution and the second negative ion placement solution; combine the second airflow path, the second conductive material covering solution and the second negative ion placement solution to generate the second design solution; iteratively optimize the second design solution to obtain the final device internal structure design solution;

[0333] Adaptive control module: It is used to integrate electrostatic monitoring and dynamic adjustment mechanisms based on the final equipment internal structure design plan, perform real-time adaptive control of the equipment, and establish an equipment protection mechanism.

[0334] In the first design solution generation module, the step of acquiring characteristic parameters of the target crushing object, determining initial structural parameters of the equipment based on the characteristic parameters, and using the initial structural parameters of the equipment as the first structural parameters includes:

[0335] Step S1110, obtaining the target crushing particle size and target yield of the target crushing object, and storing them in the process requirement table in the equipment optimization database;

[0336] Step S1120, obtaining material properties of the target crushing object, wherein the material properties include material density, material moisture content and material electrostatic properties, and storing the material properties in a material property table in the equipment optimization database;

[0337] Step S1130, based on the process requirement table and the material attribute table, combined with the preset equipment structure parameter range, determine the initial structure parameters of the equipment; the initial structure parameters include the crushing cavity space layout parameters, the airflow channel space layout parameters, the conductive material coverage ratio and the negative ion placement density; the crushing cavity space layout parameters include the size and geometric shape of the crushing cavity; the airflow channel space layout parameters include the cross-sectional shape, cross-sectional size, length, spatial position of the airflow channel, connection mode of the airflow channel and bending angle of the airflow channel;

[0338] Step S1140, storing the initial structural parameters of the device in the device optimization database as the first structural parameters.

[0339] In the first design solution generating module, generating the first design solution according to the first structural parameter includes:

[0340] Step S1210, determining a first airflow path of the pulverizing chamber according to the pulverizing chamber space layout parameters and the airflow channel space layout parameters in the initial structural parameters of the device;

[0341] Step S1220, determining a first conductive material covering scheme according to the conductive material covering ratio in the initial structural parameters of the device;

[0342] Step S1230, determining a first negative ion placement scheme according to the negative ion placement density in the initial structural parameters of the device;

[0343] Step S1240, combining the first airflow path, the first conductive material covering scheme and the first negative ion placement scheme to generate a first design scheme, and storing it in a device optimization database.

[0344] The step S1210 includes:

[0345] Step S1211, generating a three-dimensional solid model of the pulverizing cavity according to the spatial layout parameters of the pulverizing cavity, and marking geometric parameters and setting size constraints;

[0346] Step S1212, constructing a three-dimensional solid model of the airflow channel according to the spatial layout parameters of the airflow channel, and assembling and integrating it with the three-dimensional solid model of the pulverizing cavity to generate a three-dimensional assembly model of the pulverizing cavity;

[0347] Step S1213, in the three-dimensional assembly model of the crushing chamber, based on the gas-solid two-phase flow theory, by adjusting the spatial layout parameters of the airflow channel, the flow trajectory and velocity distribution of the airflow inside the crushing chamber are simulated, and the uniformity of the flow field is evaluated, and the airflow channel layout scheme with the best gas-solid two-phase flow effect is selected to form a first airflow path.

[0348] The step S1220 includes:

[0349] Step S1221, dividing the inner wall surface of the three-dimensional assembly model of the crushing cavity into n1 regular geometric partitions, and the size of each geometric partition is determined according to the coverage ratio of the conductive material;

[0350] Step S1222, determining the coverage position and coverage density of the conductive material layer, generating a three-dimensional solid model of the conductive material layer in each geometric partition, and constraining and assembling it with the three-dimensional assembly model of the pulverizing cavity to obtain the three-dimensional assembly model of the conductive pulverizing cavity;

[0351] Step S1223, based on the three-dimensional assembly model of the conductive pulverizing cavity, an electrostatic field simulation is performed to calculate the electric field uniformity index inside the pulverizing cavity;

[0352] Step S1224, adjusting the covering position and covering density of the conductive material layer according to the electric field uniformity index inside the pulverizing cavity, and obtaining the first conductive material covering scheme when the electric field uniformity index reaches a preset convergence condition.

[0353] The step S1230 includes:

[0354] Step S1231, according to the negative ion placement density, calculate the number and placement spacing of the negative ion generators required inside the pulverizing chamber; according to the number and placement spacing of the negative ion generators, generate a placement position matrix of the negative ion generators on the inner wall surface of the three-dimensional assembly model of the conductive pulverizing chamber;

[0355] Step S1232, inserting a three-dimensional solid model of the negative ion generator at each placement point of the placement position array, and constraining and assembling it with the three-dimensional assembly model of the conductive pulverizing cavity to obtain a three-dimensional assembly model of the negative ion integrated pulverizing cavity;

[0356] Step S1233, obtaining the optimal working parameter combination of the negative ion generator under different placement positions, and assigning it to the three-dimensional entity model of each negative ion generator in the three-dimensional assembly model of the negative ion integrated crushing cavity, to form a first negative ion placement plan.

[0357] In the scheme optimization module, the airflow field analysis and optimization of the first design scheme to obtain the second airflow path includes:

[0358] Step S1310, building an airflow field analysis model according to the first design scheme;

[0359] Step S1320, obtaining first airflow field parameters according to the airflow field analysis model, wherein the first airflow field parameters include velocity field, pressure field and turbulence intensity, and storing them in a device optimization database;

[0360] Step S1330, comparing the first airflow field parameter with the preset airflow field parameter to obtain a first airflow field deviation, where the first airflow field deviation includes a speed deviation, a pressure deviation, and a turbulence intensity deviation;

[0361] Step S1340: optimizing the first airflow path according to the first airflow field deviation to obtain a second airflow path.

[0362] In the scheme optimization module, the electrostatic aggregation prediction and scheme optimization of the first design scheme to obtain the second conductive material covering scheme and the second negative ion placement scheme include:

[0363] Step S1410, constructing an electrostatic accumulation prediction model based on the electrostatic characteristics of the material in the material attribute table;

[0364] Step S1420, obtaining a first powder predicted loss rate according to the first conductive material covering scheme, the first negative ion placement scheme and the electrostatic aggregation prediction model;

[0365] Step S1430, comparing the first powder predicted loss rate with a preset target loss rate to obtain a first loss rate deviation;

[0366] Step S1440, optimizing the first conductive material covering scheme and the first negative ion placement scheme based on the first loss rate deviation to obtain a second conductive material covering scheme and a second negative ion placement scheme.

[0367] In the scheme optimization module, the iterative optimization of the second design scheme to obtain the final device internal structure design scheme includes:

[0368] Step S1510, combining the second airflow path, the second conductive material covering scheme, and the second negative ion placement scheme to generate a second design scheme;

[0369] Step S1520, repeatedly executing step S1300 and step S1400, performing airflow field analysis, electrostatic accumulation prediction and scheme optimization on the second design scheme, until the Nth airflow field deviation and the Nth loss rate deviation obtained in the Nth iteration both meet the preset convergence conditions;

[0370] Step S1530, determining the Nth design scheme obtained by the Nth iteration as the final device internal structure design scheme, and storing it in the device optimization database.

[0371] In the adaptive control module, the integrated electrostatic monitoring and dynamic adjustment mechanism based on the final equipment internal structure design scheme includes: adding an electrostatic monitoring unit and an electrostatic adjustment unit based on the final equipment internal structure design scheme; the electrostatic monitoring unit includes a data acquisition module, a data processing module and a data analysis module; the electrostatic adjustment unit includes a controller and an actuator.

[0372] In the adaptive control module, the real-time adaptive control of the device includes:

[0373] Step S2210, during the operation of the device, the static electricity monitoring unit receives data collected by the static electricity sensor in real time, processes and analyzes the data, and identifies abnormal static electricity accumulation;

[0374] Step S2220, the electrostatic adjustment unit dynamically adjusts the working parameters of the negative ion generator according to the analysis result of the electrostatic monitoring unit; the working parameters of the negative ion generator include working voltage and working frequency;

[0375] In step S2230, the electrostatic adjustment unit feeds back the adjusted negative ion generator operating parameters to the electrostatic monitoring unit to form a closed-loop control.

[0376] The step S2210 includes:

[0377] Step S2211, arranging electrostatic sensors at key positions inside the pulverizing chamber to collect electrostatic voltage and charge data in real time during the operation of the equipment;

[0378] Step S2212, the data acquisition module of the static electricity monitoring unit receives static electricity voltage and charge data in real time, and transmits it to the data processing module;

[0379] Step S2213, the data processing module pre-processes the collected electrostatic voltage and charge data, including data cleaning, data normalization and data filtering;

[0380] Step S2214, the data processing module performs feature extraction on the preprocessed electrostatic voltage and charge data to obtain an electrostatic feature vector, wherein the electrostatic feature vector includes an electrostatic voltage mean value, an electrostatic voltage peak value, an electrostatic voltage fluctuation frequency, an electrostatic charge mean value, and an electrostatic charge peak value;

[0381] Step S2215, the data analysis module uses a pre-built electrostatic accumulation abnormal state recognition model based on the electrostatic feature vector to perform real-time recognition and classification of the electrostatic accumulation state during equipment operation, and sends the recognition result to the electrostatic regulation unit.

[0382] The step S2220 includes:

[0383] Step S2221, the controller of the static electricity adjustment unit receives the static electricity accumulation state identification result sent by the static electricity monitoring unit, and generates an adjustment instruction according to a preset control strategy;

[0384] Step S2222: The actuator of the electrostatic adjustment unit receives the adjustment instruction generated by the controller, transmits the adjustment instruction to the negative ion generator, and adjusts the operating voltage and operating frequency of the negative ion generator.

[0385] In the adaptive control module, the establishment of the device protection mechanism includes:

[0386] Step S2310, setting the safety threshold range of the negative ion generator operating parameters, including the voltage threshold and the frequency threshold;

[0387] Step S2320: During the electrostatic adjustment process, if the operating parameters of the negative ion generator exceed the safety threshold range, the electrostatic adjustment unit promptly sends an alarm signal to the device control system to trigger the device protection mechanism;

[0388] Step S2330: After receiving the alarm signal, the equipment control system immediately executes the preset protection measures.

[0389] The methods, systems, and devices of the present application may be implemented in many ways. For example, the methods, systems, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0390] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0391] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for designing the internal structure of a low-temperature airflow nano-crushing device for medicinal and edible plants, characterized in that: The method comprises: Acquire characteristic parameters of the target crushing object, determine initial structural parameters of the equipment based on the characteristic parameters, and use the initial structural parameters of the equipment as first structural parameters; generate a first design scheme according to the first structural parameters; analyze and optimize the airflow field of the first design scheme to obtain a second airflow path; predict electrostatic aggregation and optimize the first design scheme to obtain a second conductive material covering scheme and a second negative ion placement scheme; combine the second airflow path, the second conductive material covering scheme and the second negative ion placement scheme to generate a second design scheme; iteratively optimize the second design scheme to obtain a final device internal structure design scheme; On the basis of the final design of the internal structure of the equipment, an electrostatic monitoring and dynamic adjustment mechanism is integrated to perform real-time adaptive control of the equipment and establish an equipment protection mechanism.

2. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants according to claim 1, characterized in that: The characteristic parameters of the target crushing object include target crushing particle size, target output and material characteristics; The obtaining of characteristic parameters of the target crushing object comprises: Obtain the target crushing particle size and target yield of the target crushing object, and store them in the process requirement table in the equipment optimization database; Obtaining material properties of the target crushing object, wherein the material properties include material density, material moisture content and material electrostatic properties, and storing the material properties in a material property table in an equipment optimization database; Determining the initial structural parameters of the device based on the characteristic parameters includes: Based on the process requirement table and the material property table, combined with the preset equipment structure parameter range, the initial structure parameters of the equipment are determined; the initial structure parameters include the crushing chamber space layout parameters, the airflow channel space layout parameters, the conductive material coverage ratio and the negative ion placement density; the crushing chamber space layout parameters include the size and geometry of the crushing chamber; the airflow channel space layout parameters include the cross-sectional shape, cross-sectional size, length, spatial position of the airflow channel, connection method of the airflow channel and bending angle of the airflow channel.

3. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants according to claim 2, characterized in that: Generating a first design scheme according to the first structural parameter includes: Determining a first airflow path of the pulverizing cavity according to the pulverizing cavity space layout parameters and the airflow channel space layout parameters in the initial structural parameters of the equipment; Determining a first conductive material covering scheme according to a conductive material covering ratio in the initial structural parameters of the device; Determine a first negative ion placement plan according to the negative ion placement density in the initial structural parameters of the device; The first airflow path, the first conductive material covering scheme and the first negative ion placement scheme are combined to generate a first design scheme, and the first design scheme is stored in a device optimization database.

4. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants according to claim 3, characterized in that: Determining the first airflow path of the pulverizing cavity includes: Generate a three-dimensional solid model of the crushing cavity according to the spatial layout parameters of the crushing cavity, and perform geometric parameter annotation and size constraint setting; According to the spatial layout parameters of the airflow channel, a three-dimensional solid model of the airflow channel is constructed, and assembled and integrated with the three-dimensional solid model of the pulverizing cavity to generate a three-dimensional assembly model of the pulverizing cavity; In the three-dimensional assembly model of the grinding chamber, based on the gas-solid two-phase flow theory, by adjusting the spatial layout parameters of the airflow channel, the flow trajectory and velocity distribution of the airflow inside the grinding chamber are simulated, and the flow field uniformity is evaluated. The airflow channel layout scheme with the optimal gas-solid two-phase flow effect is selected to form the first airflow path.

5. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants according to claim 4, characterized in that: Determining the first conductive material coverage scheme includes: The inner wall surface of the three-dimensional assembly model of the crushing cavity is divided into n1 regular geometric partitions, and the size of each geometric partition is determined according to the coverage ratio of the conductive material; Determine the coverage position and coverage density of the conductive material layer, generate a three-dimensional solid model of the conductive material layer in each geometric partition, and constrain the three-dimensional solid model of the conductive material layer and the three-dimensional assembly model of the crushing cavity to obtain the three-dimensional assembly model of the conductive crushing cavity; Based on the three-dimensional assembly model of the conductive pulverizing cavity, the electrostatic field simulation is carried out to calculate the electric field uniformity index inside the pulverizing cavity; According to the electric field uniformity index inside the pulverizing cavity, the covering position and covering density of the conductive material layer are adjusted, and when the electric field uniformity index reaches a preset convergence condition, a first conductive material covering scheme is obtained.

6. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants according to claim 5, characterized in that: Determining the first negative ion placement scheme includes: According to the negative ion placement density, the number and placement spacing of the negative ion generators required inside the pulverizing chamber are calculated; according to the number and placement spacing of the negative ion generators, a placement position matrix of the negative ion generators is generated on the inner wall surface of the three-dimensional assembly model of the conductive pulverizing chamber; At each placement point of the placement position dot matrix, a three-dimensional solid model of the negative ion generator is inserted, and the three-dimensional solid model of the negative ion generator is constrained and assembled with the three-dimensional assembly model of the conductive pulverizing cavity to obtain a three-dimensional assembly model of the negative ion integrated pulverizing cavity; The optimal working parameter combination of the negative ion generator under different placement positions is obtained, and the optimal working parameter combination of the negative ion generator is assigned to each three-dimensional solid model of the negative ion generator in the three-dimensional assembly model of the negative ion integrated crushing cavity to form a first negative ion placement plan.

7. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants according to claim 3, characterized in that: The airflow field analysis and optimization of the first design scheme to obtain the second airflow path includes: According to the first design scheme, an airflow field analysis model is built; According to the airflow field analysis model, first airflow field parameters are obtained, wherein the first airflow field parameters include velocity field, pressure field and turbulence intensity, and the first airflow field parameters are stored in a device optimization database; Comparing the first airflow field parameter with the preset airflow field parameter to obtain a first airflow field deviation, wherein the first airflow field deviation includes a speed deviation, a pressure deviation, and a turbulence intensity deviation; The first airflow path is optimized according to the first airflow field deviation to obtain a second airflow path.

8. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants according to claim 3, characterized in that: The second conductive material covering scheme and the second negative ion placement scheme are obtained as follows: Based on the electrostatic characteristics of materials in the material property table, an electrostatic aggregation prediction model is constructed; According to the first conductive material covering scheme, the first negative ion placement scheme and the electrostatic aggregation prediction model, a first powder prediction loss rate is obtained; Comparing the first powder predicted loss rate with a preset target loss rate to obtain a first loss rate deviation; The first conductive material covering scheme and the first negative ion placement scheme are optimized based on the first loss rate deviation to obtain the second conductive material covering scheme and the second negative ion placement scheme.

9. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicinal and edible plants according to claim 1, characterized in that: Based on the final equipment internal structure design, the integrated electrostatic monitoring and dynamic adjustment mechanism includes: On the basis of the final design of the internal structure of the equipment, an electrostatic monitoring unit and an electrostatic regulating unit are added; the electrostatic monitoring unit includes a data acquisition module, a data processing module and a data analysis module; the electrostatic regulating unit includes a controller and an actuator; The real-time adaptive control of the device comprises: During the operation of the equipment, the static electricity monitoring unit receives the data collected by the static electricity sensor in real time, processes and analyzes it, and identifies abnormal states of static electricity accumulation; The electrostatic adjustment unit dynamically adjusts the working parameters of the negative ion generator according to the analysis results of the electrostatic monitoring unit; The electrostatic adjustment unit feeds back the adjusted working parameters of the negative ion generator to the electrostatic monitoring unit to form a closed-loop control.

10. A platform for designing the internal structure of a low-temperature airflow nano-crushing device for edible and medicinal plants, which is used to implement the method for designing the internal structure of a low-temperature airflow nano-crushing device for edible and medicinal plants according to any one of claims 1 to 9, characterized in that: The platform includes: A first design scheme generating module: used to obtain characteristic parameters of a target crushing object, determine initial structural parameters of the equipment based on the characteristic parameters, and use the initial structural parameters of the equipment as first structural parameters; and generate a first design scheme according to the first structural parameters; Solution optimization module: used to analyze and optimize the airflow field of the first design solution to obtain the second airflow path; predict the electrostatic accumulation and optimize the solution of the first design solution to obtain the second conductive material covering solution and the second negative ion placement solution; combine the second airflow path, the second conductive material covering solution and the second negative ion placement solution to generate the second design solution; iteratively optimize the second design solution to obtain the final device internal structure design solution; Adaptive control module: It is used to integrate electrostatic monitoring and dynamic adjustment mechanisms based on the final equipment internal structure design plan, perform real-time adaptive control of the equipment, and establish an equipment protection mechanism.

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