Method and Platform for the Internal Structure Design of a Low-Temperature Airflow Nanometer Comminution Equipment for Medicinal and Edible Homologous Plants

Abstract

The present invention relates to the technical field of structural design, and discloses a method and platform for the internal structure design of a low-temperature airflow nano-crushing device for medicinal and edible homologous plants. The method first obtains the characteristic parameters of the target crushing object to generate a first design scheme; then optimizes the first design scheme to obtain a second design scheme; iteratively optimizes the second design scheme to obtain the final internal structure design scheme of the device; finally, on the basis of the final scheme, an electrostatic monitoring and dynamic adjustment mechanism is integrated to perform real-time adaptive control on the device and establish a device protection mechanism. The method provided by the present invention optimizes the internal structure of the device by means of airflow field analysis, electrostatic aggregation prediction, etc., and introduces an electrostatic monitoring and dynamic adjustment mechanism to achieve intelligent control, improving the crushing efficiency and powder quality of medicinal and edible homologous plants, and at the same time enhancing the safety and reliability of the device operation.

Description

Technical Field

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

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

[0003] In the prior art, the patent application with the publication number US20220398351A1 discloses a reverse design method and system for micro-nano structures based on a deep neural network. This method uses a deep neural network to predict the electromagnetic response of micro-nano structures and obtains the optimal structural parameters that meet the target through iterative optimization according to 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 the crushing equipment for medicinal and edible homologous plants. More importantly, this technical solution does not consider 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, and this technical solution completely ignores the influence of static electricity, so it cannot solve the static electricity problem in the crushing process of medicinal and edible homologous plants.

[0004] The Chinese patent application with the authorization announcement number CN214749601U proposes a dust generating device, which includes a feeding mechanism, a mixing mechanism, a spraying mechanism and a storage bin, and also includes a negative pressure component. This device realizes the quantitative and uniform spraying of dust by controlling the feeding and airflow, and improves the performance test efficiency of dust monitors. However, this device is mainly applied to the field of environmental protection equipment for simulating a dust environment, and its design goal is to generate dust rather than crush materials. The core of this device is to control the generation and distribution of dust, rather than to achieve an efficient crushing process, and its structural design does not consider how to optimize the airflow field to achieve efficient crushing, so it cannot meet the requirements of low-temperature airflow nano-crushing of medicinal and edible homologous plants.

[0005] In summary, the prior art cannot solve the problem of how to refine the design of the internal structure of the pulverization chamber according to different material characteristics, especially electrostatic characteristics, during the low-temperature airflow nano-pulverization of medicine and food homologous plants, so as to avoid powder adhesion and agglomeration caused by static electricity, thereby improving the pulverization efficiency and product quality. Summary of the Invention

[0006] To overcome the above-mentioned defects of the prior art, the present invention provides a method and platform for designing the internal structure of a low-temperature airflow nano-pulverization device for medicine and food homologous plants. By obtaining the characteristic parameters of the pulverization object, optimizing the device structure and introducing an electrostatic monitoring and dynamic adjustment mechanism, it aims to solve the deficiencies of the prior art in terms of device intelligence and powder quality stability.

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

[0008] A method for designing the internal structure of a low-temperature airflow nano-pulverization device for medicine and food homologous plants, comprising:

[0009] Obtaining the characteristic parameters of the target pulverization object, determining the initial structure parameters of the device based on the characteristic parameters, and taking the initial structure parameters of the device as the first structure parameters; generating a first design plan according to the first structure parameters; performing airflow field analysis and optimization on the first design plan to obtain a second airflow path; performing electrostatic aggregation prediction and plan optimization on the first design plan to obtain a second conductive material coverage plan and a second negative ion placement plan; combining the second airflow path, the second conductive material coverage plan and the second negative ion placement plan to generate a second design plan; iteratively optimizing the second design plan to obtain the final internal structure design plan of the device;

[0010] Based on the final internal structure design plan of the device, integrating an electrostatic monitoring and dynamic adjustment mechanism, performing real-time adaptive control on the device, and establishing a device protection mechanism.

[0011] Further, the characteristic parameters of the target pulverization object include the target pulverization particle size, the target output, and the material characteristics;

[0012] The obtaining of the characteristic parameters of the target pulverization object includes:

[0013] Obtaining the target pulverization particle size and the target output of the target pulverization object, and storing them in the process requirement table in the device optimization database;

[0014] Obtaining the material characteristics of the target pulverization object, where the material characteristics include material density, material moisture content, and material electrostatic characteristics, and storing the material characteristics in the material attribute table in the device optimization database;

[0015] The determining of the initial structure parameters of the device based on the characteristic parameters includes:

[0016] Based on the process requirement table and the material property table, in combination with the preset range of equipment structure parameters, determine the initial equipment structure parameters; the initial structure parameters include the spatial layout parameters of the crushing cavity, the spatial layout parameters of the air flow channel, the coverage ratio of the conductive material, and the negative ion distribution density; the spatial layout parameters of the crushing cavity include the size and geometric shape of the crushing cavity; the spatial layout parameters of the air flow channel include the cross-sectional shape, cross-sectional size, length, spatial position of the air flow channel, connection mode of the air flow channel, and bending angle of the air flow channel.

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

[0018] According to the spatial layout parameters of the crushing cavity and the spatial layout parameters of the air flow channel in the initial equipment structure parameters, determine the first air flow path of the crushing cavity;

[0019] According to the coverage ratio of the conductive material in the initial equipment structure parameters, determine the first conductive material coverage scheme;

[0020] According to the negative ion distribution density in the initial equipment structure parameters, determine the first negative ion distribution scheme;

[0021] Combine the first air flow path, the first conductive material coverage scheme and the first negative ion distribution scheme to generate the first design scheme, and store the first design scheme in the equipment optimization database.

[0022] Further, the determining the first air flow path of the crushing cavity includes:

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

[0024] According to the spatial layout parameters of the air flow channel, construct a three-dimensional solid model of the air flow channel, and perform assembly integration with the three-dimensional solid model of the crushing cavity to generate a three-dimensional assembly model of the crushing cavity;

[0025] In the three-dimensional assembly model of the crushing cavity, based on the gas-solid two-phase flow theory, by adjusting the spatial layout parameters of the air flow channel, simulate the flow trajectory and velocity distribution of the air flow inside the crushing cavity, and evaluate the flow field uniformity, and select the air flow channel layout scheme with the best gas-solid two-phase flow effect to form the first air flow path.

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

[0027] Divide 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;

[0028] Determine the covering position and covering density of the conductive material layer, generate a three-dimensional solid model of the conductive material layer inside each geometric partition, and perform constrained assembly on the three-dimensional solid model of the conductive material layer and the three-dimensional assembly model of the crushing cavity to obtain a three-dimensional assembly model of the conductive crushing cavity;

[0029] Based on the three-dimensional assembly model of the conductive crushing cavity, perform electrostatic field simulation and calculate the electric field uniformity index inside the crushing cavity;

[0030] According to the electric field uniformity index inside the crushing cavity, adjust the covering position and covering density of the conductive material layer. When the electric field uniformity index reaches the preset convergence condition, obtain the first conductive material covering scheme.

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

[0032] According to the negative ion placement density, calculate the number and placement spacing of the negative ion generators required inside the crushing cavity; according to the number and placement spacing of the negative ion generators, generate a placement position lattice of the negative ion generators on the inner wall surface of the three-dimensional assembly model of the conductive crushing cavity;

[0033] At each placement point of the placement position lattice, insert the three-dimensional solid model of the negative ion generator, and perform constrained assembly on the three-dimensional solid model of the negative ion generator and the three-dimensional assembly model of the conductive crushing cavity to obtain a three-dimensional assembly model of the negative ion integrated crushing cavity;

[0034] Obtain the optimal working parameter combination of the negative ion generator at different placement positions, and assign the optimal working parameter combination of the negative ion generator 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 the first negative ion placement scheme.

[0035] Further, 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, build an airflow field analysis model;

[0037] According to the airflow field analysis model, obtain the first airflow field parameters, where the first airflow field parameters include velocity field, pressure field and turbulence intensity, and store the first airflow field parameters in the equipment optimization database;

[0038] Compare the first airflow field parameters with the preset airflow field parameters to obtain the first airflow field deviation, where the first airflow field deviation includes velocity deviation, pressure deviation and turbulence intensity deviation;

[0039] Optimize the first airflow path according to the first airflow field deviation to obtain the second airflow path.

[0040] Further, the obtaining of the second conductive material coverage plan and the second negative ion placement plan includes:

[0041] Based on the electrostatic properties of the materials in the material property table, construct an electrostatic aggregation prediction model;

[0042] According to the first conductive material coverage plan, the first negative ion placement plan, and the electrostatic aggregation prediction model, obtain the first predicted powder loss rate;

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

[0044] Optimize the first conductive material coverage plan and the first negative ion placement plan based on the first loss rate deviation to obtain the second conductive material coverage plan and the second negative ion placement plan.

[0045] Further, the integration of the electrostatic monitoring and dynamic adjustment mechanism based on the final device internal structure design plan includes:

[0046] On the basis of the final device internal structure design plan, add an electrostatic monitoring unit and an electrostatic adjustment unit; 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;

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

[0048] During the operation of the device, the electrostatic monitoring unit receives the data collected by the electrostatic sensor in real time, processes and analyzes it, and identifies the abnormal electrostatic aggregation state;

[0049] The electrostatic adjustment unit dynamically adjusts the working parameters of the negative ion generator according to the analysis result 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 the internal structure design of a low-temperature airflow nano-grinding device for medicated and edible homologous plants, which is used to implement the method for the internal structure design of a low-temperature airflow nano-grinding device for medicated and edible homologous plants, and the platform includes:

[0052] A first design scheme generation module: used to obtain the characteristic parameters of the target grinding object, determine the initial structure parameters of the device based on the characteristic parameters, and use the initial structure parameters of the device as the first structure parameters; according to the first structure parameters, generate a first design scheme;

[0053] Scheme optimization module: used to perform airflow field analysis and optimization on the first design scheme to obtain the second airflow path; perform electrostatic aggregation prediction and scheme optimization on the first design scheme to obtain the second conductive material coverage scheme and the second negative ion placement scheme; combine the second airflow path, the second conductive material coverage scheme and the second negative ion placement scheme to generate the second design scheme; iteratively optimize the second design scheme to obtain the final internal structure design scheme of the device;

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

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] By obtaining the characteristic parameters of the target crushing object and determining the initial structure parameters of the device based on the characteristic parameters, the present invention generates the first design scheme, realizes the matching of the internal structure of the device with the properties of the crushing object, and improves the crushing efficiency and powder quality.

[0057] The present invention adopts airflow field analysis and optimization technology. By simulating the flow trajectory and velocity distribution of the airflow inside the crushing cavity and evaluating the flow field uniformity, the airflow channel layout scheme is optimized, the optimal effect of gas-solid two-phase flow is realized, and the crushing efficiency and energy utilization rate are improved.

[0058] The present invention introduces an electrostatic aggregation prediction model. By optimizing the conductive material coverage scheme and the negative ion placement scheme, the uniform distribution of the electrostatic field inside the crushing cavity is realized, the electrostatic aggregation of the powder is effectively suppressed, the powder loss rate is reduced, and the powder dispersibility and preparation quality are improved.

[0059] The present invention adopts an iterative optimization strategy. Through multiple rounds of airflow field analysis, electrostatic aggregation prediction and scheme optimization, the internal structure design of the device is continuously improved, and finally the design scheme with the optimal performance is obtained, improving the design efficiency and success rate.

[0060] Based on the final design scheme, the present invention integrates an electrostatic monitoring and dynamic adjustment mechanism. By real-time monitoring the electrostatic aggregation state during the crushing process and dynamically adjusting the working parameters of the negative ion generator according to the monitoring results, the real-time adaptive control of the device operation state is realized, and the intelligent level and operation reliability of the device are improved.

[0061] The present invention constructs an electrostatic aggregation abnormal state recognition model. By continuously optimizing the model performance through an adaptive learning mechanism, the rapid diagnosis and early warning of the electrostatic aggregation abnormal state during the device operation process are realized, providing strong support for device maintenance and fault prevention.

[0062] The present invention has established a perfect equipment protection mechanism. By setting the safety threshold range of the working parameters of the negative ion generator and monitoring the equipment operation 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 technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0064] Figure 1 It is a method flowchart for the internal structure design of the low-temperature airflow nano-grinding equipment for Chinese herbal and edible homologous plants of the present invention;

[0065] Figure 2 It is a method flowchart for determining the initial structure parameters of the equipment in the present invention;

[0066] Figure 3 It is a method flowchart for generating the first design scheme according to the first structure parameters in the present invention;

[0067] Figure 4 It is a method flowchart for determining the first airflow path in the present invention;

[0068] Figure 5 It is a method flowchart for determining the first conductive material covering scheme in the present invention;

[0069] Figure 6 It is a method flowchart for performing airflow field analysis and optimization on the first design scheme to obtain the second airflow path in the present invention;

[0070] Figure 7 It is a functional module diagram of the platform for the internal structure design of the low-temperature airflow nano-grinding equipment for Chinese herbal and edible homologous plants of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0072] Embodiment 1

[0073] Please refer to Figure 1As shown, this embodiment provides a method for the internal structure design of a low-temperature airflow nano-crushing device for medicine and food homologous plants, including:

[0074] Step S1000, obtain the characteristic parameters of the target crushing object, determine the initial structure parameters of the device based on the characteristic parameters, and use them as the first structure parameters; according to the first structure parameters, generate the first design scheme; conduct airflow field analysis and optimization on the first design scheme to obtain the second airflow path; conduct electrostatic aggregation prediction and scheme optimization on the first design scheme to obtain the second conductive material coverage scheme and the second negative ion placement scheme; combine the second airflow path, the second conductive material coverage scheme and the second negative ion placement scheme to generate the second design scheme; iteratively optimize the second design scheme to obtain the final internal structure design scheme of the device;

[0075] Further, step S1000 includes:

[0076] Step S1100, obtain the characteristic parameters of the target crushing object, determine the initial structure parameters of the device based on the characteristic parameters, and use them as the first structure parameters; the characteristic parameters include the target crushing particle size, the target output, and the material properties;

[0077] Further, as Figure 2 shown, step S1100 includes:

[0078] Step S1110, obtain the target crushing particle size and the target output of the target crushing object, and store them in the process requirement table in the device optimization database;

[0079] Specifically, the target crushing granularity refers to the particle size distribution range of the material after crushing, usually measured in micrometers (μm) or nanometers (nm). For example, for a certain medicinal and edible homologous plant, the target granularity may be set 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 measured in kilograms per hour (kg / h) or tons per day (t / day). For example, the target output is set at 50 kilograms per hour to meet the requirements of industrial production. The target granularity and output are the basic parameters for the design of the crushing equipment, directly affecting the structural dimensions, air flow design, and power configuration of the equipment. Accurately obtaining these parameters helps to formulate a scientific design plan, avoid the equipment being too large or too small, and ensure production efficiency and product quality. By clarifying the target granularity and output, it is possible to more specifically select the appropriate crushing cavity size, air flow intensity, and processing speed, avoiding resource waste. At the same time, the clarification of these parameters helps to optimize the air flow path and conductive material coverage scheme in subsequent steps, improving the overall performance of the equipment. For example, assume that the target crushing object is a certain traditional Chinese medicine, and its active ingredients have the best medicinal effects when they are below 100 nanometers. The target output is set at 50 kilograms per hour, which means that the equipment needs to maintain a stable processing speed and high granularity uniformity while efficiently crushing. Store these data in the process requirement table to provide a clear basis for subsequent design.

[0080] Step S1120: Obtain the material properties of the target crushing object. The material properties include material density, material moisture content, and material electrostatic properties, and store the material properties in the material attribute table in the equipment optimization database.

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

[0082] Step S1130, based on the process requirement table and the material property table, combined with the preset range of equipment structure parameters, determine the initial equipment structure parameters; the initial structure parameters include the spatial layout parameters of the pulverization cavity, the spatial layout parameters of the air flow channel, the conductive material coverage ratio, and the negative ion distribution density; the spatial layout parameters of the pulverization cavity include the size and geometric shape of the pulverization cavity; the spatial layout parameters of the air flow channel include the cross-sectional shape, cross-sectional size, length, spatial position of the air flow channel, connection method of the air flow channel, and bending angle of the air flow channel;

[0083] Specifically, the dimensions of the crushing chamber include its length, width, and height, which are determined according to the target output and particle size. The geometric shape of the crushing chamber, such as cylindrical or cubic, affects the air flow distribution and crushing effect. These parameters are closely related to the target output, particle size, and material properties. The spatial layout parameters of the air flow channels include the cross-sectional shape, cross-sectional size, length, spatial position of the air flow channels, connection method of the air flow channels, and bending angle of the air flow channels; the cross-sectional shape can be 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 air flow channels to ensure that the air flow has sufficient kinetic energy and appropriate flow velocity during the crushing process. The length refers to the total length of the air flow channels, which affects the distribution of the air flow in the crushing chamber and the energy loss. The spatial position of the air flow channels 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 air flow channels to ensure that the air flow can be evenly distributed throughout the crushing chamber and avoid excessive or too low local flow velocity. The connection method of the air flow channels includes straight connection and bent connection, which affects the flow path and energy loss of the air flow. The bending angle of the air flow channels, such as 90 degrees or 45 degrees, affects the turning and flow efficiency of the air flow. The coverage ratio of the conductive material refers to the ratio of the coverage area of the conductive material on the inner wall of the crushing chamber to the total inner wall area, which affects the uniformity of the electrostatic field distribution. 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 the equipment structure parameters needs to comprehensively consider the material properties and process requirements to ensure that the equipment has good electrostatic control ability and crushing efficiency while meeting the output and particle size requirements. The preset range of structure parameters provides the feasibility boundary for the design and avoids blindness in the design process. By scientifically determining the spatial layout parameters of the crushing chamber and the air flow channels, the distribution of the air flow in the chamber can be optimized, and the crushing efficiency and particle size uniformity can be improved. At the same time, a reasonable coverage ratio of the conductive material and negative ion placement density can effectively inhibit the electrostatic accumulation, prevent powder agglomeration and equipment damage, and enhance the stability and reliability of the equipment.

[0085] For example, assume the target particle size is 100 nanometers and the output is 50 kg / h. According to the material density and moisture content, the crushing chamber is preliminarily determined to be a cylindrical shape with a diameter of 1 meter and a height of 2 meters. The air flow channels adopt a circular cross-section with a diameter of 0.2 meters and a length of 2 meters, and are arranged at the bottom and top of the chamber to ensure uniform air flow distribution. The coverage ratio of the conductive material is set at 70%, and the negative ion placement density is 10 negative ion generators per square meter. Such a design can effectively control the electrostatic accumulation and improve the crushing efficiency while meeting the output and particle size requirements.

[0086] Step S1140: Store the initial device structure parameters in the device optimization database as the first structure parameters.

[0087] Specifically, step S1140 systematically stores the initial structure parameters determined in step S1130, including the spatial layout parameters of the crushing cavity, the spatial layout parameters of the air flow channel, the coverage ratio of the conductive material, and the anion distribution density, in the device optimization database. The process requirement table and the material property table in the database are correlated with the structure parameters, providing data support for subsequent design and optimization. The device optimization database is the core of data management for the entire design process, ensuring that all design parameters and process requirements are effectively recorded and traceable. The structured storage of the database facilitates the rapid retrieval and update of information, supporting subsequent design optimization and iteration processes. By systematically storing the initial structure parameters, the design parameters can be accessed and modified at any time, promoting collaborative work in the design process. At the same time, the existence of the database provides a reliable data basis for air flow field analysis, electrostatic accumulation prediction, and scheme optimization in subsequent steps, ensuring the scientific and systematic nature of the design process.

[0088] For example, after inputting the above initial structure parameters into the device optimization database, it is possible to understand whether the current design meets the production and particle size requirements by querying the process requirement table, and to understand the density, moisture content, and electrostatic properties of the material through the material property table, and then adjust the air flow channel layout and the coverage ratio of the conductive material to optimize the device performance.

[0089] Through step S1100, it is possible to systematically obtain and analyze the characteristic parameters of the target crushing object, and combine the material characteristics and process requirements to scientifically determine the initial device structure parameters. This process not only ensures the pertinence and effectiveness of the device design, but also improves the crushing efficiency, product quality, and operation stability of the device by optimizing the air flow channel layout and electrostatic control measures. The systematic data management and scientific design method lay a solid foundation for subsequent air flow field analysis and optimization, electrostatic accumulation prediction and scheme optimization, and iterative optimization of the final device structure design, achieving a comprehensive improvement in device performance and reliability guarantee.

[0090] Step S1200: Generate the first design scheme based on the first structure parameters;

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

[0092] Step S1210: Determine the first air flow path of the crushing cavity according to the spatial layout parameters of the crushing cavity and the spatial layout parameters of the air flow channel in the initial device structure parameters;

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

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

[0095] Step S1211, 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 dimension constraint setting;

[0096] Specifically, use computer-aided design (CAD) software to create a detailed three-dimensional model according to the spatial layout parameters of the crushing cavity (such as size and geometric shape). This model should accurately reflect the actual structure of the cavity, including the inlet, outlet, and internal structural features. Mark the key geometric parameters in the three-dimensional model, such as the length, width, height, and wall thickness of the cavity. These parameters are the basis for subsequent air-flow simulation and optimization, ensuring the accuracy and operability of the model. According to the design requirements and manufacturing process, set dimension constraints for the key dimensions in the model to ensure that the dimensions of each part meet the actual manufacturing capacity and design specifications during the design process. Generating an accurate three-dimensional solid model is a prerequisite for optimizing the air-flow path. Geometric parameter annotation and dimension constraint not only improve the accuracy of the model, but also provide a reliable data basis for subsequent fluid dynamics simulation. Through detailed annotation and constraint, air-flow simulation deviation caused by inaccurate dimensions during the design process can be avoided. By accurately generating and annotating the three-dimensional solid model of the crushing cavity, the flow of air in the cavity can be more accurately simulated, ensuring the scientific nature and effectiveness of subsequent optimization steps. This process reduces design errors, improves the efficiency of air-flow path optimization and the reliability of the results, thereby enhancing the crushing performance of the entire equipment and product quality.

[0097] For example, assume that a cylindrical crushing cavity with a diameter of 1 meter and a height of 2 meters needs to be designed. After generating a three-dimensional model through CAD software, mark out the various dimension parameters of the cavity, such as diameter, height, and wall thickness (for example, 5 millimeters), and set dimension constraints to ensure that the model does not have problems due to dimensions exceeding the manufacturing capacity during the design process. Such a model provides an accurate data basis for subsequent air-flow simulation.

[0098] Step S1212, construct a three-dimensional solid model of the air-flow channel according to the spatial layout parameters of the air-flow channel, and assemble and integrate it with the three-dimensional solid model of the crushing cavity to generate a three-dimensional assembly model of the crushing cavity;

[0099] Specifically, according to the spatial layout parameters of the air flow channel (such as cross-sectional shape, size, length, spatial position, connection method, and bending angle), a detailed 3D model of the air flow channel is constructed using CAD software. The design of the air flow channel needs to consider the flow characteristics of the air flow and the crushing efficiency to ensure that the air flow can be evenly distributed throughout the crushing cavity. The 3D model of the air flow channel is assembled and integrated with the 3D model of the crushing cavity to form a complete 3D assembly model of the crushing cavity. This step requires ensuring the precise docking of the interface between the air flow channel and the cavity to avoid air leakage or blockage. After the assembly and integration, a preliminary inspection is carried out on the 3D assembly model to ensure that the layout of the air flow channel is reasonable, meets the design requirements, and eliminates possible geometric conflicts or structural defects. The design of the air flow channel directly affects the distribution and flow characteristics of the air flow in the crushing cavity. By constructing a 3D solid model of the air flow channel and precisely assembling it with the crushing cavity model, the rationality and effectiveness of the air flow 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 3D model of the air flow channel, the design team can visually observe and analyze the distribution of the air flow in the crushing cavity. This process helps to discover and solve potential problems in the design, such as air flow blockage or uneven distribution, thereby improving the optimization effect of the air flow path and ultimately enhancing the overall performance and reliability of the crushing equipment.

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

[0101] Step S1213, in the 3D assembly model of the crushing cavity, based on the gas-solid two-phase flow theory, by adjusting the spatial layout parameters of the air flow channel, simulate the flow trajectory and velocity distribution of the air flow inside the crushing cavity, evaluate the uniformity of the flow field, and select the air flow channel layout scheme with the best gas-solid two-phase flow effect to form the first air flow path.

[0102] Specifically, gas-solid two-phase flow refers to the behavior and interaction of gas and solid particles during simultaneous flow. In comminution equipment, the gas stream carries powder particles for efficient comminution and dispersion, and the interaction between the gas stream and the powder determines the comminution effect and the uniformity of the powder. Using fluid dynamics (CFD) software (such as ANSYS Fluent, COMSOL Multiphysics, etc.), the assembled and integrated three-dimensional model is imported for gas flow simulation. By setting appropriate boundary conditions (such as the flow velocity and pressure at the inlet and outlet) and material property parameters (such as powder density, particle size distribution, etc.), the flow trajectory and velocity distribution of the gas stream in the cavity are simulated. Through the simulation results, the uniformity indexes of the gas stream in the cavity are evaluated, such as the uniformity of the velocity distribution and the distribution of the 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 locally too high or too low gas stream velocity. According to the flow field evaluation results, the spatial layout parameters of the gas flow channel (such as the bending angle of the channel, connection method, inlet and outlet positions, etc.) are adjusted, and the gas flow simulation is carried out again until the flow field uniformity reaches the preset optimization standard. Through multiple iterations and optimizations, the gas flow channel layout scheme with the best gas-solid two-phase flow effect is selected to form the final first gas flow path. This path should ensure the uniform distribution and efficient flow of the gas stream throughout the comminution cavity, improving the comminution effect and product quality.

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

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

[0105] Step S1220, determine the first conductive material coverage scheme according to the conductive material coverage ratio in the initial structure parameters of the device;

[0106] The design of the conductive material covering scheme aims to optimize the electrostatic field distribution inside the crushing cavity by reasonably covering the inner wall of the crushing cavity, and prevent powder from agglomerating or sticking to the wall due to electrostatic accumulation.

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

[0108] Step S1221, divide 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 conductive material covering ratio;

[0109] Specifically, according to the conductive material covering ratio, divide the inner wall surface of the crushing cavity into multiple regular geometric partitions (such as rectangles, circles, etc.). The size and shape of each partition should meet the covering requirements of the conductive material to achieve the best electrostatic field control. Determine the number of partitions according to the geometric complexity of the inner wall and the design requirements. For a cavity with a simple shape, fewer partitions can be used; while for a cavity with a complex geometric shape, more partitions are required to achieve precise coverage. According to the conductive material covering ratio, calculate the area to be covered by each partition. For example, if the conductive material covering ratio is 70% and the total inner wall area is 100 square meters, the covering area of each partition should be allocated according to 70 square meters. The conductive material covering ratio refers to the ratio of the covering area of the conductive material on the inner wall of the crushing cavity to the total inner wall area. Reasonably dividing the inner wall surface area and determining the size of each partition helps to achieve uniform coverage of the conductive material, optimize the electrostatic field distribution, and prevent powder agglomeration and wall sticking caused by electrostatic accumulation. By dividing the inner wall surface into multiple geometric partitions and accurately calculating the size of each partition according to the covering ratio, the design team can ensure the uniformity of the conductive material coverage. 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 electrostatic problems.

[0110] For example, assume that the total inner wall area of the crushing cavity is 100 square meters and the conductive material covering ratio is set at 70%. The design team decides to divide the inner wall into 10 regular geometric partitions, and each partition needs to cover 7 square meters. According to the shape and design requirements of the cavity, rectangular partitions are selected, and the length and width of each partition are 2 meters and 3.5 meters respectively to ensure uniform coverage of the conductive material.

[0111] Step S1222, determine the covering position and covering density of the conductive material layer, generate a three-dimensional solid model of the conductive material layer inside each geometric partition, and perform constrained assembly with the three-dimensional assembly model of the crushing cavity to obtain a three-dimensional assembly model of the conductive crushing cavity;

[0112] Specifically, the covering positions refer to the specific distribution areas of the conductive material on the inner wall surface of the crushing cavity. 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 powder from agglomerating or sticking to the wall due to electrostatic accumulation. According to the functional requirements and structural design of the equipment, determine which areas need to be covered with more conductive material. For example, at the air inlet and outlet, due to the high air flow velocity, a strong electrostatic field is likely to be generated, so they need to be covered preferentially. The covering density refers to the covering ratio or thickness of the conductive material within each geometric partition. A high covering density means that a larger proportion of the inner wall is covered with the conductive material, which helps to more effectively neutralize and disperse static charges. Consider the conductivity, mechanical strength, and durability of the conductive material to select an appropriate covering density. A higher covering density can provide better conductivity, but it will also increase the material cost and manufacturing complexity.

[0113] Use advanced computer-aided design (CAD) software, such as SolidWorks, AutoCAD, or CATIA, to create a detailed three-dimensional model of the conductive material layer. These software can accurately simulate the geometric shape, thickness, and distribution position of the conductive material. According to the shape of each geometric partition (such as rectangular, circular, or elliptical), design the shape of the conductive material layer to ensure that it is consistent with the partition geometry. Set the thickness of the conductive material layer according to the covering density requirements. For example, in areas with high electric field intensity, the thickness of the conductive material layer may be set to 0.7 mm, while in other areas it is set to 0.5 mm. Ensure that the position of the conductive material layer exactly coincides with the boundary of the geometric partition, avoiding any geometric deviation or misalignment.

[0114] Constrained assembly refers to accurately embedding the three-dimensional solid model of the conductive material layer into the corresponding geometric partition in the three-dimensional assembly model of the crushing cavity. This process ensures a perfect fit between the conductive material layer and the inner wall surface of the cavity, avoiding air leakage or loosening of the conductive layer. In the three-dimensional assembly model of the crushing cavity, position the conductive material layer in each geometric partition accurately according to the predetermined covering positions and covering densities. Ensure that the geometric shape of the conductive material layer model exactly matches the geometric shape of the inner wall partition of the crushing cavity, avoiding any geometric conflict or mismatch. Apply geometric constraint conditions (such as surface 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 the operation of the equipment. Check the assembled model to ensure that all conductive material layers have been correctly assembled without omission or error.

[0115] The three-dimensional assembly model of the conductive crushing cavity refers to the complete structure after successfully assembling the conductive material layers within all geometric partitions onto the three-dimensional model of the crushing cavity. This model comprehensively reflects the distribution of the conductive material on the entire inner wall of the cavity, 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 electrical conductivity and good mechanical strength, and can effectively neutralize static charges, preventing powder agglomeration and wall sticking phenomena. The thickness of the conductive material layer needs to be optimized according to the results of electrostatic field simulation and actual operation requirements. A thicker conductive layer can provide higher electrical conductivity, but at the same time will increase material costs and manufacturing complexity. Therefore, it is necessary to find the best balance between electrical conductivity and cost. In areas with complex geometric shapes or drastic changes in air flow, refining the geometric partitions can achieve more precise coverage of the conductive material. This helps to improve the uniformity of the electric field, further enhancing the crushing effect and equipment stability. The shape of the partitions should be designed according to the overall geometric shape of the crushing cavity and the characteristics of the air flow distribution. For example, a more detailed partition shape can be adopted at the curved connection to adapt to the complex electric field distribution when the air flow turns.

[0116] By accurately determining the coverage position and coverage density of the conductive material layer, the three-dimensional assembly model of the conductive crushing cavity can achieve a more uniform electrostatic field distribution. This effectively reduces the static charge accumulation of the powder during the crushing process, prevents powder agglomeration and wall sticking phenomena, and improves the crushing efficiency and product quality. A uniform electrostatic field helps to stably neutralize the charges on the powder particles, preventing 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 electrostatic field distribution can be achieved throughout the cavity, ensuring that each powder particle can be evenly affected by the air flow and negative ions.

[0117] Step S1223, based on the three-dimensional assembly model of the conductive crushing cavity, conduct electrostatic field simulation and calculate the electric field uniformity index inside the crushing cavity;

[0118] Specifically, using electromagnetic field simulation software (such as ANSYS Maxwell, COMSOL Multiphysics, etc.), perform electrostatic field simulation on the three-dimensional assembly model of the conductive crushing cavity. Set appropriate electric field sources and boundary conditions to simulate the electric field distribution inside the cavity. Calculate and evaluate the uniformity index of the electric field inside the cavity, such as the standard deviation of the electric field strength, electric field gradient, etc. The higher the electric field uniformity index, the more uniform the electric field distribution, which helps to reduce the electrostatic accumulation and agglomeration of the powder. Electrostatic field simulation is a key step in evaluating the effectiveness of the conductive material coverage scheme. Through precise simulation, uneven regions in the electric field distribution can be identified, providing data support for subsequent optimization. The electric field uniformity index is an important indicator for measuring the quality of the electrostatic field distribution, directly related to the performance of the crushing equipment and the product quality. Through electrostatic field simulation and calculation of the uniformity index, the design team can scientifically evaluate the effectiveness of the conductive material coverage scheme. This process helps to discover deficiencies in the design, guide subsequent adjustments to the coverage position and density, ensure the uniform distribution of the electrostatic field, thereby improving the crushing efficiency and powder quality, and reducing equipment failures and maintenance costs.

[0119] For example, use ANSYS Maxwell to perform electrostatic field simulation on the three-dimensional assembly model of the conductive crushing cavity. The simulation results show that the electric field strength is slightly higher in the central region of the cavity and slightly lower in the edge region, with a standard deviation of 5%. To improve the electric field uniformity, it is decided to increase the coverage density of the conductive material in the edge region 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 crushing cavity includes:

[0121]

[0122] Among them:

[0123] represents the electric field uniformity index, with a value range of 0 to 1. The closer the value is to 1, the more uniform the electric field distribution.

[0124] represents a certain point inside the crushing cavity where the electric field strength vector is located.

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

[0126] represents the total volume of the crushing cavity.

[0127] represents a triple integral over the entire space of the crushing cavity.

[0128] 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. First, appropriate potential boundary conditions need to be applied inside the crushing cavity according to the distribution of the conductive material layer, and then the equations are discretely solved using numerical methods (such as the finite element method).

[0130] It can be obtained after that, through numerical integration calculation of the crushing cavity.

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

[0132] The magnitude 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 the conductive material is certain, increasing the coverage area of the conductive material layer can make the distribution of charges on the inner wall surface of the crushing cavity more uniform, thus improving the electric field uniformity; at the same time, optimizing the spatial distribution of the conductive material layer to form a uniform potential gradient inside the crushing cavity also helps to improve the electric field uniformity. 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 maximally increased.

[0133] This formula is used to quantitatively evaluate the degree of uniformity of the electric field distribution inside the crushing cavity. The denominator represents the total electric field strength that the electric field inside the crushing cavity should have under ideal conditions. The numerator represents the total deviation between the actual electric field distribution and the ideal uniform electric field. Dividing the two and then subtracting the obtained quotient from 1 gives 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, the influence of different conductive material coverage schemes on the electric field uniformity inside the crushing cavity can be quantitatively evaluated, so as to optimize the conductive material coverage scheme with the best electric field uniformity. A uniform electric field distribution is conducive to suppressing electrostatic accumulation during the crushing process, improving the crushing efficiency and powder dispersion.

[0135] Step S1224, according to the electric field uniformity index inside the crushing cavity, adjust the coverage position and coverage density of the conductive material layer. When the electric field uniformity index reaches the preset convergence condition, obtain the first conductive material coverage scheme.

[0136] Specifically, based on the electric field uniformity index calculated in step S1223, analyze the non-uniform regions of the electric field distribution and adjust the covering position and density of the conductive material layer accordingly. For example, increase the covering density of the conductive material in the regions with higher electric field strength to reduce the electric field strength in these regions and improve the overall uniformity. Through multiple adjustments and re-simulations, gradually optimize the covering scheme of the conductive material. After each adjustment, re-perform the electrostatic field simulation, calculate the new electric field uniformity index, and evaluate the optimization effect. Set preset convergence conditions, such as reducing the standard deviation of the electric field strength to within 3% or achieving an electric field uniformity of more than 90%. When the electric field uniformity index meets these conditions, terminate the optimization process and determine the final covering scheme of the conductive material. The finally determined covering scheme of the conductive material should ensure the uniform distribution of the electrostatic field in the cavity and prevent the agglomeration and wall sticking of the powder caused by electrostatic accumulation.

[0137] The optimization process of the conductive material covering scheme is an iterative feedback loop. By continuously adjusting the covering position and density and performing simulation verification, the design team can gradually approach the optimal covering scheme. This process not only improves the uniformity of the electrostatic field but also enhances the operating stability and comminution efficiency of the equipment. By iteratively adjusting and optimizing the conductive material covering scheme, the design team can scientifically control the electrostatic field distribution inside the cavity and ensure its uniformity. This not only reduces the electrostatic accumulation and agglomeration of the powder, improves the comminution efficiency and product quality, but also extends the service life of the equipment and reduces the maintenance cost. At the same time, the optimized covering scheme improves the safety of the equipment and avoids discharges and equipment damage caused by static electricity.

[0138] For example, after the initial adjustment of the conductive material covering scheme, the electrostatic field simulation shows that the standard deviation of the electric field strength decreases from 5% to 3%. Further increase the thickness of the conductive material in the regions with higher electric field strength from 0.5 mm to 0.7 mm and perform the electrostatic field simulation again. The new simulation results show that the electric field uniformity index increases to 95%, meeting the preset convergence conditions. Finally, the first conductive material covering scheme is determined to increase the covering density of the conductive material in the regions with higher electric field strength to ensure the uniform distribution of the electrostatic field throughout the comminution cavity.

[0139] By executing step S1210 and step S1220, the first air flow path and the first conductive material covering scheme of the comminution cavity can be scientifically and systematically determined. This process not only ensures the uniform distribution of the air flow in the cavity, improves the comminution 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. Finally, these optimization measures significantly improve the overall performance, stability and reliability of the equipment, laying a solid foundation for the efficient operation and high-quality product output of the low-temperature air flow nano-comminution equipment for medicine and food homologous plants.

[0140] Step S1230: Determine the first negative ion distribution scheme according to the negative ion distribution density in the initial structure parameters of the device.

[0141] Furthermore, step S1230 includes:

[0142] Step S1231: Calculate the number and distribution spacing of negative ion generators required inside the crushing cavity according to the negative ion distribution density; generate a distribution position lattice of negative ion generators on the inner wall surface of the three-dimensional assembly model of the conductive crushing cavity according to the number and distribution spacing of the negative ion generators.

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

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

[0145] Determine the calculation method of the distribution spacing: Calculate the uniform spacing between each unit according to the distribution density of the negative ion generators. The calculation formula for the distribution spacing is:

[0146] Spacing m;

[0147] In the high electric field region, appropriately reduce the distribution spacing to increase the negative ion density; in the low electric field region, maintain or appropriately increase the distribution spacing to save resources.

[0148] The distribution position lattice refers to the positions where negative ion generators are arranged on the inner wall surface according to the predetermined spacing and distribution pattern. Use CAD software (such as SolidWorks) to generate a distribution position lattice of negative ion generators on the inner wall surface of the three-dimensional assembly model of the conductive crushing cavity according to the calculated distribution spacing and number. Ensure that the lattice distribution is uniform and covers the entire inner wall surface, especially the high electric field intensity region.

[0149] According to the gas flow path and electrostatic field simulation results in the cavity, optimize the placement positions of the negative ion generators to ensure that negative ions can effectively cover the pulverization area and uniformly neutralize static charges. The placement dot matrix can be flexibly adjusted according to actual needs, such as using a grid dot matrix, a random dot matrix, or a concentrated placement strategy, to adapt to different design requirements and operating conditions. Through precise calculation and dot matrix generation, ensure the uniform distribution of negative ion generators in the cavity, improve the electrostatic neutralization effect, and reduce powder agglomeration and wall sticking phenomena. The uniform distribution of negative ions helps to maintain the good dispersion of the powder, ensure that the powder particles can be uniformly stressed during the pulverization process, and improve the pulverization efficiency and product quality. Reasonably place the negative ion generators according to the electric field intensity in different regions to avoid resource waste and optimize the equipment cost.

[0150] Step S1232, at each placement point of the placement position dot matrix, insert the three-dimensional solid model of the negative ion generator and perform constrained assembly with the three-dimensional assembly model of the conductive pulverization cavity to obtain the three-dimensional assembly model of the negative ion integrated pulverization cavity.

[0151] Specifically, use CAD software (such as SolidWorks) to create a detailed three-dimensional model of the negative ion generator, including its shape, size, and functional components. Set its geometric parameters according to the operating parameters of the negative ion generator (such as operating voltage, operating frequency) to ensure its effective operation in the pulverization cavity. According to the placement position dot matrix, precisely insert the three-dimensional solid model of the negative ion generator into the corresponding placement points of the three-dimensional assembly model of the conductive pulverization cavity. Apply geometric constraint conditions (such as surface contact, position fixation, etc.) to fix the negative ion generator at the placement point to ensure its stability and reliability during the operation of the equipment. The three-dimensional assembly model of the negative ion integrated pulverization cavity refers to the complete structure after all negative ion generators are successfully assembled into the three-dimensional assembly model of the conductive pulverization cavity. This model demonstrates the synergistic effect of the conductive material and the negative ion generator, which helps further functional verification and optimization. Check the placement position and assembly status of the negative ion generator through a visualization tool to ensure that it meets the design requirements and avoid 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, and it is necessary to select the appropriate type according to specific requirements and conduct detailed design. High-precision assembly ensures that the negative ion generator can work stably during the operation of the equipment, avoiding displacement or damage caused by vibration or air flow disturbance. By precisely placing and firmly assembling the negative ion generator, it is ensured that negative ions can effectively cover the inside of the pulverizing cavity, improving the electrostatic neutralization efficiency and reducing the electrostatic accumulation and agglomeration of powders. The firm assembly method enhances the operation stability of the negative ion generator, reduces the failure rate during equipment operation, and improves the reliability of the overall equipment. Uniform and efficient negative ion distribution ensures uniform stress and dispersion of powder particles during the pulverizing process, significantly improving the fineness and consistency of the final product.

[0153] Step S1233, obtain the optimal working parameter combination of the negative ion generator at different placement positions, and assign it to each three-dimensional entity model of the negative ion generator in the three-dimensional assembly model of the negative ion integrated pulverizing cavity to form the 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 settings that enable the negative ion generator to achieve the most efficient and uniform negative ion generation and distribution at a specific placement position. According to the working principle of the negative ion generator, analyze the influence of different parameter combinations on the negative ion generation efficiency and distribution. 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 at different placement positions and placement densities. These data usually come from experimental tests and technical materials provided by manufacturers. Through experiments or simulation tests, verify the effects of different working parameter combinations and select the parameter combination most suitable for a specific placement position.

[0155] Assign the obtained optimal working parameter combination (such as working voltage 5 kV, working frequency 50 Hz) to the three-dimensional entity model of the negative ion generator. In CAD software, update the working parameter attributes of the negative ion generator to ensure that the model reflects the actual working state. Integrate the working parameters of all negative ion generators to form a complete first negative ion placement plan.

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

[0157] For example, for the negative ion generator placed at the airflow inlet, since the electric field intensity in this area is relatively high, the design team determined the optimal combination of operating parameters as an operating voltage of 6 kV and an operating frequency of 60 Hz by referring to the performance parameter database and conducting experimental tests. These parameters were assigned to the 3D solid model of the negative ion generator to ensure its efficient generation of negative ions in the high electric field area and optimize the electrostatic field uniformity.

[0158] Step S1240: Combine the first airflow path, the first conductive material coverage scheme, and the first negative ion placement scheme to generate the first design scheme and store it in the equipment optimization database.

[0159] Specifically, the first airflow path includes parameters such as the shape, size, length, inlet and outlet positions of the airflow channel, ensuring that the airflow can cover the entire pulverizing chamber to achieve efficient pulverization and uniform dispersion. The first conductive material coverage scheme includes the type, coverage ratio, thickness of the conductive material, and its specific distribution method within different geometric partitions, ensuring 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 locations, operating voltage, and operating frequency, ensuring that negative ions are evenly distributed within the chamber and enhancing the electrostatic suppression effect. Integrate the first airflow path, the first conductive material coverage scheme, and the first negative ion placement scheme into a unified design scheme to ensure the coordination and compatibility among various parts. Use simulation software to verify the overall performance of the integrated design scheme, including airflow distribution, electrostatic field uniformity, and negative ion distribution effect, to ensure the scientificity and feasibility of the design scheme.

[0160] The equipment optimization database includes process requirement tables, material property tables, equipment structure parameter tables, etc. All design scheme data are stored orderly and are interrelated. Record in detail all the parameters and configurations of the first design scheme, including the specific values and layout diagrams of the air flow path, conductive material coverage scheme, and negative ion placement scheme. Ensure that the design team can easily retrieve and access the design schemes stored in the database to support subsequent optimization and adjustment.

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

[0162] By integrating the air flow path, conductive material coverage scheme, and negative ion placement scheme into the first design scheme, ensure the coordination and complementarity of each part of the design, and improve the rationality and effectiveness of the overall design. Each design scheme interacts with each other to jointly optimize the air flow distribution and electrostatic field uniformity, avoiding the problem of local optimization but overall disharmony that may be brought by a single design scheme. Systematically store the first design scheme in the equipment optimization database to ensure the integrity and traceability of 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, improving the efficiency and flexibility of the design process, and ensuring the continuous optimization and improvement of equipment design. The comprehensively optimized design scheme improves the uniformity of air flow distribution, the balance of electrostatic field, and the effectiveness of negative ion distribution, significantly enhancing the crushing efficiency, product quality, and operating stability of the equipment. Uniform air flow and electrostatic field distribution ensure uniform force on the powder during the crushing process, reducing agglomeration and wall sticking phenomena, and improving crushing efficiency and powder fineness. At the same time, the effective distribution of negative ions further inhibits electrostatic accumulation, enhancing the operating stability and reliability of the equipment. Structured data management and integrated processing of design schemes enable the design team to design and optimize more efficiently, shortening the design cycle, and reducing labor and material costs. Through a systematic design process and the support of an optimization database, repetitive labor and errors in the design process are reduced, design efficiency is improved, and design costs are reduced.

[0163] Step S1300, conduct air flow field analysis and optimization on the first design scheme to obtain a second air flow path;

[0164] The purpose of step S1300 is to identify and optimize the deficiencies in the air flow distribution by conducting a detailed flow field analysis of the preliminary designed air flow path, so as to obtain a more efficient and uniform second air flow path. This step ensures that the air flow can evenly cover the entire grinding cavity during the grinding process, improving the grinding efficiency and reducing energy loss.

[0165] Further, as Figure 6 shown, step S1300 includes:

[0166] Step S1310, build an air flow field analysis model according to the first design scheme;

[0167] Specifically, import the air flow path, conductive material coverage scheme, and negative ion placement scheme determined in the first design scheme into computer simulation analysis software, such as ANSYS Fluent or COMSOL Multiphysics. These software have powerful computational fluid dynamics (CFD) simulation capabilities and can accurately simulate the flow behavior of air in complex geometric structures. Set boundary conditions including the inlet and outlet and material property parameters; according to the air inlet and outlet positions determined by the design scheme, set the corresponding velocity, pressure, or mass flow boundary conditions. For example, if the inlet is set to a constant velocity, the specific velocity value needs to be input. Material property parameters include the density, viscosity, moisture content, etc. of the material, and these parameters will affect the flow characteristics of the air and the grinding effect. Adopt a higher grid density in the internal area of the grinding cavity and the air flow channel to improve the simulation accuracy; adopt a lower grid density in the area with less flow change to save computing resources. Select a suitable grid type, such as hexahedral grid or unstructured grid, to adapt to complex geometric shapes. Select a suitable turbulence model, such as the k-ε model or LES (Large Eddy Simulation) model, to accurately describe the turbulence characteristics in the air flow. If the presence of powder is considered, model the gas-solid two-phase flow and select a suitable multiphase flow model, such as the Euler-Euler model or the Discrete Phase Model (DPM).

[0168] Through detailed air flow field analysis, the flow trajectory and velocity distribution of the air flow in the grinding cavity can be accurately predicted, and potential flow dead zones or high-pressure areas can be found. Provide reliable data support for subsequent air flow path optimization to ensure that the optimized air flow path has better performance during actual operation.

[0169] For example, assume that the preliminary designed air flow path forms a local high-pressure area on one side of the grinding cavity, resulting in serious powder accumulation in this area. Through the air flow field analysis model, this non-uniform air flow distribution can be clearly seen, thus guiding the designers to adjust the layout of the air flow channel to achieve a more uniform air flow distribution.

[0170] Step S1320: Obtain the first airflow field parameters according to the airflow field analysis model. The first airflow field parameters include the velocity field, pressure field, and turbulence intensity, and store them in the equipment optimization database.

[0171] Specifically, start the airflow field analysis model and perform numerical simulation calculations to obtain detailed flow field information of the airflow in the crushing cavity. The velocity field describes the flow velocity distribution of the airflow at each position, reflecting the kinetic energy and flow direction of the airflow. The pressure field reflects the pressure distribution of the airflow at different positions, helping to identify possible pressure unevenness problems. The turbulence intensity quantifies the degree of turbulence in the airflow, affecting the crushing effect of the powder and energy loss. Store the above airflow field parameter data in the equipment optimization database, specifically in the airflow field parameter table, for subsequent analysis and optimization use.

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

[0173] Step S1330: Compare the first airflow field parameters with the preset airflow field parameters to obtain the first airflow field deviation. The first airflow field deviation includes velocity deviation, pressure deviation, and turbulence intensity deviation.

[0174] Specifically, at the initial stage of design, set ideal airflow field parameters according to empirical data or process requirements, including the target velocity field, target pressure field, and target turbulence intensity. Compare the difference between the actually 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. Consider the velocity deviation, pressure deviation, and turbulence intensity deviation comprehensively to form a first airflow field deviation report, which is recorded in the airflow field deviation table in the equipment optimization database.

[0175] Through the quantified deviation values, objectively evaluate the airflow field performance of the current design scheme, and identify specific aspects that need improvement. The 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 velocity should be maintained at 5 meters per second, and the actual simulation results show that it is only 3 meters per second in a certain area, the velocity deviation is -2 m / s. This specific deviation value indicates that the airflow supply needs to be increased or the airflow channel layout needs to be adjusted in this area to increase the airflow velocity.

[0176] Step S1340: Optimize the first air flow path according to the first air flow field deviation to obtain the second air flow path.

[0177] Specifically, step S1340 includes the following:

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

[0179] Application of optimization algorithms:

[0180] Genetic Algorithm (GA): Simulate natural selection and genetic mechanisms, and find the optimal air flow path design through iterative evolution.

[0181] Particle Swarm Optimization (PSO): Simulate the foraging behavior of bird flocks and optimize the air flow path parameters through group collaboration.

[0182] Other computer-aided optimization algorithms: Such as Simulated Annealing, Gradient Descent, etc., and select the most suitable algorithm according to specific requirements.

[0183] Parameter adjustment:

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

[0185] Branch and confluence point design: Redesign the number of branches and confluence methods of the air flow channel to ensure that the air flow can be evenly distributed throughout the crushing cavity.

[0186] Bending angle and radius: Optimize the bending angle and bending 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 air flow path parameters to re-perform air flow field simulation and verify whether the optimization effect meets the expected goals.

[0188] Generate the second air flow path: If the optimization effect is satisfactory, determine the optimized air flow path as the second air flow path and record it in the "second air flow path" field of the equipment optimization database.

[0189] The optimized airflow path can cover the entire pulverization cavity more evenly, avoiding overly strong or weak local airflow, and improving the pulverization efficiency. By reducing the energy loss in the airflow path, higher energy utilization efficiency is achieved, and the operating cost of the equipment is reduced. The optimized airflow path reduces eddy currents and abnormal flow phenomena, lowering the risk of equipment wear and failure and extending the service life of the equipment. For example, during the initial optimization process, it was found that there was still a low-speed area on the right side of the pulverization cavity. By applying the particle swarm optimization algorithm, the branch angle and bending radius of the airflow channel were adjusted, enabling the airflow to be more evenly distributed to the right side area. After re-simulation, it was found that the airflow velocity in this area increased significantly, approaching the preset target value, indicating a significant optimization effect.

[0190] Step S1400: Conduct electrostatic aggregation prediction and scheme optimization on the first design scheme to obtain the second conductive material coverage scheme and the second negative ion placement scheme.

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

[0192] Furthermore, step S1400 includes:

[0193] Step S1410: Construct an electrostatic aggregation prediction model based on the electrostatic properties of the material in the material property table.

[0194] Specifically, step S1410 includes the following:

[0195] Collect material electrostatic property data:

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

[0197] Dielectric constant of the material: Reflects the polarization ability of the material in an electric field and affects the distribution of the electrostatic field.

[0198] Surface resistivity of the material: 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 non-linear relationships.

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

[0202] Model construction:

[0203] Input features: Include the electrostatic properties of the material, the coverage ratio of the conductive material, the density of negative ion distribution, etc.

[0204] Output target: The powder loss rate, that is, the proportion of powder loss caused by electrostatic aggregation during the crushing process.

[0205] Model training and optimization:

[0206] Data preprocessing: Include 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 a non-linear relationship between the powder loss rate and the input features.

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

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

[0210] Step S1420, according to the first conductive material coverage scheme, the first negative ion distribution scheme, and the electrostatic aggregation prediction model, obtain the first predicted powder loss rate;

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

[0212] Input parameters:

[0213] The first conductive material coverage scheme: Includes the specific coverage ratio, coverage area, and material type of the conductive material on the inner wall of the crushing cavity.

[0214] The first negative ion distribution scheme: Includes the distribution density, position distribution, and working parameters (such as working voltage, frequency) of the negative ion generator.

[0215] Model prediction:

[0216] Input the above input parameters into the electrostatic aggregation prediction model, and the model predicts the powder loss rate under the current design scheme according to the trained non-linear relationship.

[0217] Result storage:

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

[0219] Step S1420 provides a quantitative metric (powder loss rate) to help evaluate the electrostatic control effect of the current design solution. The predicted powder loss rate provides a clear direction for improvement in the next step, ensuring that the optimization goal is clear and specific. For example, assume that under the first design solution, 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 solutions.

[0220] In step S1430, compare the first predicted powder loss rate with a preset target loss rate to obtain the first loss rate deviation;

[0221] Specifically, according to process requirements or industry standards, set an acceptable target value for the powder loss rate, such as 2%. The first loss rate deviation = the first predicted powder 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%. Record the calculated first loss rate deviation in the loss rate deviation table in the equipment optimization database for use in the optimization steps.

[0222] Step S1430 clearly understands the gap between the current design solution and the target through the deviation value, and clarifies which aspects need to be improved. It provides a specific quantitative metric for improvement, ensuring that the optimization process has a clear direction and measurement standard. For example, in the above example, a deviation of +3% indicates that the powder loss rate of the current design solution 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] In step S1440, optimize the first conductive material coverage solution and the first negative ion placement solution based on the first loss rate deviation to obtain the second conductive material coverage solution and the second negative ion placement solution.

[0224] Specifically, step S1440 includes the following:

[0225] Optimization goal setting:

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

[0227] Take into account equipment cost and manufacturability: During the optimization process, not only the electrostatic control effect needs to be considered, but also the costs of the conductive material and the negative ion generator and the feasibility of their placement.

[0228] Application of optimization algorithm:

[0229] Genetic Algorithm (GA): By simulating natural selection and genetic variation, iteratively optimize the coverage ratio of the conductive material and the density of negative ion placement.

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

[0231] Multi-objective optimization: Simultaneously optimize multiple objectives, such as minimizing the deviation of the powder loss rate and equipment cost, using methods such as weight allocation or Pareto front.

[0232] Parameter adjustment:

[0233] Coverage ratio of conductive material: According to the deviation situation, 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] Density of negative ion placement: Adjust the placement density and position of the negative ion generator, such as increasing the number of negative ion generators or optimizing their distribution positions, to enhance the electrostatic neutralization effect.

[0235] Re-prediction and verification:

[0236] Predict the loss rate: Input the optimized conductive material coverage plan and negative ion placement plan into the electrostatic aggregation prediction model to predict the new powder loss rate.

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

[0238] Generate the second plan:

[0239] Determine the optimized conductive material coverage plan and negative ion placement plan as the second plan, and record them in the "Second Conductive Material Coverage Plan" and "Second Negative Ion Placement Plan" 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, it optimizes the use of conductive materials and negative ion generators to reduce the equipment operation and maintenance costs. The optimized scheme meets the electrostatic control requirements while ensuring its feasibility and stability in actual manufacturing and operation. For example, during the initial optimization process, after increasing the conductive material coverage ratio to 60%, the predicted powder loss rate drops to 3%. At the same time, by increasing the density of negative ion generators, the negative ion distribution becomes more uniform, further reducing the predicted loss rate to 2.1%. This optimization process not only achieves a loss rate close to the target but also completes the optimization within a reasonable cost range.

[0241] In step S1500, combine the second air flow path, the second conductive material coverage scheme and the second negative ion placement scheme to generate the second design scheme; iteratively optimize the second design scheme to obtain the final internal structure design scheme of the equipment.

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

[0243] Furthermore, step S1500 includes:

[0244] In step S1510, combine the second air flow path, the second conductive material coverage scheme and the second negative ion placement scheme to generate the second design scheme;

[0245] Adopt the second air flow path, that is, the optimized air flow path, to ensure a more uniform and efficient air flow distribution. Apply the second conductive material coverage scheme, where 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. Implement the second negative ion placement scheme, where the placement density and position of the negative ion generators are optimized to enhance the electrostatic neutralization effect. By integrating the optimization results of the air flow path, conductive material coverage and negative ion placement scheme, ensure that the optimization measures in all aspects are coordinated with each other to avoid a decline in overall performance caused by local optimization. Ensure the consistency of the internal structure design scheme of the equipment in different optimization links to improve the reliability and feasibility of the design.

[0246] Step S1520: Repeatedly execute Step S1300 and Step S1400 to conduct airflow field analysis, electrostatic accumulation prediction, and scheme optimization for the second design scheme until both the Nth airflow field deviation and the Nth loss rate deviation obtained at the Nth iteration meet the preset convergence conditions.

[0247] Specifically, set preset convergence conditions, such as both the airflow field deviation and the powder loss rate deviation being lower than a certain threshold (for example, the airflow field deviation is lower than 5% and the powder loss rate deviation 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 to improve the accuracy and reliability of the design scheme. Dynamically adjust the design scheme according to the actual deviation situation to adapt to different materials and process requirements, and improve the versatility and adaptability of the equipment.

[0248] Exemplarily, in the first iteration, the airflow field deviation is 6% and the powder loss rate deviation is 1.5%. By optimizing the airflow path and the coverage ratio of the conductive material, after the second iteration, the airflow field deviation is reduced to 4.8% and the powder loss rate deviation is reduced to 0.9%, both meeting the preset convergence conditions (the airflow field deviation is lower than 5% and the powder loss rate deviation is lower than 1%), so the iteration is stopped and the final design scheme is determined.

[0249] Step S1530: Determine the Nth design scheme obtained at the Nth iteration as the final internal structure design scheme of the equipment and store it in the equipment optimization database.

[0250] Specifically, confirm that at the Nth iteration, both the airflow field deviation and the powder loss rate deviation have met the preset convergence conditions. Conduct a comprehensive verification of the final design scheme to ensure that it can achieve the expected effect during actual operation. Store the detailed parameters of the design scheme obtained at the Nth iteration, including the airflow path, the conductive material coverage scheme, and the negative ion placement scheme, in the "Final Design Scheme" field of the equipment optimization database. Generate a detailed design document for the final design scheme, including 3D assembly model drawings, airflow field distribution diagrams, electric field uniformity index reports, etc., as a record and display of the design results. Convert the final design scheme into a manufacturing and assembly guidance document for the actual equipment to ensure the smooth implementation of the design scheme.

[0251] Through multiple iterations, ensure the accuracy and efficiency of the final design scheme in terms of airflow distribution and electrostatic control. Comprehensively record the iteration process and the final design scheme for subsequent maintenance, optimization, and technical communication. Through detailed design documents and parameter records, ensure the feasibility and reliability of the final design scheme during actual manufacturing and operation.

[0252] Step S2000: Based on the final internal structure design scheme of the device, integrate an electrostatic monitoring and dynamic adjustment mechanism to perform real-time adaptive control on the device and establish a device protection mechanism.

[0253] Furthermore, step S2000 includes:

[0254] Step S2100: Based on the final internal structure design scheme of the device, add an electrostatic monitoring unit and an electrostatic adjustment unit; 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.

[0255] Specifically, arrange electrostatic sensors at key positions inside the crushing cavity (such as near the air inlet, outlet, and negative ion generator, etc.) to monitor the electrostatic voltage and charge amount during the operation of the device in real time. The data acquisition module is responsible for receiving the data collected by the electrostatic sensors; use high-precision data acquisition equipment to ensure the accuracy and real-time nature of the collected data. The data processing module preprocesses the collected raw data, including steps such as filtering, denoising, and normalization, to improve the quality and usability of the data. Use digital signal processing algorithms, such as low-pass filtering, Gaussian filtering, etc., to remove interference signals and noise. The data analysis module deeply analyzes the preprocessed data, extracts key features, and identifies abnormal states. Use feature extraction algorithms and pattern recognition techniques, such as principal component analysis (PCA), support vector machine (SVM), etc., 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. Use an embedded control system with high response speed and reliability, which 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 the working voltage and working frequency, to achieve dynamic adjustment of the electrostatic state. Use high-precision voltage and frequency adjustment devices to ensure that the working parameters of the negative ion generator can be accurately adjusted according to the instructions.

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

[0258] By adding an electrostatic monitoring unit, it is possible to monitor the electrostatic state during the operation of the equipment in real time, detect abnormal situations in a timely manner, and ensure the normal operation of the equipment. The electrostatic regulation 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 accumulation, improve the pulverization efficiency and powder quality. Through real-time monitoring and dynamic regulation, it prevents excessive or insufficient electrostatic accumulation, reduces the occurrence of equipment failures and safety accidents, and extends the service life of the equipment. It realizes the intelligent and automatic control of the equipment, reduces manual intervention, and improves production efficiency and product consistency. For example, during actual operation, when the electrostatic sensor detects a sudden increase in the electrostatic voltage at a certain key position, the data acquisition module records this change in real time. The data processing module removes noise through filtering, and the data analysis module identifies that this is a state of excessive electrostatic accumulation. After receiving this abnormal state, the controller immediately generates a regulation instruction, instructing the actuator to increase the working voltage and frequency of the negative ion generator to increase the generation amount of negative ions and quickly neutralize excessive electrostatic charges. Through this dynamic regulation process, the electrostatic voltage quickly returns to the normal range, avoiding the risks of powder agglomeration and equipment discharge, and ensuring the stability and efficiency of the pulverization process.

[0259] Step S2200, perform real-time adaptive control on the equipment;

[0260] Furthermore, step S2200 includes:

[0261] Step S2210, during the operation of the equipment, the electrostatic monitoring unit receives the data collected by the electrostatic sensor in real time, processes and analyzes it, and identifies the abnormal state of electrostatic accumulation;

[0262] Furthermore, step S2210 includes:

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

[0264] Specifically, electrostatic sensors with high sensitivity and wide frequency response range are selected, such as electrostatic voltage sensors and electrostatic charge sensors, to ensure accurate capture of electrostatic changes in the crushing chamber. At key positions in the crushing chamber, such as the air inlet, air outlet, near the negative ion generator, and the middle and bottom of the crushing chamber, electrostatic sensors are evenly arranged to comprehensively monitor the electrostatic states in different regions. The sensors need to be firmly installed on the inner wall or structural support parts of the crushing chamber to ensure stable operation of the sensors and avoid inaccurate data acquisition caused by vibration or impact. An appropriate sampling frequency is set, such as 100 samples per second, to ensure real-time capture of rapidly changing electrostatic data. According to the electrostatic working range of the device, the measurement range of the sensors is set to avoid data overflow or distortion. By arranging multiple electrostatic sensors at key positions, comprehensive monitoring of the electrostatic states in different regions of the crushing chamber is achieved, avoiding potential electrostatic anomalies being overlooked. Selecting high-sensitivity sensors and reasonable sampling parameters ensures that the collected data has high precision and high real-time performance, providing a reliable basis for subsequent data processing and analysis.

[0265] Step S2212: The data acquisition module of the electrostatic monitoring unit receives electrostatic 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 sensors in real time through high-speed data interfaces, such as USB, Ethernet, or wireless transmission protocols. The received electrostatic voltage and charge data are seamlessly transmitted to the data processing module to ensure the integrity and continuity of the data. Time stamps are added to each set of collected data to ensure that 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 caused by instantaneous network latency or data processing lag. Through high-speed data interfaces and efficient data transmission protocols, it is ensured that the data collected by the electrostatic sensors can be quickly and accurately transmitted to the data processing module, reducing data latency and loss. The 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 preprocesses 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, improving 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 a preset standard range (such as 0 to 1) to eliminate the dimension difference and improve the comparability of the data. The data curve is smoothed by methods such as moving average to reduce data fluctuations and enhance the stability of the data. 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 more accurate feature extraction and pattern recognition in subsequent analysis processes, reducing the risks of misjudgment and missed judgment. For example, during the crushing process, due to external electromagnetic interference in a certain data collection, a certain sensor recorded an instantaneous spike value. The data processing module identified and removed this spike value through an outlier detection algorithm and used a low-pass filter to remove high-frequency noise, finally obtaining smooth and accurate static voltage and charge quantity data to ensure the accuracy of subsequent analysis.

[0269] Step S2214, the data processing module extracts features from the preprocessed static voltage and charge quantity data to obtain a static electricity feature vector, and the static electricity feature vector includes the mean value of the static voltage, the peak value of the static voltage, the fluctuation frequency of the static voltage, the mean value of the static charge quantity, and the peak value of the static charge quantity;

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

[0271] Feature extraction method:

[0272] Statistical feature extraction:

[0273] Mean value of static voltage: Calculate the average value of the static voltage within a certain time window, reflecting the overall static electricity level.

[0274] Peak value of static voltage: Identify the highest point of the static voltage, reflecting the extreme static electricity state.

[0275] Mean value of static charge quantity: Calculate the average value of the static charge quantity within a certain time window, reflecting the overall charge distribution.

[0276] Peak value of static charge quantity: Identify the highest point of the static charge quantity, reflecting the extreme charge state.

[0277] Frequency domain feature extraction:

[0278] Fluctuation frequency of static voltage: Through frequency domain analysis methods such as Fourier transform, identify the main frequency components of the static voltage change, reflecting the stability and volatility of the static electricity state.

[0279] Feature vector construction:

[0280] Combine the extracted eigenvalue (average electrostatic voltage, peak electrostatic voltage, electrostatic voltage fluctuation frequency, average electrostatic charge quantity, peak electrostatic charge quantity) into a multi-dimensional feature vector as a comprehensive description of the electrostatic state.

[0281] Feature vector standardization:

[0282] Perform standardization processing on the feature vector to ensure that each feature is under the same dimension, improving the accuracy and stability of the subsequent classification model.

[0283] By extracting key features, convert complex time-series electrostatic data into a concise feature vector, simplifying subsequent pattern recognition and classification analysis. Selecting representative features helps improve the accuracy and robustness of the electrostatic aggregation abnormal state recognition model. Through feature extraction, dimensionality reduction of data is achieved, reducing computational complexity and improving analysis efficiency.

[0284] Exemplarily, during a certain pulverization process, after feature extraction of the preprocessed electrostatic voltage data, the following feature vector is obtained:

[0285] Average electrostatic voltage: 4.8 kV;

[0286] Peak electrostatic voltage: 6.2 kV;

[0287] Electrostatic voltage fluctuation frequency: 50 Hz;

[0288] Average electrostatic charge quantity: 3.5 μC;

[0289] Peak electrostatic charge quantity: 5.0 μC; This feature vector will be input into the electrostatic aggregation abnormal state recognition model to determine whether the current device operating state is normal.

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

[0291] 1) Electrostatic over-aggregation state: The average electrostatic voltage and the average electrostatic charge quantity exceed the preset safety threshold and the duration exceeds the preset time length, indicating that the electrostatic aggregation degree inside the pulverization cavity is too high, which may cause problems such as powder agglomeration, wall sticking, and discharge. It is necessary to adjust the working parameters of the negative ion generator in time to increase the negative ion generation amount; the working parameters of the negative ion generator include the working voltage and the working frequency.

[0292] 2) Electrostatic under-aggregation state: The average electrostatic voltage and the average electrostatic charge quantity are lower than the preset lower threshold value, and the duration exceeds the preset time length, indicating that the degree of electrostatic aggregation inside the pulverizing cavity is too low, which may affect the powder dispersibility and pulverizing efficiency, and it is necessary to appropriately reduce the amount of negative ions generated;

[0293] 3) Abnormal electrostatic fluctuation state: The fluctuation frequency of the electrostatic voltage exceeds the preset frequency threshold, indicating that the electrostatic aggregation state inside the pulverizing cavity is unstable, and there may be problems such as partial discharge or charge mutation. It is necessary to optimize the negative ion distribution scheme and adjust the layout and working mode of the negative ion generator;

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

[0295] The recognition model for abnormal electrostatic aggregation states is trained and optimized using the Support Vector Machine (SVM) algorithm. By performing offline analysis on a large amount of historical operation data, the characteristic patterns of abnormal electrostatic aggregation states are extracted, and a multi-classifier model is established; during the model training process, parameters such as kernel functions and penalty factors are introduced to improve the non-linear 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 hyperparameters of the model to avoid overfitting and underfitting problems.

[0296] The training data set of the recognition model for abnormal electrostatic aggregation states includes the equipment historical operation data and the test bench simulation data; among them, the equipment historical operation data comes from pulverizing 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 numerically simulating and physically simulating the electrostatic field inside the pulverizing cavity, and simulates the electrostatic characteristic data under various abnormal electrostatic aggregation states. By comprehensively using the actual operation data and simulation data, the applicability and robustness of the recognition model for abnormal electrostatic aggregation states are improved.

[0297] In order to meet the requirements for recognizing electrostatic aggregation states under different pulverizing processes and material characteristics, the recognition model for abnormal electrostatic aggregation states 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 abnormal electrostatic aggregation state pattern is recognized, the recognition model for abnormal electrostatic aggregation states can automatically perform incremental learning, add the new abnormal state pattern to the recognition scope, and continuously expand and improve the abnormal state knowledge base. Through the adaptive learning mechanism, the real-time performance and accuracy of the recognition model for abnormal electrostatic aggregation states are improved, and the dynamic monitoring and diagnosis of the equipment operation state 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 recognition result is the electrostatic under-accumulation state, the controller determines whether the working voltage and working frequency of the current negative ion generator reach the preset minimum value. If not, an adjustment instruction is generated to decrease the working voltage and working frequency by a preset step size until the preset minimum value is reached or the electrostatic accumulation state returns to normal; if the minimum value has been reached, the current working parameters are maintained unchanged, and a prompt signal is sent to the equipment control system, indicating that the electrostatic accumulation degree is relatively low under the current process parameters, and the comminution process or material formula needs to be adjusted.

[0306] When the recognition result is the electrostatic fluctuation abnormal state, the controller analyzes the relationship between the electrostatic voltage fluctuation frequency and the working 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 working frequency of the negative ion generator to a safe frequency band far from the electrostatic voltage fluctuation frequency, and increase the working voltage to suppress the electrostatic fluctuation; if not, an adjustment instruction is generated to simultaneously adjust the working voltage and working frequency of the negative ion generator to the intermediate value within the preset safe range, and continuously monitor the change trend of the electrostatic accumulation state, and make fine adjustments according to the change trend.

[0307] When the recognition result is the electrostatic polarization abnormal state, the controller immediately generates a shutdown instruction and sends a severe warning signal to the equipment control system, indicating that emergency maintenance and fault diagnosis are required; at the same time, the controller packs the current equipment structure parameters, process parameters, and abnormal state data and sends them to the equipment optimization database to trigger the internal structure optimization process of the equipment. During the shutdown maintenance and optimization period, the controller continuously monitors the electrostatic voltage and charge amount data inside the comminution cavity to determine whether the electrostatic polarization phenomenon has been eliminated, and decides whether to allow the equipment to restart according to the judgment result.

[0308] By generating accurate adjustment instructions according to the real-time recognition result by the controller, the refined control of the operation of the negative ion generator is realized, ensuring that the electrostatic state is maintained within the ideal range. The automated control strategy enables the equipment to quickly respond to changes in the electrostatic state, reduces the lag of manual intervention, and improves the dynamic adaptability of the equipment. The optimized adjustment of the working parameters can improve the neutralization effect of negative ions, enhance the powder dispersibility and comminution efficiency during the comminution process, and improve the product quality. For example, assume that during operation, the electrostatic monitoring unit recognizes that the current is the electrostatic over-accumulation state. After receiving this result, the controller decides to increase the working voltage of the negative ion generator from 5 kV to 7 kV and the working frequency from 50 Hz to 70 Hz according to the preset control strategy. The controller generates the corresponding adjustment instruction and transmits it to the actuator, and the actuator then adjusts the parameters of the negative ion generator, increasing the generation amount of negative ions, effectively neutralizing excessive electrostatic charges, and restoring the normal operation state of the equipment.

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

[0310] The actuator receives the regulation instruction from the controller through a wired or wireless communication interface. The actuator analyzes the regulation instruction to determine the specific values of the working voltage and working frequency that need to be adjusted. By adjusting the power supply module or control circuit, the working voltage of the negative ion generator is accurately adjusted to the value specified in the instruction. Ensure that the adjusted working voltage can be stably output to avoid new electrostatic problems caused by voltage fluctuations. By adjusting the frequency generator or control circuit, the working frequency of the negative ion generator is accurately adjusted to the value specified in the instruction. Ensure that the adjusted working frequency does not resonate or intermodulate with the voltage fluctuation frequency inside the device to avoid triggering a new electrostatic abnormal state. 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 regulation instructions or fault handling processes. The actuator can accurately adjust the working parameters of the negative ion generator according to the regulation instruction to ensure the accuracy and effectiveness of the adjustment. Through precise parameter adjustment and stable output, the overall operation stability of the device is improved, and the occurrence frequency of electrostatic abnormal states is reduced. An automated parameter adjustment process is realized, reducing manual intervention and improving the automation level and production efficiency of the device operation. For example, after receiving the regulation instruction, the actuator adjusts the working voltage of the negative ion generator from 5 kV to 7 kV and the working frequency from 50 Hz to 70 Hz at the same time. 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 negative ion generation amount of the negative ion generator increases, successfully neutralizing excessive static charges, and the static state in the crushing cavity returns to normal, ensuring the stability and efficiency of the crushing process.

[0311] Step S2230: The electrostatic regulation unit feeds back the adjusted working parameters of the negative ion generator 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. Through the feedback mechanism, the working parameters of the negative ion generator are continuously evaluated and optimized, so as to achieve precise control of the static state in the crushing cavity. This process ensures that the device can continuously maintain the best electrostatic suppression effect in a dynamic operating environment, improve the crushing efficiency and product quality, extend the service life of the device at the same time, and reduce the maintenance cost.

[0313] After the electrostatic regulation unit completes the regulation 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 (regulation instructions) to ensure that the system output is stabilized within the expected range. Through closed-loop control, the system can respond in real time to changes during equipment operation, automatically adjust the working parameters of the negative ion generator, and maintain the best 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 electrostatic state monitored in real time to adapt to different operating conditions and material characteristics. Continuous feedback and adjustment ensure that the electrostatic state during equipment operation remains stable, reducing problems such as powder agglomeration and wall sticking caused by over-accumulation or under-accumulation of static electricity. Automated closed-loop control reduces dependence on operators, reduces the risk of human operation errors, and improves the reliability and consistency of equipment operation.

[0314] Exemplarily, during the actual operation process, the electrostatic regulation unit adjusts the working frequency of the negative ion generator from 70 Hz to 90 Hz to cope with the detected abnormal state of electrostatic fluctuation. After the adjustment is completed, the new working 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 fluctuation has significantly weakened and the electrostatic accumulation state has returned to normal. Based on this evaluation result, the system records the adjustment effect this time and refers to this adjustment strategy for optimization when encountering similar situations in the future to ensure the continuous improvement of the electrostatic suppression effect.

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

[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 electrostatic accumulation, and ensure the safety of the equipment and operators. At the same time, this mechanism also continuously improves the reliability and adaptability of the equipment through dynamic feedback and optimization.

[0317] Furthermore, step S2300 includes:

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

[0319] Determine the safety thresholds for each parameter based on the design specifications of the negative ion generator and the data provided by the manufacturer. Combine the specific requirements of the crushing process to adjust the threshold range to ensure the safe operation of the equipment under different process conditions. Optimize the threshold settings based on laboratory tests and actual operation data to ensure their scientific nature and practicality. Set the maximum safety value of the working voltage of the negative ion generator (such as 8 kV), exceeding which may cause excessive static electricity neutralization, leading to discharge or equipment damage. Set the minimum safety value of the working voltage (such as 3 kV), below which may result in insufficient static electricity accumulation, affecting the crushing efficiency and product quality. Set the maximum safety value of the working frequency of the negative ion generator (such as 100 Hz), exceeding which may cause internal electromagnetic interference or resonance in the equipment. Set the minimum safety value of the working frequency (such as 20 Hz), below which may result in insufficient static electricity neutralization effect, affecting 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, trigger the corresponding alarm mechanism to prompt the operator to take necessary emergency measures.

[0320] By setting reasonable safety thresholds, prevent safety accidents caused by abnormal operation of the equipment due to parameters, and protect the safety of the equipment and operators. Ensure that the negative ion generator operates within a safe range, avoid equipment failures and unstable operation caused by abnormal parameters, and improve the overall reliability of the equipment. Through scientific threshold setting, prevent potential safety risks in advance, reduce equipment downtime and maintenance costs, and improve production efficiency.

[0321] Step S2320, during the static electricity regulation process, if the working parameters of the negative ion generator exceed the safety threshold range, the static electricity regulation unit promptly sends an alarm signal to the equipment control system to trigger the equipment protection mechanism;

[0322] Specifically, the static electricity 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. Compare the real-time monitored working parameters with the preset safety thresholds to determine whether there is a situation beyond the range. Once it is detected that the working voltage or working frequency exceeds the safety threshold, the system immediately identifies it as an abnormal state. The static electricity regulation unit generates the corresponding alarm signal, including information such as the alarm type (such as voltage overlimit, frequency overlimit) and alarm level (such as severe, warning). Through a reliable communication interface (such as industrial Ethernet, fiber optic communication, etc.), transmit the alarm signal to the equipment control system. After receiving the alarm signal, the equipment control system confirms and records the alarm event. According to the alarm level, automatically execute the preset protection measures, such as power off, shutdown, etc., to prevent the equipment from being damaged or causing safety accidents due to abnormal parameter operation. In some cases, the system may require the operator's confirmation or manual intervention to ensure the accuracy and necessity of the protection measures.

[0323] Once the operating parameters are detected to exceed the safe range, the system can respond immediately, quickly trigger protection measures, and reduce the likelihood of accidents. Through timely alarms and protection mechanisms, serious accidents such as equipment damage, personnel injury, and production interruption caused by abnormal static electricity parameters can be prevented. Each alarm event is recorded for subsequent fault analysis and equipment optimization, 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 rises to 120 Hz, exceeding the preset safety upper limit of 100 Hz. The static electricity regulation unit immediately generates an alarm signal for frequency overlimit and transmits it to the equipment control system through the industrial Ethernet. After receiving the alarm signal, the equipment control system automatically executes the shutdown protection measure, 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 the equipment control system receives the alarm signal, it immediately executes the preset protection measures.

[0325] Specifically, after the equipment control system receives the alarm signal from the static electricity regulation unit, it immediately conducts response processing. The alarm information is displayed on the operation panel or monitoring interface to alert the operator. 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, starts the emergency braking system to quickly stop the mechanical movement of the equipment and prevent further damage. Operator intervention, according to the alarm information, the operator can manually execute further protection measures, such as closing a specific circuit, adjusting equipment parameters, etc. Maintenance request sends a fault report to the maintenance team to initiate the equipment maintenance and repair process.

[0326] The equipment control system records the detailed information of this alarm event, including the alarm type, occurrence time, trigger 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 for remote fault diagnosis and technical support. Monitor the execution status of the protection measures to ensure that measures such as power-off and shutdown have been successfully implemented. Feedback the execution result of the protection measures to the operator and relevant systems to confirm that the equipment has entered a safe state.

[0327] By quickly implementing preset protection measures, serious accidents caused by abnormal operation of equipment parameters are 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. The reliability and response ability of the equipment control system are enhanced, and the overall safety management level of the equipment is improved.

[0328] Exemplarily, after the electrostatic regulation unit triggers a frequency overlimit alarm, the equipment control system receives the alarm signal and immediately executes power-off and shutdown protection measures, quickly cutting off the power supply of the negative ion generator and stopping the equipment operation. The alarm message "Frequency overlimit, equipment has stopped" is displayed on the operation panel, and at the same time, the alarm event is recorded and sent to the remote monitoring center. After receiving the notice, the maintenance personnel go to the site to check the equipment, find that there is a fault in the frequency adjustment module, replace the components in time, ensure that the equipment resumes normal operation, and avoid further damage to the equipment and potential safety risks.

[0329] Embodiment 2

[0330] Based on Embodiment 1, this embodiment provides a platform for the internal structure design of a low-temperature airflow nano-crushing device for medicine and food homologous plants, as Figure 7 shown, including:

[0331] The first design scheme generation module: used to obtain the characteristic parameters of the target crushing object, determine the initial structure parameters of the equipment based on the characteristic parameters, and take the initial structure parameters of the equipment as the first structure parameters; generate the first design scheme according to the first structure parameters;

[0332] The scheme optimization module: used to perform airflow field analysis and optimization on the first design scheme to obtain the second airflow path; perform electrostatic aggregation prediction and scheme optimization on the first design scheme to obtain the second conductive material coverage scheme and the second negative ion placement scheme; combine the second airflow path, the second conductive material coverage scheme and the second negative ion placement scheme to generate the second design scheme; iteratively optimize the second design scheme to obtain the final internal structure design scheme of the equipment;

[0333] The adaptive control module: used to integrate the electrostatic monitoring and dynamic adjustment mechanism on the basis of the final internal structure design scheme of the equipment, perform real-time adaptive control on the equipment, and establish an equipment protection mechanism.

[0334] In the first design scheme generation module, the obtaining the characteristic parameters of the target crushing object, determining the initial structure parameters of the equipment based on the characteristic parameters, and taking the initial structure parameters of the equipment as the first structure parameters includes:

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

[0336] Step S1120: Obtain the material properties of the target crushing object. The material properties include material density, material moisture content, and material electrostatic properties, and store the material properties in the material attribute table in the equipment optimization database.

[0337] Step S1130: Based on the process requirement table and the material attribute table, and in combination with the preset range of equipment structure parameters, determine the initial equipment structure parameters. The initial structure parameters include the spatial layout parameters of the crushing cavity, the spatial layout parameters of the air flow channel, the coverage ratio of conductive materials, and the negative ion distribution density. The spatial layout parameters of the crushing cavity include the size and geometric shape of the crushing cavity. The spatial layout parameters of the air flow channel include the cross-sectional shape, cross-sectional size, length, spatial position of the air flow channel, connection method of the air flow channel, and bending angle of the air flow channel.

[0338] Step S1140: Store the initial equipment structure parameters in the equipment optimization database as the first structure parameters.

[0339] In the first design scheme generation module, generating the first design scheme according to the first structure parameters includes:

[0340] Step S1210: Determine the first air flow path of the crushing cavity according to the spatial layout parameters of the crushing cavity and the spatial layout parameters of the air flow channel in the initial equipment structure parameters.

[0341] Step S1220: Determine the first conductive material coverage scheme according to the coverage ratio of conductive materials in the initial equipment structure parameters.

[0342] Step S1230: Determine the first negative ion distribution scheme according to the negative ion distribution density in the initial equipment structure parameters.

[0343] Step S1240: Combine the first air flow path, the first conductive material coverage scheme, and the first negative ion distribution scheme to generate the first design scheme, and store it in the equipment optimization database.

[0344] The said Step S1210 includes:

[0345] Step S1211: 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 dimension constraint setting.

[0346] Step S1212: Construct a three-dimensional solid model of the air flow channel according to the spatial layout parameters of the air flow channel, and assemble and integrate it with the three-dimensional solid model of the crushing cavity to generate a three-dimensional assembly model of the crushing cavity.

[0347] Step S1213: In the three-dimensional assembly model of the crushing cavity, based on the gas-solid two-phase flow theory, by adjusting the spatial layout parameters of the air flow channel, simulate the flow trajectory and velocity distribution of the air flow inside the crushing cavity, evaluate the flow field uniformity, and select the air flow channel layout scheme with the optimal gas-solid two-phase flow effect to form the first air flow path.

[0348] The said step S1220 includes:

[0349] Step S1221: Divide 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: Determine the coverage position and coverage density of the conductive material layer, generate a three-dimensional solid model of the conductive material layer inside each geometric partition, and perform constrained assembly with the three-dimensional assembly model of the crushing cavity to obtain the three-dimensional assembly model of the conductive crushing cavity;

[0351] Step S1223: Based on the three-dimensional assembly model of the conductive crushing cavity, conduct an electrostatic field simulation and calculate the electric field uniformity index inside the crushing cavity;

[0352] Step S1224: According to the electric field uniformity index inside the crushing cavity, adjust the coverage position and coverage density of the conductive material layer. When the electric field uniformity index reaches the preset convergence condition, obtain the first conductive material coverage scheme.

[0353] The said step S1230 includes:

[0354] Step S1231: Calculate the number and placement spacing of the negative ion generators required inside the crushing cavity according to the negative ion placement density; generate a placement position lattice of the negative ion generators on the inner wall surface of the three-dimensional assembly model of the conductive crushing cavity according to the number and placement spacing of the negative ion generators;

[0355] Step S1232: Insert the three-dimensional solid model of the negative ion generator at each placement point of the placement position lattice, and perform constrained assembly with the three-dimensional assembly model of the conductive crushing cavity to obtain the three-dimensional assembly model of the negative ion integrated crushing cavity;

[0356] Step S1233: Obtain the optimal working parameter combination of the negative ion generators at different placement positions, and assign it to each three-dimensional solid model of the negative ion generators in the three-dimensional assembly model of the negative ion integrated crushing cavity to form the first negative ion placement scheme.

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

[0358] Step S1310: Build an air flow field analysis model according to the first design scheme;

[0359] Step S1320: Obtain first air flow field parameters according to the air flow field analysis model. The first air flow field parameters include a velocity field, a pressure field, and a turbulence intensity, and store them in the equipment optimization database;

[0360] Step S1330: Compare the first air flow field parameters with preset air flow field parameters to obtain a first air flow field deviation. The first air flow field deviation includes a velocity deviation, a pressure deviation, and a turbulence intensity deviation;

[0361] Step S1340: Optimize the first air flow path according to the first air flow field deviation to obtain a second air flow 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 coverage scheme and the second negative ion placement scheme include:

[0363] Step S1410: Build an electrostatic aggregation prediction model based on the electrostatic properties of the materials in the material attribute table;

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

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

[0366] Step S1440: Optimize the first conductive material coverage scheme and the first negative ion placement scheme based on the first loss rate deviation to obtain a second conductive material coverage 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 equipment internal structure design scheme includes:

[0368] Step S1510: Combine the second air flow path, the second conductive material coverage scheme, and the second negative ion placement scheme to generate a second design scheme;

[0369] Step S1520: Repeat Step S1300 and Step S1400 to perform air flow field analysis, electrostatic aggregation prediction, and scheme optimization on the second design scheme until the Nth air flow field deviation and the Nth loss rate deviation obtained in the Nth iteration both meet the preset convergence conditions;

[0370] Step S1530: Determine the Nth design scheme obtained in the Nth iteration as the final equipment internal structure design scheme and store it in the equipment optimization database.

[0371] In the adaptive control module, based on the final internal structure design scheme of the device, the integration of the electrostatic monitoring and dynamic regulation mechanism includes: on the basis of the final internal structure design scheme of the device, adding an electrostatic monitoring unit and an electrostatic regulation unit; 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.

[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 electrostatic monitoring unit receives the data collected by the electrostatic sensor in real time, processes and analyzes it, and identifies the abnormal electrostatic aggregation state;

[0374] Step S2220, the electrostatic regulation 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 the working voltage and the working frequency;

[0375] Step S2230, the electrostatic regulation unit feeds back the adjusted working parameters of the negative ion generator to the electrostatic monitoring unit to form a closed-loop control.

[0376] The said step S2210 includes:

[0377] Step S2211, arrange electrostatic sensors at key positions inside the crushing cavity to collect the electrostatic voltage and charge amount data during the operation of the device in real time;

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

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

[0380] Step S2214, the data processing module extracts features from the preprocessed electrostatic voltage and charge amount data to obtain an electrostatic feature vector, and the electrostatic feature vector includes the mean value of the electrostatic voltage, the peak value of the electrostatic voltage, the fluctuation frequency of the electrostatic voltage, the mean value of the electrostatic charge amount, and the peak value of the electrostatic charge amount;

[0381] Step S2215, based on the electrostatic feature vector, the data analysis module uses a pre-constructed abnormal electrostatic aggregation state recognition model to identify and classify the electrostatic aggregation state during the operation of the device in real time, and sends the recognition result to the electrostatic regulation unit.

[0382] The said step S2220 includes:

[0383] Step S2221: The controller of the electrostatic regulation unit receives the electrostatic aggregation state recognition result sent by the electrostatic monitoring unit, and generates a regulation instruction according to a preset control strategy.

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

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

[0386] Step S2310: Set the safety threshold range of the working parameters of the negative ion generator, including the voltage threshold and the frequency threshold.

[0387] Step S2320: During the electrostatic regulation process, if the working parameters of the negative ion generator exceed the safety threshold range, the electrostatic regulation 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 device control system immediately executes the preset protection measures.

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

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

[0391] As described above in the specific embodiments, the purpose, technical solutions, and beneficial effects of the present invention are further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. Method for designing the internal structure of a low-temperature air-flow nano-crushing device for medicine and food homologous plants, characterized in that, The method includes: Obtaining the characteristic parameters of the target crushing object, determining the initial structure parameters of the device based on the characteristic parameters, and taking the initial structure parameters of the device as the first structure parameters; generating a first design plan according to the first structure parameters; performing airflow field analysis and optimization on the first design plan to obtain a second airflow path; performing electrostatic aggregation prediction and plan optimization on the first design plan to obtain a second conductive material coverage plan and a second negative ion placement plan; combining the second airflow path, the second conductive material coverage plan and the second negative ion placement plan to generate a second design plan; iteratively optimizing the second design plan to obtain the final internal structure design plan of the device; Based on the final internal structure design plan of the device, integrating an electrostatic monitoring and dynamic adjustment mechanism to perform real-time adaptive control on the device and establish a device protection mechanism.

2. The method for the internal structure design of the low-temperature airflow nano-crushing equipment for medicine and food homology plants according to claim 1, wherein The characteristic parameters of the target crushing object include the target crushing granularity, the target output and the material properties; The obtaining of the characteristic parameters of the target crushing object includes: Obtaining the target crushing granularity and the target output of the target crushing object and storing them in the process requirement table in the device optimization database; Obtaining the material properties of the target crushing object, where the material properties include the material density, the material moisture content and the material electrostatic properties, and storing the material properties in the material attribute table in the device optimization database; The determining of the initial structure parameters of the device based on the characteristic parameters includes: Based on the process requirement table and the material attribute table, and in combination with the preset range of device structure parameters, determining the initial structure parameters of the device; the initial structure parameters include the spatial layout parameters of the crushing cavity, the spatial layout parameters of the airflow channel, the coverage ratio of the conductive material and the negative ion placement density; the spatial layout parameters of the crushing cavity include the size and geometric shape of the crushing cavity; the spatial layout parameters of the airflow channel include the cross-sectional shape, the cross-sectional size, the length, the spatial position of the airflow channel, the connection mode of the airflow channel and the bending angle of the airflow channel.

3. The method for the internal structure design of the low-temperature air-flow nano-comminution equipment for medicine and food homology plants according to claim 2, characterized in that, The generating of the first design plan according to the first structure parameters includes: Determining the first airflow path of the crushing cavity according to the spatial layout parameters of the crushing cavity and the spatial layout parameters of the airflow channel in the initial structure parameters of the device; Determining the first conductive material coverage plan according to the coverage ratio of the conductive material in the initial structure parameters of the device; Determining the first negative ion placement plan according to the negative ion placement density in the initial structure parameters of the device; Combining the first airflow path, the first conductive material coverage plan and the first negative ion placement plan to generate a first design plan and storing the first design plan in the device optimization database.

4. The method for the internal structure design of the low-temperature airflow nano-crushing equipment for medicine and food homologous plants according to claim 3, characterized in that The determining of the first airflow path of the crushing cavity includes: Generating a three-dimensional solid model of the crushing cavity according to the spatial layout parameters of the crushing cavity, and performing geometric parameter annotation and dimension constraint setting; 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 crushing cavity to generate a three-dimensional assembly model of the crushing cavity; In the three-dimensional assembly model of the crushing cavity, based on the gas-solid two-phase flow theory, by adjusting the spatial layout parameters of the air flow channel, the flow trajectory and velocity distribution of the air flow inside the crushing cavity are simulated, the uniformity of the flow field is evaluated, and the air flow channel layout scheme with the optimal gas-solid two-phase flow effect is selected to form the first air flow path.

5. The method for designing the internal structure of the low-temperature air-flow nano-crushing equipment for medicine and food homology plants according to claim 4, characterized in that The determination of the first conductive material coverage scheme includes: 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 conductive material coverage ratio; Determining the coverage position and coverage density of the conductive material layer, generating a three-dimensional solid model of the conductive material layer inside each geometric partition, and performing constrained assembly on the three-dimensional solid model of the conductive material layer and the three-dimensional assembly model of the crushing cavity to obtain a three-dimensional assembly model of the conductive crushing cavity; Based on the three-dimensional assembly model of the conductive crushing cavity, perform electrostatic field simulation and calculate the electric field uniformity index inside the crushing cavity; According to the electric field uniformity index inside the crushing cavity, adjust the coverage position and coverage density of the conductive material layer. When the electric field uniformity index reaches the preset convergence condition, the first conductive material coverage scheme is obtained.

6. The method for the internal structure design of the low-temperature air-flow nano-crushing equipment for medicine and food homology plants according to claim 5, characterized in that, The determination of the first negative ion placement scheme includes: According to the negative ion placement density, calculate the number and placement spacing of the negative ion generators required inside the crushing cavity; according to the number and placement spacing of the negative ion generators, generate a placement position lattice of the negative ion generators on the inner wall surface of the three-dimensional assembly model of the conductive crushing cavity; At each placement point of the placement position lattice, insert a three-dimensional solid model of the negative ion generator, and perform constrained assembly on the three-dimensional solid model of the negative ion generator and the three-dimensional assembly model of the conductive crushing cavity to obtain a three-dimensional assembly model of the negative ion integrated crushing cavity; Obtain the optimal working parameter combination of the negative ion generator under different placement positions, and assign the optimal working parameter combination of the negative ion generator 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 the first negative ion placement scheme.

7. The method for designing the internal structure of the low-temperature airflow nano-crushing equipment for medicine and food homologous 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: Build an airflow field analysis model according to the first design scheme; According to the airflow field analysis model, obtain the first airflow field parameters, where the first airflow field parameters include velocity field, pressure field, and turbulence intensity, and store the first airflow field parameters in the equipment optimization database; Compare the first airflow field parameters with the preset airflow field parameters to obtain the first airflow field deviation, where the first airflow field deviation includes velocity deviation, pressure deviation, and turbulence intensity deviation; Optimize the first airflow path according to the first airflow field deviation to obtain the second airflow path.

8. The method for the internal structure design of the low-temperature airflow nano-crushing equipment for medicine and food homologous plants according to claim 3, characterized in that, The obtaining of the second conductive material coverage scheme and the second negative ion placement scheme includes: Based on the electrostatic properties of the materials in the material property table, construct an electrostatic aggregation prediction model; According to the first conductive material coverage scheme, the first negative ion placement scheme, and the electrostatic aggregation prediction model, obtain the first predicted powder loss rate; Compare the first predicted powder loss rate with the preset target loss rate to obtain the first loss rate deviation; Optimize the first conductive material coverage scheme and the first negative ion placement scheme based on the first loss rate deviation to obtain the second conductive material coverage scheme and the second negative ion placement scheme.

9. The method for the internal structure design of the low-temperature air-flow nano-crushing equipment for medicine and food homology plants according to claim 1, characterized in that Based on the final device internal structure design scheme, the integration of the electrostatic monitoring and dynamic regulation mechanism includes: On the basis of the final device internal structure design scheme, add an electrostatic monitoring unit and an electrostatic regulation unit; 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; The real-time adaptive control of the device includes: During the operation of the device, the electrostatic monitoring unit receives the data collected by the electrostatic sensor in real time, processes and analyzes it, and identifies the abnormal state of electrostatic accumulation; The electrostatic regulation unit dynamically adjusts the working parameters of the negative ion generator according to the analysis results of the electrostatic monitoring unit; The electrostatic regulation 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 the internal structure design of a low-temperature air-flow nano-crushing device for medicine and food homology plants, which is used to implement the method for the internal structure design of the low-temperature air-flow nano-crushing device for medicine and food homology plants described in any one of claims 1-9, characterized in that, The platform includes: The first design scheme generation module: used to obtain the characteristic parameters of the target crushing object, determine the initial device structure parameters based on the characteristic parameters, and use the initial device structure parameters as the first structure parameters; generate the first design scheme according to the first structure parameters; The scheme optimization module: used to analyze and optimize the air flow field of the first design scheme to obtain the second air flow path; predict and optimize the electrostatic accumulation of the first design scheme to obtain the second conductive material coverage scheme and the second negative ion placement scheme; combine the second air flow path, the second conductive material coverage scheme, and the second negative ion placement scheme to generate the second design scheme; iteratively optimize the second design scheme to obtain the final device internal structure design scheme; The adaptive control module: used to integrate the electrostatic monitoring and dynamic regulation mechanism on the basis of the final device internal structure design scheme, perform real-time adaptive control on the device, and establish a device protection mechanism.

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