Medicine crushing method and device

Abstract

The invention provides a medicine crushing method and device, and relates to the technical field of medicine processing, and the technical scheme is characterized in that a target crushing state is set, and the target crushing state comprises a target particle size distribution range and a target effective component retention rate; acquiring real-time particle size distribution information and process parameter information in the traditional Chinese medicine crushing process, wherein the process parameter information comprises temperature, humidity and pressure in a crushing cavity; calculating the deviation between the current crushing state and the target crushing state according to the real-time particle size distribution information, the process parameter information and the target crushing state; based on the deviation and a preset traditional Chinese medicine characteristic model, a crushing parameter adjustment scheme is generated, and the crushing parameter adjustment scheme comprises adjustment values of the rotating speed of a crushing main machine, the pressure in a crushing cavity and the feeding speed; and according to the crushing parameter adjustment scheme, working parameters of the crushing equipment are automatically adjusted. The medicine smashing method and device have the advantages that the smashing efficiency is improved, and the smashing quality and the effective component retention rate are guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of drug processing, and more specifically, to a drug pulverization method and device. Background Art

[0002] During the drug pulverization process, there are many problems with traditional pulverization methods. Usually, operators need to pause the operation after the equipment has been running for a period of time, take samples from the discharge port for particle size inspection. This method may not only cause the loss of components of the pulverized medicinal materials, but also affect the pulverization uniformity. It may also affect the temperature control due to the equipment stop, thereby affecting the retention of active ingredients. Operators must quickly complete the particle size analysis within a limited time and immediately adjust the pulverization parameters according to the results. Since different batches of drugs may require different parameter settings, it may take several attempts to achieve the ideal pulverization effect.

[0003] This frequent sampling and adjustment process not only affects the overall processing efficiency, increases labor costs, but also requires ensuring that the retention rate of active ingredients meets the requirements while maintaining an appropriate particle size range. The entire process requires operators to make accurate judgments and adjustments within a limited time to balance the complex relationship among pulverization quality, efficiency, retention of active ingredients, and cost control. This traditional method is difficult to achieve continuous and stable production, nor can it adapt to the characteristic differences of different batches of medicinal materials, severely restricting the efficiency and quality of pulverization processing.

[0004] In addition, traditional methods lack real-time monitoring and automatic adjustment mechanisms and cannot respond in a timely manner to dynamic changes during the pulverization process. For example, process parameters such as temperature, humidity, and pressure in the pulverization chamber have a significant impact on the pulverization effect and the retention rate of active ingredients, but it is difficult for traditional methods to achieve real-time monitoring and precise control of these parameters. At the same time, different types and batches of Chinese medicinal materials have different physical properties, such as hardness, fiber structure, and water content, and these factors will all affect the pulverization effect, but it is difficult for traditional methods to make personalized parameter adjustments for these characteristics.

[0005] In view of the above problems, there is an urgent need for improvement in the prior art. Summary of the Invention

[0006] The purpose of this application is to provide a drug pulverization method and device, which have the advantages of improving pulverization efficiency, ensuring pulverization quality, and retention rate of active ingredients.

[0007] This application provides a drug pulverization method, and the technical solution is as follows: Including: setting a target comminution state, where the target comminution state includes a target particle size distribution range and a target active ingredient retention rate; obtaining real-time particle size distribution information and process parameter information during the comminution of traditional Chinese medicine, where the process parameter information includes the temperature, humidity, and pressure in the comminution chamber; calculating the deviation between the current comminution state and the target comminution state based on the real-time particle size distribution information, process parameter information, and target comminution state; generating a comminution parameter adjustment scheme based on the deviation and a preset traditional Chinese medicine characteristic model that combines at least one characteristic parameter of the hardness, fiber structure, water content, and thermal sensitivity of active ingredients of traditional Chinese medicine, where the comminution parameter adjustment scheme includes adjustment values for the rotation speed of the comminution main machine, the pressure in the comminution chamber, and the feeding speed; automatically adjusting the working parameters of the comminution equipment according to the comminution parameter adjustment scheme.

[0008] Further, the present application also proposes that the step of generating a comminution parameter adjustment scheme based on the deviation and a preset traditional Chinese medicine characteristic model that combines at least one characteristic parameter of the hardness, fiber structure, water content, and thermal sensitivity of active ingredients of traditional Chinese medicine includes: obtaining the type information and batch information of the current traditional Chinese medicine; retrieving corresponding traditional Chinese medicine characteristic parameters from a preset traditional Chinese medicine characteristic database that is part of the preset traditional Chinese medicine characteristic model according to the type information and batch information, where the characteristic parameters include the hardness of the medicinal material, fiber structure, water content, and thermal sensitivity of active ingredients; calculating the optimal adjustment values for the rotation speed of the comminution main machine, the pressure in the comminution chamber, and the feeding speed based on the traditional Chinese medicine characteristic parameters and the deviation; using the optimal adjustment values as the adjustment values of the comminution parameter adjustment scheme.

[0009] Further, the present application also proposes that the step of calculating the optimal adjustment values for the rotation speed of the comminution main machine, the pressure in the comminution chamber, and the feeding speed based on the traditional Chinese medicine characteristic parameters and the deviation includes: obtaining an optimization weight parameter input by an operator, where the optimization weight parameter is used to adjust the relative importance of particle size distribution, active ingredient retention rate, and energy consumption in the multi-objective optimization process; establishing a relationship model between comminution parameters and particle size distribution, active ingredient retention rate, and energy consumption according to the traditional Chinese medicine characteristic parameters; inputting the relationship model, the deviation, and the optimization weight parameter into a preset multi-objective optimization algorithm; calculating the optimal adjustment values for the rotation speed of the comminution main machine, the pressure in the comminution chamber, and the feeding speed under the current optimization weight through the multi-objective optimization algorithm; comparing the optimal adjustment values with a preset safety parameter range to ensure that the adjusted parameters do not exceed the safe operation range.

[0010] Furthermore, the present application also proposes that the step of calculating the optimal adjustment values of the rotation speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed under the current optimization weight through the multi-objective optimization algorithm includes: obtaining the thermosensitivity value of the active ingredient in the traditional Chinese medicine characteristic parameters; setting a temperature change threshold and a temperature monitoring time interval according to the thermosensitivity value of the active ingredient; obtaining the temperature data in the crushing chamber within each temperature monitoring time interval and calculating the temperature change rate; when the temperature change rate exceeds the temperature change threshold, triggering a temperature control strategy, including: reducing the rotation speed of the crushing main machine to reduce heat generation by friction; increasing the feeding speed to enhance heat dissipation; adjusting the pressure in the crushing chamber to optimize the heat distribution; calculating the optimal adjustment values of the rotation speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed under the current optimization weight according to the multi-objective optimization algorithm and the temperature control strategy.

[0011] Furthermore, the present application also proposes that the step of obtaining the optimization weight parameters input by the operator includes: retrieving the corresponding recommended optimization weight parameters from a preset traditional Chinese medicine characteristic database according to the type information and batch information of the current traditional Chinese medicine, where the recommended optimization weight parameters include particle size distribution weight, active ingredient retention rate weight, and energy consumption weight; displaying the recommended optimization weight parameters to the operator; receiving the adjustment input of the operator to the recommended optimization weight parameters; generating the final optimization weight parameters according to the adjustment input.

[0012] Furthermore, the present application also proposes that the step of comparing the optimal adjustment values with a preset safety parameter range to ensure that the adjusted parameters do not exceed the safe operating range includes: obtaining the corresponding safety parameter range from a preset traditional Chinese medicine safety parameter database according to the type information and batch information of the traditional Chinese medicine, where the safety parameter range includes the rotation speed range of the crushing main machine, the pressure range in the crushing chamber, and the feeding speed range; comparing the calculated optimal adjustment values of the rotation speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed with the corresponding safety parameter ranges respectively; when any optimal adjustment value exceeds the corresponding safety parameter range, perform the following steps: setting the adjustment value exceeding the range to the boundary value of the safety parameter range close to the original adjustment value; recalculating the adjustment values of other parameters based on the corrected adjustment value to maintain the balance between crushing effect and safety; updating the safety parameter range in the traditional Chinese medicine safety parameter database and recording the current adjustment situation.

[0013] Further, the present application also proposes that the step of setting the temperature change threshold and the temperature monitoring time interval according to the thermosensitivity value of the active ingredient includes: obtaining the current ambient humidity data and the fiber structure data in the traditional Chinese medicine characteristic parameters; based on the thermosensitivity value of the active ingredient, the ambient humidity data and the fiber structure data, calculating the dynamic temperature change threshold through a heat conduction model; determining a correction coefficient for the temperature monitoring time interval according to the fiber structure data; performing weighted calculation on the dynamic temperature change threshold and a preset basic temperature change threshold to obtain the final temperature change threshold; and adjusting the preset basic temperature monitoring time interval based on the correction coefficient to obtain the final temperature monitoring time interval.

[0014] Further, the present application also proposes that the step of retrieving the corresponding traditional Chinese medicine characteristic parameters from a preset traditional Chinese medicine characteristic database that is part of the preset traditional Chinese medicine characteristic model according to the type information and batch information includes: sending a query request containing the type information and batch information to the traditional Chinese medicine characteristic database; receiving, according to the query request, a query result including the hardness of the medicinal material, the fiber structure, the water content, and the thermosensitivity of the active ingredient; comparing the query result with a preset parameter range; when any parameter in the query result exceeds the preset parameter range, triggering a parameter correction process, including: obtaining the actual crushing data of the traditional Chinese medicine of the current batch; based on the actual crushing data, performing correction calculation on the parameter that exceeds the preset parameter range; updating the corrected parameter to the traditional Chinese medicine characteristic database; and using the finally determined traditional Chinese medicine characteristic parameters for subsequent crushing parameter adjustment.

[0015] Furthermore, the present application also proposes that the traditional Chinese medicine property model at least includes a dynamic particle size distribution prediction model and a multi-objective optimization control model. The step of generating a comminution parameter adjustment scheme based on the deviation and the preset traditional Chinese medicine property model includes: predicting the particle size distribution at the next moment by using the dynamic particle size distribution prediction model based on the real-time particle size distribution information and process parameter information, where: the dynamic particle size distribution prediction model is: P(d,t) = A * exp(-((d-μ(t))^2) / (2*σ(t)^2)) * W(t); μ(t) = k1*v(t) + k2*p(t) + k3*f(t) + k4*v(t)*p(t)+ k5*dT(t) / dt; σ(t) = c1*v(t) + c2*p(t) + c3*f(t) + c4*H(t) + c5*∫T(t)dt; W(t)= 1 + w1*sin(2πt / τ) + w2*exp(-t / τ); where, P(d,t) is the probability density of the particle size d at time t, d is the particle diameter, t is the time, A is the normalization coefficient, μ(t) is the average particle size, σ(t) is the particle size standard deviation, W(t) is the time-varying weight function, v(t) is the rotation speed of the comminution main machine, p(t) is the pressure in the comminution chamber, f(t) is the feeding speed, T(t) is the working temperature, H(t) is the relative humidity, τ is the characteristic time constant, k1-k5, c1-c5, w1-w2 are model coefficients; evaluating the deviation by using the multi-objective optimization control model according to the predicted particle size distribution and the current process parameters, so as to calculate the optimal comminution parameter adjustment scheme, where: the multi-objective optimization control model is: objective function: J = min[λ1*(1-Q) + λ2*(1-R(t)) + λ3*E]; Q = ∫P(d,t)dd (50μm ≤ d ≤ 200μm); R(t) = R0 * exp(-α*T(t)) * exp(-β*H(t)); E = η1*v(t)^2 + η2*p(t) + η3*f(t); constraint conditions: 50μm ≤ μ(t) ≤ 200μm; R(t) ≥ 95%; T(t) ≤Tmax; v(t) ≤ vmax; p(t) ≤ pmax; where, J is the objective function, Q is the proportion of particles in the target particle size range, R(t) is the effective ingredient retention rate, E is the energy consumption function, R0 is the initial retention rate, α is the temperature influence coefficient, β is the humidity influence coefficient, λ1-λ3 are optimization weight coefficients, η1-η3 are energy consumption coefficients, Tmax is the maximum allowable temperature, vmax is the maximum allowable rotation speed, pmax is the maximum allowable pressure; taking the calculated optimal comminution parameter adjustment scheme as the comminution parameter adjustment scheme for automatically adjusting the working parameters of the comminution equipment.

[0016] Furthermore, the present application also proposes a drug pulverizing device, including: a target setting module for setting a target pulverizing state, where the target pulverizing state includes a target particle size distribution range and a target active ingredient retention rate; a data acquisition module for obtaining real-time particle size distribution information and process parameter information during the pulverization of traditional Chinese medicine, where the process parameter information includes the temperature, humidity, and pressure in the pulverizing chamber; a calculation module for calculating the deviation between the current pulverizing state and the target pulverizing state based on the real-time particle size distribution information, process parameter information, and target pulverizing state; a scheme generation module for generating a pulverizing parameter adjustment scheme based on the deviation and a preset traditional Chinese medicine characteristic model that combines at least one characteristic parameter of the hardness, fiber structure, water content, and heat sensitivity of the active ingredient of the traditional Chinese medicine, where the pulverizing parameter adjustment scheme includes adjustment values for the rotation speed of the pulverizing main machine, the pressure in the pulverizing chamber, and the feeding speed; and a parameter adjustment module for automatically adjusting the working parameters of the pulverizing equipment according to the pulverizing parameter adjustment scheme.

[0017] As can be seen from the above, a drug pulverizing method and device provided by the present application include setting a target pulverizing state, obtaining real-time pulverizing information, calculating the deviation, generating a parameter adjustment scheme, and automatically adjusting the working parameters. Through the real-time monitoring and automatic adjustment mechanism, it can respond in a timely manner to the dynamic changes during the pulverization process, improve the pulverization efficiency, ensure the pulverization quality and the active ingredient retention rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a drug pulverizing method provided by the present application.

[0019] Figure 2 It is a schematic structural diagram of a drug pulverizing device provided by the present application.

[0020] In the figure: 210, target setting module; 220, data acquisition module; 230, calculation module; 240, scheme generation module; 250, parameter adjustment module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the present application will be clearly and completely described in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the present application described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0022] During the process of traditional Chinese medicine crushing and processing, real-time and precise particle size analysis and automatic parameter adjustment are key technical challenges. Traditional methods rely on frequent downtime sampling and manual analysis, which not only affects production efficiency but also may lead to a decrease in crushing uniformity and loss of active ingredients. In addition, the characteristic differences of medicinal materials in different batches require operators to have rich experience to make accurate judgments and adjustments within a limited time. This method is difficult to achieve continuous and stable production and cannot effectively adapt to raw material changes, seriously restricting the quality control and efficiency improvement of traditional Chinese medicine crushing and processing.

[0023] For example, a batch of Astragalus membranaceus needs to be crushed to a particle size range of 50 - 200 μm while ensuring that the retention rate of active ingredients is greater than 95%. Traditional processes require sampling to be stopped every 5 minutes of operation, which not only interrupts continuous production but also may affect temperature control due to equipment stoppage, thereby affecting the retention of active ingredients. Operators need to complete particle size analysis and adjust parameters within an extremely short time. Such high-pressure and high-frequency operations are prone to human errors. In addition, due to the lack of real-time monitoring means, key parameters such as temperature, humidity, and pressure during the crushing process cannot be adjusted in a timely manner, which may lead to fluctuations in crushing quality.

[0024] In response to this, referring to Figure 1 , this application proposes a method for crushing drugs, including: S110. Set the target crushing state, where the target crushing state includes the target particle size distribution range and the target retention rate of active ingredients; S120. Obtain real-time particle size distribution information and process parameter information during the crushing of traditional Chinese medicine, where the process parameter information includes the temperature, humidity, and pressure in the crushing chamber; S130. Calculate the deviation between the current crushing state and the target crushing state based on the real-time particle size distribution information, process parameter information, and the target crushing state; S140. Generate a crushing parameter adjustment plan based on the deviation and a preset traditional Chinese medicine characteristic model that combines at least one characteristic parameter of the hardness, fiber structure, water content, and thermal sensitivity of active ingredients of traditional Chinese medicine. The crushing parameter adjustment plan includes adjustment values for the rotation speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed; S150. Automatically adjust the working parameters of the crushing equipment according to the crushing parameter adjustment plan.

[0025] Among them, the target crushing state refers to the expected crushing effect, including the target particle size distribution range and the target retention rate of active ingredients.

[0026] Among them, the real-time particle size distribution information refers to the data of the particle size distribution of medicinal material particles continuously obtained during the crushing process. Specifically, it can be achieved by using an online laser particle size analyzer or an image analysis system.

[0027] Among them, the process parameter information refers to the key process indicators that affect the crushing effect, including the temperature, humidity, and pressure inside the crushing chamber. Specifically, temperature sensors, humidity sensors, and pressure sensors can be used to achieve real-time monitoring.

[0028] Among them, the crushing parameter adjustment scheme refers to the parameter adjustment suggestions generated according to the deviation between the current crushing state and the target state for optimizing the crushing effect. Specifically, an algorithm based on a preset model can be used to calculate the adjustment values of the main crusher speed, the pressure inside the crushing chamber, and the feeding speed.

[0029] The core innovation of this application lies in establishing a closed-loop control system, which realizes real-time monitoring, precise analysis, and automatic adjustment of the traditional Chinese medicine crushing process. By setting a clear target crushing state, the system can continuously compare the real-time data with the target state, calculate the deviation, and generate targeted parameter adjustment schemes based on the preset traditional Chinese medicine characteristic model. This method not only avoids the problem of frequent shutdown for sampling in traditional processes but also can automatically adjust the crushing strategy according to the characteristics of different batches of medicinal materials, greatly improving the efficiency, stability, and adaptability of the crushing process.

[0030] The working principle of this application can be described in detail as follows: First, before the crushing equipment starts, the operator sets the target crushing state through the control interface, including the target particle size distribution range (such as 50 - 200 μm) and the target active ingredient retention rate (such as ≥ 95%). These parameters are input into the control unit of the system.

[0031] After the crushing process starts, the on-line laser particle size analyzer installed in the crushing chamber continuously collects particle size distribution data, and the data collection frequency can be set to once per second. At the same time, temperature sensors, humidity sensors, and pressure sensors respectively monitor the temperature, humidity, and pressure inside the crushing chamber in real time, and these data are also recorded at a frequency of once per second.

[0032] The control unit receives this real-time data and compares it with the preset target crushing state. Through a pre-programmed algorithm, the system calculates the deviation between the current crushing state and the target state. This deviation includes the deviation of the particle size distribution (such as the difference in the proportion of particles within the target range) and the estimated deviation of the active ingredient retention rate.

[0033] Next, the system calls the preset traditional Chinese medicine characteristic model. This model can be established based on a large amount of historical data and research on the characteristics of medicinal materials, and it contains the behavioral characteristics of different types of traditional Chinese medicine under different crushing parameters. The system inputs the calculated deviation data into this model.

[0034] Based on the traditional Chinese medicine characteristic model and the current deviation, the system generates a crushing parameter adjustment plan. This plan includes suggestions for adjusting the rotation speed of the main crushing machine, the pressure in the crushing chamber, and the feeding speed. For example, if the current particle size is too large, the system may suggest increasing the rotation speed or decreasing the feeding speed; if the temperature is too high and may affect the active ingredients, the system may suggest reducing the pressure or increasing the feeding speed to enhance heat dissipation.

[0035] Finally, these adjustment suggestions are transmitted to the actuators of the crushing equipment. The actuators include a variable-frequency motor controller (for adjusting the rotation speed), a pressure regulating valve (for adjusting the pressure in the chamber), and a feeding speed controller. These actuators automatically adjust the corresponding parameters according to the received instructions.

[0036] In some of the above embodiments of the present application, a crushing parameter adjustment plan is generated based on the deviation and a preset traditional Chinese medicine characteristic model to optimize the crushing process. However, in this process, the consideration of specific traditional Chinese medicine characteristics is lacking. Different types and batches of traditional Chinese medicine may have different physical and chemical characteristics, which will directly affect the crushing effect and the retention of active ingredients. If the characteristic information cannot be accurately obtained and utilized, it may lead to inaccurate adjustment of crushing parameters, affecting the final crushing quality and efficiency.

[0037] In response to this, the present application further proposes steps for generating a crushing parameter adjustment plan based on the deviation and a preset traditional Chinese medicine characteristic model that combines at least one characteristic parameter of the hardness, fiber structure, water content, and heat sensitivity of active ingredients of traditional Chinese medicine, including: obtaining the type information and batch information of the current traditional Chinese medicine; according to the type information and batch information, retrieving the corresponding traditional Chinese medicine characteristic parameters from a preset traditional Chinese medicine characteristic database that is part of the preset traditional Chinese medicine characteristic model, where the characteristic parameters include the hardness of the medicinal material, fiber structure, water content, and heat sensitivity of active ingredients; calculating the optimal adjustment values of the rotation speed of the main crushing machine, the pressure in the crushing chamber, and the feeding speed based on the traditional Chinese medicine characteristic parameters and the deviation; and using the optimal adjustment values as the adjustment values of the crushing parameter adjustment plan.

[0038] The technical solution of the present application realizes the intelligent and refined control of the crushing process by introducing traditional Chinese medicine characteristic parameters and databases. By obtaining the type and batch information of the current traditional Chinese medicine, the system can retrieve the corresponding characteristic parameters from the preset database, including the hardness of the medicinal material, fiber structure, water content, and heat sensitivity of active ingredients. This method ensures that the characteristics of each batch of traditional Chinese medicine can be accurately considered, rather than using general parameter settings.

[0039] Specifically, the hardness of the medicinal materials affects the required crushing force, the fiber structure affects the ease of crushing, the water content affects the temperature change and adhesiveness during the crushing process, and the thermosensitivity of the active ingredients is related to the ingredient retention rate. By comprehensively considering these characteristic parameters, the system can more accurately calculate the optimal rotational speed of the crushing mainframe, the pressure in the crushing chamber, and the adjustment value of the feeding speed.

[0040] For example, for Chinese medicinal materials with higher hardness, the system may increase the rotational speed of the crushing mainframe to provide sufficient crushing force. For Chinese medicinal materials with complex fiber structures, it may be necessary to adjust the pressure in the crushing chamber to ensure sufficient crushing. For Chinese medicinal materials with higher water content, the system may reduce the feeding speed to avoid adhesion. For active ingredients with higher thermosensitivity, the system may adopt a lower rotational speed of the crushing mainframe and a higher feeding speed to reduce heat generation and shorten the crushing time.

[0041] Furthermore, the technical solution of this application can also be automatically adjusted according to the characteristic differences of different batches of traditional Chinese medicine. For example, for the same kind of Chinese medicinal materials, due to differences in origin, harvesting time, or storage conditions, their characteristics such as hardness and water content may vary. By obtaining batch information and retrieving corresponding characteristic parameters, the system can perform personalized parameter adjustment for each batch of Chinese medicinal materials.

[0042] Thus, the technical solution of this application realizes the personalization and precision of crushing parameter adjustment. This method not only improves the adaptability and efficiency of the crushing process, but also helps to improve the ingredient retention rate and ensure the crushing quality. By introducing the characteristic parameters and database of traditional Chinese medicine, the system can automatically adjust the crushing strategy for different types and batches of traditional Chinese medicine, greatly improving the intelligent level of the crushing process.

[0043] As a preferred implementation manner, the traditional Chinese medicine characteristic database can contain information in multiple dimensions. In addition to the basic hardness of the medicinal materials, fiber structure, water content, and thermosensitivity of the active ingredients, it can also include parameters such as the density of the medicinal materials, particle shape, and brittleness index. These additional parameters can further improve the precise control of the crushing process. For example, density information can be used to optimize the feeding speed, particle shape information can be used to predict the particle size distribution after crushing, and the brittleness index can be used to adjust the crushing force.

[0044] Specifically, when the system obtains that the current traditional Chinese medicine is Astragalus membranaceus and the batch number is xxxxxxxx, it will retrieve the corresponding parameters from the preset traditional Chinese medicine characteristic database. Suppose the retrieved parameters are: medicinal material hardness 8.5 (Mohs hardness), fiber structure complexity 0.7 (0-1 standardized value), water content 12%, and active ingredient thermosensitivity 0.6 (0-1 standardized value, 1 indicating the most sensitive).

[0045] Based on these characteristic parameters and the deviation between the current comminution state and the target state, the system may calculate the following optimal adjustment values: the rotation speed of the main comminution machine: increase by 50 rpm, the pressure in the comminution chamber: increase by 0.2 MPa, the feeding speed: decrease by 10 kg / h.

[0046] These adjustment values take into account the relatively high hardness of Astragalus membranaceus (requiring an increase in rotation speed), the complex fiber structure (requiring an increase in pressure), the moderate water content, and the high thermosensitivity (requiring a decrease in the feeding speed to control the temperature). Through this refined adjustment, the system can maximize the protection of active ingredients while ensuring the comminution effect.

[0047] The technical solution of this application realizes the refined control of the comminution process by introducing the characteristic parameters of traditional Chinese medicine and utilizing the preset database of traditional Chinese medicine characteristics. This method can effectively solve the problem of insufficient consideration of the characteristics of different traditional Chinese medicines in traditional comminution methods, and improve the comminution efficiency and quality. By considering the specific characteristics of the medicinal materials, such as hardness and fiber structure, the system can more precisely control the comminution process, making the particle size distribution of the final product more in line with the target requirements. By considering the thermosensitivity and water content of the medicinal materials, the system can optimize the comminution parameters to reduce heat generation, thereby better protecting the thermosensitive ingredients. By automatically adjusting the comminution parameters, the number of manual interventions and downtime sampling is reduced, significantly improving the production efficiency. For different types and batches of traditional Chinese medicines, the system can automatically adjust the comminution strategy, improving the adaptability and versatility of the equipment. By precisely controlling the comminution parameters, the situations of over-comminution or under-comminution are avoided, thus optimizing the energy use. Through the application of the database and automatic control, the influence of human factors is reduced, and the quality consistency between different batches of products is improved.

[0048] In some of the above embodiments of this application, the optimal adjustment values of the rotation speed of the main comminution machine, the pressure in the comminution chamber, and the feeding speed are calculated based on the characteristic parameters of traditional Chinese medicine and the deviation to optimize the comminution parameters. However, in this process, relying solely on the characteristic parameters of traditional Chinese medicine and the deviation may not fully consider the experience of the operator and the special requirements of different batches of medicinal materials, resulting in the optimization results may not be comprehensive and flexible enough. In addition, directly applying the calculated optimal adjustment values may exceed the safe operating range of the equipment, posing potential safety hazards.

[0049] In response to this, the present application further proposes to obtain optimized weight parameters input by an operator, where the optimized weight parameters are used to adjust the relative importance of particle size distribution, active ingredient retention rate, and energy consumption in the multi-objective optimization process; establish a relationship model between comminution parameters and particle size distribution, active ingredient retention rate, and energy consumption according to the characteristics of traditional Chinese medicine; input the relationship model, deviation, and optimized weight parameters into a preset multi-objective optimization algorithm; calculate, through the multi-objective optimization algorithm, the optimal adjustment values of the comminution mainframe rotation speed, pressure in the comminution chamber, and feed rate under the current optimized weights; compare the optimal adjustment values with the preset safety parameter range to ensure that the adjusted parameters do not exceed the safe operating range.

[0050] Through the introduction of the optimized weight parameters input by the operator, this technical solution enables the comminution process to flexibly adjust the optimization objectives according to the characteristics of different batches of medicinal materials and production requirements. Establishing a relationship model between comminution parameters and particle size distribution, active ingredient retention rate, and energy consumption enables the system to more comprehensively consider the mutual influence among various factors. Using a multi-objective optimization algorithm to calculate the optimal adjustment values can find the best balance point while meeting multiple objectives. Finally, comparing the calculated optimal adjustment values with the preset safety parameter range ensures the safe operation of the equipment.

[0051] In the implementation process of the technical solution of the present application, it is first necessary to obtain the optimized weight parameters input by the operator. These parameters can be input through a human-machine interface or retrieved from a preset database and then fine-tuned by the operator. The optimized weight parameters generally include the weight of particle size distribution, the weight of active ingredient retention rate, and the weight of energy consumption, and their numerical range can be set between 0 and 1, and the sum of the three is 1.

[0052] Next, establish a relationship model between comminution parameters and particle size distribution, active ingredient retention rate, and energy consumption according to the characteristics of traditional Chinese medicine. This model can be constructed using methods such as multiple regression analysis, neural networks, or support vector machines. For example, a multiple non-linear regression model in the following form can be used: Particle size distribution = f1(Rotation speed, Pressure, Feed rate, Hardness, Fiber structure); Active ingredient retention rate = f2(Rotation speed, Pressure, Feed rate, Thermosensitivity, Water content); Energy consumption = f3(Rotation speed, Pressure, Feed rate); Among them, f1, f2, and f3 are non-linear functions that can be obtained by fitting historical data.

[0053] Input the established relationship model, the calculated deviation, and the optimization weight parameters input by the operator into a preset multi-objective optimization algorithm. The multi-objective optimization algorithm can be selected from genetic algorithms, particle swarm algorithms, multi-objective evolutionary algorithms, etc. The objective function of the algorithm can be set as: min J = w1 * (target particle size distribution - actual particle size distribution)^2 + w2 * (1 - active ingredient retention rate) + w3 * energy consumption.

[0054] Among them, w1, w2, and w3 are the optimization weight parameters input by the operator.

[0055] Through the multi-objective optimization algorithm, calculate the optimal adjustment values of the rotational speed of the main grinding machine, the pressure in the grinding chamber, and the feeding speed under the current optimization weights. These adjustment values represent the best balance point under the current conditions, taking into account both the grinding effect and the retention of active ingredients and energy consumption control.

[0056] Finally, compare the calculated optimal adjustment values with the preset safety parameter range. The safety parameter range can be preset according to the equipment specifications and the characteristics of traditional Chinese medicine materials.

[0057] If the optimal adjustment value exceeds the safety range, the system will automatically adjust it to the closest safety boundary value and recalculate other parameters to ensure the overall optimization effect.

[0058] This method not only considers the characteristics of traditional Chinese medicine and the current grinding state, but also incorporates the experience of the operator, while taking into account multiple objectives such as grinding effect, active ingredient retention, and energy consumption. By introducing the comparison of safety parameters, the reliability and safety of the system are further improved. This comprehensive optimization method can better adapt to the characteristic differences of different batches of medicinal materials, improve the flexibility and accuracy of the grinding process, and ensure the safety of production.

[0059] In practical applications, the technical solution of this application can be integrated into the control system of traditional Chinese medicine grinding equipment. The control system includes a human-machine interface, a data acquisition module, a model calculation module, and a parameter adjustment module. The human-machine interface is used to display the current grinding state and receive the optimization weight parameters input by the operator. The data acquisition module obtains the particle size distribution information and process parameter information in real time. The model calculation module performs the establishment of the relationship model, the operation of the multi-objective optimization algorithm, and the comparison of safety parameters. The parameter adjustment module automatically adjusts the working parameters of the grinding equipment according to the calculation results.

[0060] For example, during the grinding process of Astragalus membranaceus, the operator may input the following optimization weight parameters according to the characteristics of the current batch of Astragalus membranaceus and production requirements: particle size distribution weight: 0.5, active ingredient retention rate weight: 0.3, energy consumption weight: 0.2.

[0061] The system first retrieves the characteristic parameters of Astragalus membranaceus from the traditional Chinese medicine characteristic database, such as hardness, fiber structure, water content, and thermosensitivity of active ingredients. Then, based on the particle size distribution information and process parameter information collected in real time, it calculates the deviation between the current grinding state and the target grinding state.

[0062] Next, the multi-objective optimization algorithm calculates the optimal grinding parameter adjustment plan based on the established relationship model, the calculated deviation, and the input optimization weight parameters. Suppose the calculation result is: the rotation speed of the main grinding machine: increase by 100 rpm, the pressure in the grinding chamber: decrease by 0.05 MPa, and the feeding speed: increase by 2 kg / h.

[0063] The system compares these adjustment values with the preset safety parameter range. If all parameters are within the safety range, the control system will directly execute these adjustments. If a certain parameter exceeds the safety range, the system will automatically adjust it to the safety boundary value and recalculate other parameters.

[0064] In this way, the technical solution of this application can realize the intelligent adjustment of grinding parameters on the premise of ensuring grinding quality and equipment safety. This not only improves the accuracy and stability of the grinding process, but also reduces manual intervention, lowers the operation difficulty and human error. At the same time, due to considering multiple objectives and safety factors, this method can better balance the grinding effect, retention of active ingredients, and energy consumption control, improving the overall production efficiency and product quality.

[0065] In some of the above embodiments of this application, the optimal adjustment values of the rotation speed of the main grinding machine, the pressure in the grinding chamber, and the feeding speed are calculated based on the traditional Chinese medicine characteristic parameters and the deviation to optimize the grinding process. However, in this process, for traditional Chinese medicinal materials with high thermosensitivity, relying solely on static parameter adjustment may not be able to respond to the temperature changes in the grinding process in a timely manner, thus affecting the retention rate of active ingredients. Therefore, a dynamic adjustment method that can monitor and control temperature changes in real time is needed to better protect thermosensitive ingredients.

[0066] For this, this application further proposes to obtain the thermosensitivity value of the active ingredient in the traditional Chinese medicine characteristic parameters; set the temperature change threshold and temperature monitoring time interval according to the thermosensitivity value of the active ingredient; within each temperature monitoring time interval, obtain the temperature data in the grinding chamber and calculate the temperature change rate; when the temperature change rate exceeds the temperature change threshold, trigger the temperature control strategy, including: reducing the rotation speed of the main grinding machine to reduce heat generation by friction; increasing the feeding speed to enhance heat dissipation; adjusting the pressure in the grinding chamber to optimize heat distribution; according to the multi-objective optimization algorithm and the temperature control strategy, calculate the optimal adjustment values of the rotation speed of the main grinding machine, the pressure in the grinding chamber, and the feeding speed under the current optimization weight.

[0067] The technical solution of this application introduces a dynamic temperature control strategy based on the thermosensitivity of active ingredients. First, key parameters for temperature monitoring are determined by obtaining the thermosensitivity value of traditional Chinese medicine. Then, during the crushing process, the temperature change is monitored in real time. When the change exceeds the preset threshold, the temperature control strategy is immediately triggered. This strategy includes three aspects of adjustment: reducing the rotation speed to generate less heat, increasing the feed rate to enhance heat dissipation, and adjusting the pressure to optimize heat distribution. Finally, these temperature control measures are combined with a multi-objective optimization algorithm to calculate the optimal parameter adjustment plan.

[0068] This method solves the problem that static parameter adjustment cannot respond to temperature changes in a timely manner. Through real-time monitoring and dynamic adjustment, the temperature change during the crushing process can be controlled more precisely, thereby better protecting thermosensitive ingredients. This not only improves the retention rate of active ingredients but also optimizes energy consumption while ensuring the crushing quality. The innovation of this method lies in the combination of the temperature control strategy and the multi-objective optimization algorithm, realizing the intelligent and dynamic adjustment of crushing parameters, and significantly improving the precise control ability and product quality of the traditional Chinese medicine crushing process.

[0069] In the technical solution of this application, first, the thermosensitivity value of the active ingredient in the traditional Chinese medicine characteristic parameters is obtained. This value can be obtained through a pre-established traditional Chinese medicine characteristic database or determined through experiments. For example, for some traditional Chinese medicines with high thermosensitivity, their thermosensitivity values may be between 0.8 - 1.0 (assuming a normalized scale of 0 - 1).

[0070] According to the obtained thermosensitivity value, the system sets the temperature change threshold and the temperature monitoring time interval. The temperature change threshold can be dynamically adjusted according to the thermosensitivity value. For example, for traditional Chinese medicine with a thermosensitivity value of 0.9, the temperature change threshold can be set to 1.5 °C per minute. The temperature monitoring time interval can also be adjusted according to the thermosensitivity value. For example, for traditional Chinese medicine with high thermosensitivity, the monitoring interval can be set to once every 5 seconds.

[0071] Within each temperature monitoring time interval, the system obtains the temperature data in the crushing chamber through a temperature sensor and calculates the temperature change rate. The calculation of the temperature change rate can use a simple difference method or more complex methods such as moving window averaging to reduce noise effects.

[0072] When the calculated temperature change rate exceeds the preset temperature change threshold, the system triggers the temperature control strategy. This strategy includes three main aspects: Reducing the rotation speed of the crushing mainframe to reduce heat generation by friction. For example, when the temperature change rate exceeds the threshold, the system may reduce the rotation speed of the crushing mainframe by 10 - 20%.

[0073] Increase the feeding speed to enhance heat dissipation. By increasing the input of fresh medicinal materials, part of the heat can be carried away. The system may increase the feeding speed by 15 - 25%.

[0074] Adjust the pressure in the crushing chamber to optimize heat distribution. The adjustment of pressure can affect the distribution and transfer of heat in the crushing chamber. The system may adjust the pressure by 5 - 15% according to the specific situation.

[0075] The adjustments in these three aspects interact with each other and jointly act on temperature control. For example, reducing the rotation speed will reduce heat generation but may affect the crushing efficiency; increasing the feeding speed can enhance heat dissipation but may affect the crushing uniformity; adjusting the pressure can optimize heat distribution but may affect the particle size distribution. Therefore, a multi - objective optimization algorithm is needed to balance these factors.

[0076] The multi - objective optimization algorithm will comprehensively consider the temperature control strategy, the current optimization weights (such as the relative importance of particle size distribution, active ingredient retention rate, and energy consumption), and other relevant parameters, and calculate the optimal adjustment values of the rotation speed of the crushing main unit, the pressure in the crushing chamber, and the feeding speed. This optimization process may use methods such as genetic algorithms, particle swarm optimization, or simulated annealing to find the best balance point among multiple objectives.

[0077] Through this method of dynamic temperature control and parameter optimization, the technical solution of this application can respond to temperature changes in real - time during the crushing process and effectively protect heat - sensitive ingredients. Compared with traditional static parameter adjustment, this method can control the crushing process more precisely, significantly improving the active ingredient retention rate. At the same time, because the parameter adjustment is based on real - time data and multi - objective optimization, it can also optimize energy consumption while ensuring the crushing quality, improving the overall processing efficiency.

[0078] As a preferred implementation mode, the technical solution of this application can be applied to the crushing and processing of Astragalus membranaceus. Astragalus membranaceus is a commonly used traditional Chinese medicine, and its active ingredients are sensitive to temperature. In this embodiment, first, obtain the heat - sensitivity value of the active ingredients of Astragalus membranaceus from the traditional Chinese medicine property database, assumed to be 0.85 (on a 0 - 1 standardized scale).

[0079] According to this heat - sensitivity value, the system sets the temperature change threshold to 1.8 °C per minute and the temperature monitoring time interval to 8 seconds. During the crushing process, the system obtains temperature data every 8 seconds through a high - precision temperature sensor installed in the crushing chamber. Suppose at a certain time point, the system detects that the temperature rises from 25 °C to 26.1 °C within 16 seconds, and the calculated temperature change rate is 4.05 °C per minute, exceeding the preset threshold.

[0080] At this time, the system immediately triggers the temperature control strategy: Reduce the rotational speed of the crushing main machine from the original 3000 rpm to 2550 rpm, with a reduction amplitude of 15%.

[0081] Increase the feeding speed from the original 50 kg / h to 60 kg / h, with an increase amplitude of 20%.

[0082] Adjust the pressure in the crushing chamber from the original 0.8 MPa to 0.74 MPa, with a reduction amplitude of 7.5%.

[0083] At the same time, the system inputs these parameter changes into a pre-trained multi-objective optimization algorithm. This algorithm takes into account the current optimization weights (assumed to be: particle size distribution 40%, active ingredient retention rate 40%, energy consumption 20%), as well as other characteristic parameters of astragalus (such as hardness, fiber structure, etc.).

[0084] After calculation, output the final optimal adjustment values: rotational speed of the crushing main machine: 2600 rpm, feeding speed: 58 kg / h, pressure in the crushing chamber: 0.76 MPa.

[0085] The system then immediately applies these optimal adjustment values to the crushing equipment. Through this real-time and dynamic adjustment, the temperature during the crushing process is effectively controlled, which not only ensures a high retention rate of the active ingredients of astragalus, but also maintains an ideal crushing effect and energy efficiency.

[0086] This dynamic adjustment method based on real-time temperature monitoring and multi-objective optimization can better adapt to the characteristic differences of different batches of astragalus and the temperature changes during the crushing process compared with the traditional static parameter setting. It not only improves the quality and efficiency of astragalus crushing and processing, but also reduces the need for manual intervention, lowers the operation difficulty and human error.

[0087] In some of the above embodiments of the present application, it is proposed to obtain the optimization weight parameters input by the operator to adjust the relative importance of the particle size distribution, active ingredient retention rate, and energy consumption in the multi-objective optimization process. However, in this process, directly inputting the optimization weight parameters by the operator may be subjective and uncertain, and it is difficult to ensure the scientificity and rationality of the parameter setting. In addition, the operator may lack sufficient professional knowledge to accurately judge the best optimization weights required for different types and batches of traditional Chinese medicines, which may lead to unstable or unsatisfactory effects during the crushing process.

[0088] In response to this, the present application further proposes to retrieve the corresponding recommended optimization weight parameters from a preset traditional Chinese medicine characteristic database according to the type information and batch information of the current traditional Chinese medicine. The recommended optimization weight parameters include the particle size distribution weight, active ingredient retention rate weight, and energy consumption weight; display the recommended optimization weight parameters to the operator; receive the adjustment input of the operator to the recommended optimization weight parameters; and generate the final optimization weight parameters according to the adjustment input.

[0089] The technical solution of this application provides scientific and reasonable parameter suggestions for operators by introducing a preset traditional Chinese medicine property database and recommended optimization weight parameters. This method combines the professional knowledge in the database and the actual experience of operators, effectively solving the subjectivity and uncertainty problems that may be brought by direct input from operators.

[0090] Specifically, first, the recommended parameters are retrieved from the database according to the type and batch information of the current traditional Chinese medicine, which ensures the scientificity and pertinence of the initial parameter setting. The recommended parameters are displayed to the operator so that they can understand the optimal settings recommended by the system. The adjusted input from the operator is received, allowing for fine-tuning according to the actual situation, which reflects the advantage of the combination of man and machine. Finally, the final parameters are generated based on the adjustment, realizing the intelligence and personalization of the optimization weight parameter setting.

[0091] This method not only improves the accuracy and efficiency of parameter setting but also reduces the dependence on the professional knowledge of operators. By combining database knowledge and manual experience, this solution can better adapt to the characteristic differences of different types and batches of traditional Chinese medicine, thereby improving the stability and effect of the crushing process. In addition, this method also has good scalability and learning ability. As the database is continuously updated and improved, the accuracy of the recommended parameters will be further improved.

[0092] The technical solution of this application can be implemented in various ways. For example, the preset traditional Chinese medicine property database can use a relational database or a non-relational database to store and manage data. The database can contain historical crushing parameters, optimization weight parameters, and corresponding crushing effect data for different types and batches of traditional Chinese medicine.

[0093] The generation of the recommended optimization weight parameters can be based on various algorithms, such as machine learning algorithms, statistical analysis methods, or expert systems. These algorithms can consider multiple factors, such as the physical properties, chemical components, and seasonal changes of traditional Chinese medicine, to generate more accurate recommended parameters.

[0094] Displaying the recommended optimization weight parameters to the operator can be achieved through a graphical user interface (GUI). The interface can use intuitive charts or numerical display methods so that the operator can quickly understand the meaning and impact of the recommended parameters.

[0095] Receiving the adjusted input from the operator can be achieved through various interaction methods, such as sliders, numerical input boxes, or preset options. The system can set a reasonable adjustment range to prevent the operator from entering extreme or unreasonable values.

[0096] When generating the final optimized weight parameters according to the adjusted input, the system can adopt an intelligent algorithm to balance the adjustments of the operator and the recommended parameters. For example, a weight factor can be set to determine the influence degree of the operator's adjustment on the final parameters according to the operator's experience level.

[0097] For example, for a specific traditional Chinese medicine, such as Astragalus membranaceus, the optimized weight parameters recommended by the system may be: particle size distribution weight 0.4, active ingredient retention rate weight 0.4, energy consumption weight 0.2. These recommended values are generated based on the characteristics of Astragalus membranaceus and historical comminution data. The operator can fine-tune these weights according to the actual situation of the current batch, such as the origin and harvesting time of Astragalus membranaceus. Suppose the operator adjusts the particle size distribution weight to 0.45, the active ingredient retention rate weight to 0.35, and the energy consumption weight remains unchanged. The system will generate the final optimized weight parameters according to these adjustments.

[0098] The advantage of this method is that it not only utilizes the professional knowledge accumulated in the database but also allows the operator to make adjustments according to the actual situation. This flexibility enables the comminution process to better adapt to the characteristic changes of different batches of traditional Chinese medicine, thereby improving the consistency and stability of the comminution quality.

[0099] At the same time, this method also provides an opportunity for the operator to learn and improve. By comparing the recommended parameters and the actual comminution effect, the operator can gradually accumulate experience and improve the understanding of the comminution characteristics of different traditional Chinese medicines. This way of human-machine collaboration not only improves production efficiency but also promotes the improvement of the operator's professional skills.

[0100] In practical applications, the technical solution of this application can be implemented as follows: First, establish a database containing the characteristic data of various traditional Chinese medicines. Taking Astragalus membranaceus as an example, the database may contain the following information: variety (such as Astragalus membranaceus Fisch. var. mongholicus (Bge.) Hsiao, Astragalus membranaceus (Fisch.) Bunge, etc.), origin, harvesting season, medicinal material year, and historical comminution data (including the optimized weight parameters used and the corresponding comminution effects).

[0101] When a new batch of Astragalus membranaceus needs to be comminuted, the system first reads its variety and batch information. Suppose this batch of Astragalus membranaceus is from Gansu and is harvested in autumn. The system will retrieve the historical data under similar conditions from the database and generate the recommended optimized weight parameters.

[0102] For example, the system may recommend the following parameters: particle size distribution weight: 0.45, active ingredient retention rate weight: 0.40, energy consumption weight: 0.15.

[0103] These recommended parameters will be displayed to the operator through the operation interface. The interface may be in the form of a pie chart or a bar chart, visually showing the proportion of each weight. At the same time, the interface may also display the confidence level of these recommended parameters and the basis for generating these recommendations (such as based on how many historical records).

[0104] After the operator views the recommended parameters, they can adjust them according to the actual situation of the current batch of astragalus. Suppose the operator notices that the moisture content of this batch of astragalus is slightly higher and more energy may be required to reach the target particle size, so they decide to slightly increase the energy consumption weight. The operator can adjust it by using a slider or directly entering a value: particle size distribution weight: 0.43 (-0.02), effective ingredient retention rate weight: 0.38 (-0.02), energy consumption weight: 0.19 (+0.04).

[0105] After the system receives these adjustments, it will perform a rationality check to ensure that the adjusted parameters are still within the allowable range. Then, the system will generate the final optimized weight parameters based on these adjustments. In this process, the system may apply some intelligent algorithms, such as considering the operator's experience level, to determine the deviation degree of the final parameters from the recommended parameters and the adjustment input.

[0106] The finally generated optimized weight parameters can be used in the subsequent multi-objective optimization process to guide the parameter adjustment of the crushing equipment to achieve the best crushing effect.

[0107] In this way, the technical solution of this application effectively combines data-driven intelligent recommendations and flexible adjustments based on human experience, greatly improving the scientificity and adaptability of the optimized weight parameter setting. This not only improves the efficiency and quality of the crushing process, but also reduces material waste and energy consumption caused by improper parameter settings. At the same time, this method also provides strong support for operators with different experience levels, helping to improve the overall operation level and production stability.

[0108] In some of the above embodiments of this application, it is proposed to compare the optimal adjustment value with the preset safety parameter range to ensure that the adjusted parameters do not exceed the safe operation range to ensure the safety of the crushing process. However, in this process, it may occur that the optimal adjustment value exceeds the safety parameter range, which may lead to unsafe operation of the crushing equipment or poor crushing effect. In addition, the fixed safety parameter range may not be able to adapt to the characteristic changes of different types and batches of traditional Chinese medicine, and a more flexible and intelligent method is needed to handle this situation.

[0109] In response to this, the present application further proposes to obtain the corresponding safety parameter range from a preset traditional Chinese medicine safety parameter database according to the type information and batch information of the traditional Chinese medicine. The safety parameter range includes the range of the rotation speed of the crushing main machine, the range of the pressure in the crushing chamber, and the range of the feeding speed. Compare the optimal adjustment values of the calculated rotation speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed with the corresponding safety parameter ranges respectively. When any optimal adjustment value exceeds the corresponding safety parameter range, perform the following steps: Set the adjustment value that exceeds the range to the boundary value of the safety parameter range close to the original adjustment value; Based on the corrected adjustment value, recalculate the adjustment values of other parameters to maintain the balance between the crushing effect and safety; Update the safety parameter range in the traditional Chinese medicine safety parameter database and record the current adjustment situation.

[0110] The technical solution of the present application solves the above problems through key features such as dynamic safety parameter acquisition, real-time comparison mechanism, intelligent parameter correction, global parameter recalculation, and database dynamic update.

[0111] Dynamic safety parameter acquisition is an important feature of the present application. Specifically, the system obtains the corresponding safety parameter range from a pre-established traditional Chinese medicine safety parameter database according to the type and batch information of the traditional Chinese medicine being processed. These safety parameter ranges include the range of the rotation speed of the crushing main machine, the range of the pressure in the crushing chamber, and the range of the feeding speed. This method ensures the pertinence and adaptability of the safety parameters and can better adapt to the characteristic changes of different types and batches of traditional Chinese medicine.

[0112] The real-time comparison mechanism is another key feature of the present application. The system will compare the optimal adjustment values calculated by the multi-objective optimization algorithm with the safety parameter ranges obtained from the database in real time. This step can timely detect potential safety hazards and prevent the crushing equipment from operating under unsafe parameters.

[0113] When it is found that the optimal adjustment value exceeds the safety range, the present application adopts an intelligent parameter correction strategy. Specifically, the system will set the adjustment value that exceeds the range to the boundary value of the safety parameter range close to the original adjustment value. This method not only ensures the safe operation of the equipment but also maintains the original optimization effect as much as possible. For example, if the calculated optimal rotation speed of the crushing main machine is 3000 rpm and the upper limit of the safety parameter range is 2800 rpm, the system will adjust the actual rotation speed used to 2800 rpm.

[0114] After correcting the parameters that exceed the range, the present application also introduces a step of global parameter recalculation. The system will recalculate the adjustment values of other parameters based on the corrected adjustment value. The purpose of this step is to maintain the balance between the overall crushing effect and safety. For example, if the rotation speed of the crushing main machine is reduced, the system may correspondingly adjust the feeding speed or the pressure in the crushing chamber to maintain the original crushing effect as much as possible.

[0115] Finally, the present application also includes the feature of dynamic database update. The system will record each adjustment and update the safety parameter range in the traditional Chinese medicine safety parameter database. This mechanism enables the system to continuously learn and adapt to the characteristic changes of different traditional Chinese medicines. For example, if a certain traditional Chinese medicine requires parameters close to the upper limit of the safety range during multiple pulverization processes, the system may appropriately expand the safety parameter range of this traditional Chinese medicine to improve the efficiency of future pulverization processes.

[0116] This method not only solves the problem that the optimal adjustment value may exceed the safety range, but also improves the system's adaptability to the characteristics of different traditional Chinese medicines through the dynamic adjustment and learning mechanism. While ensuring the safe operation of the equipment, it maintains the optimization of the pulverization effect as much as possible, achieving a balance between safety and efficiency. In addition, by continuously updating the safety parameter database, the system can continuously accumulate experience, improve the processing ability for different types and batches of traditional Chinese medicines, and thus enhance the overall intelligence level and adaptability of the pulverization process.

[0117] The technical solution of the present application can have various implementation manners in practical applications. For example, when obtaining the safety parameter range, a multi-level query method can be adopted. First, query the general parameter range according to the type information of the traditional Chinese medicine, and then make a refined adjustment according to the batch information. This method can improve the query efficiency and ensure the accuracy of the parameters at the same time.

[0118] When performing parameter comparison, a buffer can be set. For example, if the optimal adjustment value is close to but does not exceed the safety range, the system can issue a warning to remind the operator to pay attention to observing the pulverization process. This method can make the best use of the equipment performance while ensuring safety.

[0119] For parameter correction, a progressive adjustment method can be adopted. For example, when the parameter needs to be adjusted to the safety range, the system can make multiple small adjustments instead of a large one-time adjustment. This method can reduce the sudden interference to the pulverization process and maintain the stability of the pulverization effect.

[0120] When recalculating other parameters, a machine learning algorithm can be introduced. The system can learn the mutual influence relationship between different parameters based on historical data, so as to more accurately predict and adjust other parameters after one parameter is corrected.

[0121] For database update, a weighted average method can be adopted. The new adjustment records can be weighted averaged with the historical data to smooth the influence of short-term fluctuations and obtain a more stable long-term trend.

[0122] Dynamic security parameter acquisition provides the basis for real-time comparison; the results of real-time comparison directly affect the triggering of intelligent parameter correction; parameter correction will trigger global parameter recalculation; and the results of the entire process will eventually be fed back to the system through dynamic database updates, forming a closed-loop optimization process.

[0123] As a preferred embodiment, the technical solution of the present application can be applied to an intelligent Chinese medicine pulverization system. The system includes a pulverization device, a control unit and a database. The pulverization device is equipped with sensors for real-time monitoring of particle size distribution, temperature, humidity and pressure. The control unit is responsible for executing algorithms for parameter calculation, comparison and adjustment. The database stores the safety parameter ranges and historical pulverization records of various Chinese medicines.

[0124] In actual operation, the operator first inputs the type and batch information of the Chinese medicine to be processed. The system then retrieves the corresponding safety parameter range from the database. For example, for a batch of astragalus, the system may obtain the following safety parameter range: the speed range of the crushing main machine is 1500-2800rpm, the pressure range in the crushing chamber is 0.05-0.15MPa, and the feed rate range is 50-100kg / h.

[0125] After the crushing process begins, the system continuously monitors the real-time particle size distribution and process parameters. Assume that at a certain moment, the optimal adjustment value calculated by the multi-objective optimization algorithm is: crushing host speed 2900rpm, crushing chamber pressure 0.14MPa, feed rate 90kg / h. The system immediately compares and finds that the crushing host speed exceeds the safe range.

[0126] The system then starts the intelligent parameter correction process. It adjusts the main crushing machine speed to the upper limit of the safety range of 2800rpm, and then recalculates other parameters. After calculation, the system may adjust the pressure in the crushing chamber to 0.15MPa and the feed rate to 95kg / h to maintain the original crushing effect as much as possible.

[0127] The result of this adjustment will be recorded and used to update the safety parameter range in the database. If similar adjustments occur multiple times during the crushing process of this batch of astragalus, the system may consider slightly increasing the safety upper limit of the crushing host speed, for example, to 2850rpm.

[0128] In this way, the technical solution of the present application can continuously optimize the crushing parameters and improve the crushing efficiency and quality under the premise of ensuring safety. It solves the problems of frequent shutdown for sampling, untimely parameter adjustment, and difficulty in adapting to the characteristics of different batches of medicinal materials in traditional methods, and greatly improves the automation and intelligence level of Chinese medicine crushing processing.

[0129] In some of the above embodiments of the present application, a temperature change threshold and a temperature monitoring time interval are set according to the thermosensitivity value of the active ingredient to control the temperature change during the pulverization process. However, in this process, only considering the thermosensitivity of the active ingredient may not comprehensively reflect the characteristics of traditional Chinese medicine and the influence of environmental factors on temperature control. For example, environmental humidity and the fiber structure of traditional Chinese medicine also affect heat conduction and distribution, thereby affecting the rate and pattern of temperature change. This may lead to inaccurate temperature control strategies, unable to meet the requirements of different traditional Chinese medicines and environmental conditions, thus affecting the pulverization effect and the retention rate of active ingredients.

[0130] In response to this, the present application further proposes to obtain the current environmental humidity data and the fiber structure data in the traditional Chinese medicine characteristic parameters; based on the thermosensitivity value of the active ingredient, the environmental humidity data and the fiber structure data, calculate the dynamic temperature change threshold through a heat conduction model; determine the correction coefficient of the temperature monitoring time interval according to the fiber structure data; perform weighted calculation on the dynamic temperature change threshold and the preset basic temperature change threshold to obtain the final temperature change threshold; and adjust the preset basic temperature monitoring time interval based on the correction coefficient to obtain the final temperature monitoring time interval.

[0131] This technical solution calculates the dynamic temperature change threshold by introducing environmental humidity and traditional Chinese medicine fiber structure data and combining with the thermosensitivity of the active ingredient using a heat conduction model. This method considers multiple influencing factors and can more accurately predict and control the temperature change during the pulverization process. At the same time, the solution also introduces a correction coefficient for the temperature monitoring time interval to adjust the monitoring frequency according to the fiber structure characteristics of traditional Chinese medicine. This dynamic adjustment mechanism can optimize the monitoring frequency while ensuring the monitoring accuracy, improving the system efficiency.

[0132] By performing weighted calculation on the dynamically calculated threshold and the preset basic threshold, and using the correction coefficient to adjust the basic monitoring time interval, the refined adjustment of temperature control parameters is achieved. This method not only retains the preset values based on experience but also introduces dynamic adjustments based on real-time data, thus maintaining good temperature control effects under different conditions.

[0133] The specific implementation of this technical solution may include the following aspects: First, the environmental humidity data can be collected in real time by a humidity sensor installed around the pulverization equipment. The fiber structure data of traditional Chinese medicine can be obtained from a pre-established traditional Chinese medicine database or by rapid detection methods such as near-infrared spectroscopy analysis.

[0134] The selection of the heat conduction model can be based on the physical properties of traditional Chinese medicinal materials. For example, for traditional Chinese medicinal materials with strong porosity, a porous medium heat conduction model can be adopted; for traditional Chinese medicinal materials with complex fiber structures, a heterogeneous material heat conduction model can be used. The input parameters of the model include the numerical value of the thermal sensitivity of the active ingredient, environmental humidity data, and fiber structure data, and the output is the dynamic temperature change threshold.

[0135] Specifically, the porous medium heat conduction model can use two sets of energy equations to describe the heat transfer processes of the solid phase and the fluid phase respectively: Fluid phase energy equation: ε (ρCp)_f ∂T_f / ∂t + (ρCp)_f **u** · ∇T_f = ∇ · (k_f,eff ∇T_f) +h_sf a_sf (T_s - T_f) ε: Porosity, representing the proportion of the fluid in the total volume; (ρCp)_f: Volume heat capacity of the fluid (J / m³·K); T_f: Fluid temperature (K); t: Time (s); **u**: Fluid velocity vector (m / s); ∇: Gradient operator; k_f,eff: Effective thermal conductivity of the fluid phase (W / m·K); h_sf: Solid-fluid interface heat transfer coefficient (W / m²·K); a_sf: Interface area per unit volume (m² / m³); T_s: Solid phase temperature (K).

[0136] Solid phase energy equation: (1-ε) (ρCp)_s ∂T_s / ∂t = ∇ · (k_s,eff ∇T_s) + h_sf a_sf (T_f - T_s)+ Q_gen (1-ε): Solid volume fraction; (ρCp)_s: Volume heat capacity of the solid (J / m³·K); k_s,eff: Effective thermal conductivity of the solid phase (W / m·K); Q_gen: Volume heat source inside the solid phase (W / m³), such as heat generated by friction.

[0137] Heterogeneous material heat conduction model The heterogeneous material heat conduction model takes into account the spatial variation of material physical property parameters and is more suitable for describing traditional Chinese medicinal materials with complex fiber structures, and can be expressed as: ρ(r) Cp(r) ∂T(r,t) / ∂t = ∇ · (k(r) ∇T(r,t)) + Q_gen(r,t) r = (x, y, z): Spatial position coordinates; ρ(r): Position-dependent density (kg / m³); Cp(r): Position-dependent specific heat capacity (J / kg·K); T(r,t): Temperature distribution related to space and time (K); k(r): Position-dependent thermal conductivity (W / m·K); Q_gen(r,t): Volume heat source related to position and time (W / m³).

[0138] In addition, two models can be combined to obtain a hybrid model: ρ_eff(r,F,H) C_p,eff(r,F,H) ∂T(r,t) / ∂t = ∇ · (k_eff(r,F,H) ∇T(r,t)) + Q_gen(r,v,p,F,t) Where: ρ_eff(r,F,H): Effective density considering fiber structure and humidity; C_p,eff(r,F,H): Effective specific heat capacity considering fiber structure and humidity; k_eff(r,F,H): Effective thermal conductivity considering fiber structure and humidity; Q_gen(r,v,p,F,t): Heat source term considering comminution parameters and fiber structure.

[0139] The correction factor for the temperature monitoring time interval can be obtained by fitting experimental data. For example, comminution experiments can be carried out on traditional Chinese medicinal materials with different fiber structures, the temperature change curves can be recorded, and then the relationship between the fiber structure and the optimal monitoring interval can be determined through data analysis, so as to obtain the calculation formula of the correction factor.

[0140] The weighted calculation of the dynamic temperature change threshold and the preset basic threshold can adopt an adaptive weight method. In the initial stage, more reliance can be placed on the preset basic threshold, and as the comminution process progresses, the weight of the dynamic temperature change threshold can be gradually increased. This method can optimize the temperature control strategy step by step while ensuring safety.

[0141] In addition, the determination of the final temperature monitoring time interval can also be combined with the response characteristics of the equipment and the computing power of the control system. For example, if the temperature of the equipment changes rapidly or the computing power of the control system is strong, a shorter monitoring interval can be selected; otherwise, the interval can be appropriately extended to balance the control accuracy and system load.

[0142] Environmental humidity data and fiber structure data directly affect the calculation results of the heat conduction model, and thus affect the determination of the dynamic temperature change threshold. At the same time, the fiber structure data also determines the correction coefficient of the temperature monitoring time interval. The comprehensive consideration of these multiple factors enables the temperature control strategy to better adapt to different Chinese herbal medicines and environmental conditions.

[0143] In addition, the weighted calculation of the dynamic temperature change threshold and the base threshold, as well as the adjustment of the monitoring time interval based on the correction coefficient, form a closed-loop adaptive control system. This system can continuously optimize the control parameters according to real-time data, so as to maximize the protection of the active ingredients of Chinese herbal medicines while ensuring the crushing effect.

[0144] The implementation process of this technical solution can be described as follows: First, obtain the current environmental humidity data through a humidity sensor, for example, the relative humidity is 60%. At the same time, retrieve the fiber structure data of the current Chinese herbal medicine (such as Astragalus membranaceus) being processed from the Chinese medicine property database, including information such as fiber length, diameter, and distribution.

[0145] Next, input these data and the previously obtained thermal sensitivity value of the active ingredient (for example, the thermal sensitivity index of the main active ingredient of Astragalus membranaceus is 0.8) into a pre-established heat conduction model. This model takes into account the porous structure and inhomogeneous characteristics of Chinese herbal medicines and obtains the dynamic temperature change threshold through numerical calculation, for example, 2.5 °C per minute.

[0146] Then, calculate the correction coefficient of the temperature monitoring time interval according to the fiber structure data of Astragalus membranaceus. Assume that the correction coefficient calculation formula obtained through experiments is: correction coefficient = 1 + 0.1 * (fiber length / average fiber length) - 0.05 * (fiber diameter / average fiber diameter). For the current batch of Astragalus membranaceus, the calculated correction coefficient is 1.2.

[0147] Perform a weighted calculation on the calculated dynamic temperature change threshold (2.5 °C / minute) and the preset base temperature change threshold (assumed to be 2 °C / minute). Use an adaptive weight method, giving the base threshold a weight of 0.7 and the dynamic threshold a weight of 0.3 in the initial stage. Therefore, the final temperature change threshold is: 2 * 0.7 + 2.5 * 0.3 = 2.15 °C / minute.

[0148] Finally, adjust the preset base temperature monitoring time interval (assumed to be 30 seconds) based on the correction coefficient (1.2). The adjusted final temperature monitoring time interval is: 30 seconds / 1.2 ≈ 25 seconds.

[0149] Through this method, the pulverizing equipment can more precisely control the temperature change, monitor the temperature every 25 seconds, and ensure that the temperature change rate does not exceed 2.15 °C / minute. This can not only effectively protect the thermosensitive components of Astragalus membranaceus, but also make real-time adjustments according to the environmental humidity and the characteristics of Astragalus membranaceus itself, improving the adaptability and stability of the pulverizing process.

[0150] This technical solution realizes more precise temperature control by comprehensively considering multiple factors such as environmental humidity and the fiber structure of traditional Chinese medicine. Compared with the method that only considers the thermosensitivity of active ingredients, the new solution can better adapt to different types and batches of traditional Chinese medicine, as well as changing environmental conditions. This refined temperature control not only improves the retention rate of active ingredients, but also ensures the quality stability of the pulverized products. At the same time, by dynamically adjusting the monitoring time interval, the use of system resources is optimized, and the overall pulverizing efficiency is improved. In addition, the adaptive characteristics of this solution enable it to be flexibly applied in different production scenarios, providing important technical support for the standardization and intelligentization of traditional Chinese medicine pulverizing processes.

[0151] In some of the above embodiments of the present application, it is proposed to retrieve the corresponding traditional Chinese medicine characteristic parameters from a preset traditional Chinese medicine characteristic database according to the type information and batch information to obtain the characteristic information of the medicinal materials. However, in this process, there may be a problem that the parameters in the database do not match the actual situation. This may lead to inaccurate adjustment of subsequent pulverizing parameters, affecting the pulverizing effect and the retention rate of active ingredients. Therefore, a method for correcting and updating traditional Chinese medicine characteristic parameters is needed to ensure the accuracy and timeliness of the data.

[0152] In response to this, the present application further proposes that the steps of retrieving the corresponding traditional Chinese medicine characteristic parameters from a preset traditional Chinese medicine characteristic database that is part of the preset traditional Chinese medicine characteristic model according to the type information and batch information include: sending a query request containing the type information and batch information to the traditional Chinese medicine characteristic database according to the query request; receiving a query result including the hardness, fiber structure, water content, and thermosensitivity of active ingredients of the medicinal materials; comparing the query result with a preset parameter range; when any parameter in the query result exceeds the preset parameter range, triggering a parameter correction process, including: obtaining the actual pulverizing data of the current batch of traditional Chinese medicine; based on the actual pulverizing data, performing a correction calculation on the parameter that exceeds the preset parameter range; updating the corrected parameter to the traditional Chinese medicine characteristic database; and using the finally determined traditional Chinese medicine characteristic parameters for subsequent pulverizing parameter adjustment.

[0153] The technical solution proposed in this application ensures the accuracy and real-time nature of traditional Chinese medicine characteristic parameters through multiple steps. First, by sending a query request containing variety and batch information to the database, key characteristic parameters such as the hardness, fiber structure, water content, and thermal sensitivity of active ingredients of the medicinal materials are obtained. These parameters are crucial for the subsequent pulverization process as they directly affect the pulverization effect and the retention of active ingredients.

[0154] Next, the query results are compared with the preset parameter ranges. This step can quickly identify parameters that may be abnormal. The preset parameter ranges can be set based on historical data and expert experience, providing a reasonable variation range for each characteristic parameter of traditional Chinese medicine.

[0155] When abnormal parameters are found, a calibration process is triggered. This includes obtaining actual pulverization data, performing correction calculations on the abnormal parameters based on this data, and then updating the database. The actual pulverization data may include information such as the real-time particle size distribution, temperature change, and energy consumption during the pulverization process. Various algorithms can be used for the correction calculations, such as the least squares method, neural networks, etc., to find the parameter values that best match the actual situation.

[0156] Updating the database is a key step, which ensures that the information in the database always remains up-to-date and accurate. This dynamic update mechanism enables the system to continuously learn and adapt to the characteristic changes of different batches of traditional Chinese medicine.

[0157] Finally, the finally determined parameters are used for subsequent adjustment of the pulverization parameters to ensure that the pulverization process is based on the most accurate traditional Chinese medicine characteristic information. This not only improves the accuracy and efficiency of the pulverization process but also helps to maintain a high-quality pulverization effect and active ingredient retention rate.

[0158] In the implementation process of the technical solution of this application, there can be various specific implementation methods. For example, when sending a query request to the database, an encrypted communication protocol can be used to ensure the security of data transmission. A timeout mechanism can be set for receiving the query results. If no response is received within the predetermined time, a backup data source or an alarm mechanism is triggered.

[0159] The parameter range comparison can adopt multi-level thresholds instead of simple binary judgments. For example, warning thresholds and error thresholds can be set. When a parameter exceeds the warning threshold but does not reach the error threshold, the system will issue a warning but continue to use the parameter; only when the parameter exceeds the error threshold will a complete calibration process be triggered.

[0160] In the parameter calibration process, the methods for obtaining actual pulverization data can be diversified. In addition to directly collecting data from the pulverization process of the current batch, historical data can also be combined for comprehensive analysis. Iterative optimization algorithms can be used for the correction calculations to gradually approach the optimal solution through multiple calculations.

[0161] Database updates can adopt an incremental update strategy, only updating the parameters that have changed to improve the update efficiency. At the same time, the historical versions of the parameters can be retained to facilitate tracing and analyzing the parameter change trends.

[0162] The technical solution of this application can be implemented as follows in practical applications: Suppose a batch of newly arrived astragalus needs to be pulverized. First, the operator inputs the type information of astragalus (such as "astragalus") and batch information (such as "20240601 - A"). The system then sends a query request to the traditional Chinese medicine property database.

[0163] The query results returned by the database may be as follows: Medicinal material hardness: 7.5 (Mohs hardness), fiber structure: medium fiber content (30%), water content: 12%, heat sensitivity of active ingredients: medium (maximum tolerable temperature 65°C).

[0164] The system compares these results with the preset parameter ranges. Suppose the preset water content range of astragalus is 8% - 15%, then 12% is within the range and no correction is needed. However, if the preset range of medicinal material hardness is 6.0 - 7.0, then 7.5 is outside the range and the correction process needs to be triggered.

[0165] In the correction process, the system will obtain the actual pulverization data of the current batch of astragalus. For example, under standard pulverization conditions (such as rotation speed 3000 rpm, pressure 0.5 MPa, feeding speed 50 kg / h), the measured actual particle size distribution may show that 80% of the particles are in the range of 50 - 200 μm, rather than the expected 90%. Based on this data, the system may correct the medicinal material hardness to 7.8.

[0166] The corrected parameters will be updated to the database, and the time and basis of the correction will be recorded. In this way, the next time the same batch or astragalus with similar characteristics is processed, more accurate parameters can be used.

[0167] Finally, the system uses the corrected parameters to adjust the working parameters of the pulverization equipment. For example, it may slightly increase the rotation speed of the pulverization main machine or reduce the feeding speed to cope with the higher medicinal material hardness and ensure the target particle size distribution is achieved.

[0168] In this way, the technical solution of this application can dynamically adapt to the characteristic changes of different batches of traditional Chinese medicines, improving the accuracy and efficiency of the pulverization process. It not only solves the problem that the parameters in the database may not match the actual situation, but also establishes an adaptive parameter optimization mechanism, enabling the entire pulverization system to continuously learn and improve, thereby continuously improving the pulverization quality and the retention rate of active ingredients.

[0169] In addition, in some specific embodiments, the present application proposes that the traditional Chinese medicine characteristic model at least includes a dynamic particle size distribution prediction model and a multi-objective optimization control model. The steps of generating a grinding parameter adjustment scheme based on the deviation and the preset traditional Chinese medicine characteristic model include: Based on the real-time particle size distribution information and process parameter information, use the dynamic particle size distribution prediction model to predict the particle size distribution at the next moment. The dynamic particle size distribution prediction model uses the Gaussian distribution function as the basis, and introduces the time-varying average particle size μ(t), standard deviation σ(t) and weight function W(t) to achieve an accurate description of the dynamic changes in the particle size distribution. This model considers the effects of multiple process parameters such as rotational speed, pressure, feed rate, temperature and humidity on the particle size distribution.

[0170] Specifically, the mathematical expression of the dynamic particle size distribution prediction model is: P(d,t) = A * exp(-((d-μ(t))^2) / (2*σ(t)^2)) * W(t); Among them, the calculation formulas of μ(t), σ(t) and W(t) are respectively: μ(t) = k1*v(t) + k2*p(t) + k3*f(t) + k4*v(t)*p(t) + k5*dT(t) / dt; σ(t) = c1*v(t) + c2*p(t) + c3*f(t) + c4*H(t) + c5*∫T(t)dt; W(t) = 1 + w1*sin(2πt / τ) + w2*exp(-t / τ); In these formulas, the meanings of the parameters are as follows: P(d,t) is the probability density of the particle size d at time t; d is the particle diameter; t is the time; A is the normalization coefficient; μ(t) is the average particle size; σ(t) is the particle size standard deviation; W(t) is the time-varying weight function; v(t) is the rotational speed of the grinding host; p(t) is the pressure in the grinding chamber; f(t) is the feed rate; T(t) is the working temperature; H(t) is the relative humidity; τ is the characteristic time constant; k1-k5, c1-c5, w1-w2 are model coefficients.

[0171] This dynamic particle size distribution prediction model can more accurately predict the particle size distribution at the next moment by considering the effects of multiple process parameters. For example, changes in the rotational speed v(t) and pressure p(t) will directly affect the average particle size μ(t), while changes in the temperature T(t) and humidity H(t) will affect the particle size standard deviation σ(t). The introduction of the time-varying weight function W(t) further improves the adaptability of the model to the dynamic characteristics of the grinding process.

[0172] According to the predicted particle size distribution and current process parameters, this application uses a multi-objective optimization control model to calculate the optimal crushing parameter adjustment plan. The multi-objective optimization control model simultaneously considers three key indicators: the proportion Q of particles within the target particle size range, the active ingredient retention rate R(t), and the energy consumption E.

[0173] The mathematical expression of the multi-objective optimization control model is: Objective function: J = min[λ1*(1 - Q) + λ2*(1 - R(t)) + λ3*E]; Among them, Q = ∫P(d,t)dd (50μm ≤ d ≤ 200μm); R(t) = R0 * exp(-α*T(t)) * exp(-β*H(t)); E = η1*v(t)^2 + η2*p(t) + η3*f(t); Constraint conditions: 50μm ≤ μ(t) ≤ 200μm; R(t) ≥ 95%; T(t) ≤ Tmax; v(t) ≤ vmax; p(t) ≤ pmax; In this model, J is the objective function, Q is the proportion of particles within the target particle size range, R(t) is the active ingredient retention rate, and E is the energy consumption function. R0 is the initial retention rate, α is the temperature influence coefficient, and β is the humidity influence coefficient. λ1 - λ3 are optimization weight coefficients used to adjust the relative importance of the three objectives during the optimization process. η1 - η3 are energy consumption coefficients, respectively representing the influence of rotational speed, pressure, and feed rate on energy consumption. Tmax, vmax, and pmax are the maximum allowable temperature, maximum allowable rotational speed, and maximum allowable pressure, respectively.

[0174] This multi-objective optimization control model can flexibly balance the relative importance of the three objectives of particle size control, active ingredient retention, and energy consumption optimization by adjusting the optimization weight coefficients λ1 - λ3. For example, when λ1 is larger, the model pays more attention to particle size control; when λ2 is larger, it pays more attention to the retention of active ingredients; when λ3 is larger, it pays more attention to energy consumption optimization.

[0175] By combining the dynamic particle size distribution prediction model and the multi-objective optimization control model, the technical solution of this application can, on the basis of predicting the future particle size distribution, comprehensively consider multiple objectives such as particle size control, active ingredient retention, and energy consumption optimization, and calculate the optimal crushing parameter adjustment plan. This method can not only achieve precise control of the crushing process but also optimize production efficiency and energy utilization while ensuring product quality.

[0176] Specifically, the technical solution of this application first uses a dynamic particle size distribution prediction model to predict the particle size distribution at the next moment based on the current real-time particle size distribution information and process parameter information. This step takes into account the dynamic characteristics of the comminution process and can more accurately reflect the changing trend of the comminution state.

[0177] Then, the predicted particle size distribution information and the current process parameters are input into the multi-objective optimization control model. This model will calculate the optimal comminution parameter adjustment plan according to the set objective function and constraint conditions. In this process, the model will simultaneously consider three key indicators: the proportion of particles within the target particle size range, the retention rate of active ingredients, and energy consumption, and balance them according to the set optimization weights.

[0178] For example, if the current predicted particle size distribution result shows that the particles are too large, the model may suggest increasing the rotation speed or pressure to improve the comminution effect. However, at the same time, the model will also consider the impact of these adjustments on the retention rate of active ingredients and energy consumption. If increasing the rotation speed may cause the temperature to rise, thereby affecting the retention rate of active ingredients, the model may suggest appropriately increasing the feed rate to balance the temperature increase.

[0179] Through this method of dynamic prediction and multi-objective optimization, the technical solution of this application can achieve continuous and precise control of the comminution process, avoiding the problem of frequent shutdown for sampling in traditional methods. At the same time, due to considering multiple objectives and constraint conditions, this method can maximize the retention rate of active ingredients and optimize energy consumption while ensuring the particle size requirements.

[0180] As a preferred implementation method, different optimization weights can be set in actual applications to adapt to different production requirements. For example, for traditional Chinese medicinal materials with high requirements for the retention rate of active ingredients, the value of λ2 can be increased; for production lines sensitive to energy consumption, the value of λ3 can be appropriately increased. This flexible adjustment mechanism enables the technical solution of this application to adapt to different production scenarios and the characteristics of traditional Chinese medicinal materials.

[0181] In a specific embodiment, assume that a batch of temperature-sensitive Astragalus membranaceus is being processed. First, according to the characteristics of Astragalus membranaceus, the initial parameters are set: the target particle size range is 50 - 200 μm, the target retention rate of active ingredients is 95%, the maximum allowable temperature Tmax is 60 °C, the maximum allowable rotation speed vmax is 3000 rpm, and the maximum allowable pressure pmax is 0.5 MPa.

[0182] The initial parameters of the dynamic particle size distribution prediction model may be as follows: k1 = 0.02, k2 = 0.01, k3 = -0.005, k4 = 0.0001, k5 = 0.1; c1 = 0.01, c2 = 0.005, c3 = 0.002, c4 = 0.5, c5 = 0.01; w1 = 0.1, w2 = 0.05, τ = 300s; The initial parameters of the multi-objective optimization control model may be as follows: λ1 = 0.4, λ2 = 0.4, λ3 = 0.2 (Considering the temperature sensitivity of Astragalus membranaceus, a higher weight is given to the retention rate of active ingredients); R0 = 1, α = 0.01, β = 0.005; η1 = 0.0001, η2 = 0.01, η3 = 0.005; During the crushing process, the system continuously monitors the real-time particle size distribution and process parameters. Suppose at a certain moment t, the following data are monitored: v(t) = 2500rpm, p(t) = 0.3MPa, f(t) = 50kg / h, T(t) = 55°C, H(t) = 40%; The system first uses the dynamic particle size distribution prediction model to predict the particle size distribution at the next moment t+Δt. Then, the prediction results and the current process parameters are input into the multi-objective optimization control model. The following adjustment suggestions may be obtained after the model calculation: Slightly reduce the rotational speed v(t) to 2400rpm to control the temperature; Keep the pressure p(t) unchanged at 0.3MPa; Slightly increase the feed rate f(t) to 55kg / h to increase the heat dissipation effect; These adjustment suggestions not only consider maintaining the target particle size range but also take into account the retention rate of active ingredients and energy consumption optimization. The system will automatically execute these adjustments and repeat this process in the next monitoring cycle to achieve continuous optimization control of the crushing process.

[0183] By adopting this method of dynamic prediction and multi-objective optimization, the technical solution of this application can achieve precise control of the crushing process, overcoming the limitations of traditional methods that only rely on current state information for adjustment. At the same time, through multi-objective optimization, this method can optimize production efficiency and energy utilization while ensuring product quality, providing an efficient, precise, and intelligent solution for traditional Chinese medicine crushing and processing.

[0184] In the second aspect, referring to Figure 2 , this application further proposes a drug crushing device, including: A target setting module 210 for setting a target crushing state, where the target crushing state includes a target particle size distribution range and a target active ingredient retention rate; A data acquisition module 220 for obtaining real-time particle size distribution information and process parameter information during the Chinese medicine crushing process, where the process parameter information includes the temperature, humidity, and pressure in the crushing chamber; A calculation module 230 for calculating the deviation between the current crushing state and the target crushing state based on the real-time particle size distribution information, process parameter information, and target crushing state; A solution generation module 240 for generating a crushing parameter adjustment solution based on the deviation and a preset Chinese medicine characteristic model, where the crushing parameter adjustment solution includes adjustment values for the rotation speed of the main crushing machine, the pressure in the crushing chamber, and the feeding speed; A parameter adjustment module 250 for automatically adjusting the working parameters of the crushing equipment according to the crushing parameter adjustment solution.

[0185] Through the real-time monitoring and automatic adjustment mechanism, it is possible to respond promptly to the dynamic changes during the crushing process, improve the crushing efficiency, and ensure the crushing quality and active ingredient retention rate.

[0186] In addition, in some preferred embodiments, a drug crushing device proposed in the present application can execute any one of the steps in the above method.

[0187] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for crushing a drug, characterized in that, Comprising: Setting a target crushing state, where the target crushing state includes a target particle size distribution range and a target active ingredient retention rate; Obtaining real-time particle size distribution information and process parameter information during the Chinese medicine crushing process, where the process parameter information includes the temperature, humidity, and pressure in the crushing chamber; Calculating the deviation between the current crushing state and the target crushing state based on the real-time particle size distribution information, process parameter information, and target crushing state; Generating a crushing parameter adjustment plan based on the deviation and a preset Chinese medicine characteristic model that combines at least one characteristic parameter of the hardness, fiber structure, water content, and thermal sensitivity of active ingredients of Chinese medicine. The crushing parameter adjustment plan includes adjustment values for the rotational speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed; Automatically adjusting the working parameters of the crushing equipment according to the crushing parameter adjustment plan.

2. The method for crushing a drug according to claim 1, wherein The step of generating a crushing parameter adjustment plan based on the deviation and a preset Chinese medicine characteristic model that combines at least one characteristic parameter of the hardness, fiber structure, water content, and thermal sensitivity of active ingredients of Chinese medicine includes: Obtaining the variety information and batch information of the current Chinese medicine; According to the variety information and batch information, retrieving the corresponding Chinese medicine characteristic parameters from a preset Chinese medicine characteristic database that is part of the preset Chinese medicine characteristic model. The characteristic parameters include the hardness of the medicinal material, fiber structure, water content, and thermal sensitivity of active ingredients; Calculating the optimal adjustment values for the rotational speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed based on the Chinese medicine characteristic parameters and the deviation; Taking the optimal adjustment values as the adjustment values of the crushing parameter adjustment plan.

3. The method for pulverizing a drug according to claim 2, wherein, The step of calculating the optimal adjustment values for the rotational speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed based on the Chinese medicine characteristic parameters and the deviation includes: Obtaining the optimization weight parameters input by the operator, where the optimization weight parameters are used to adjust the relative importance of particle size distribution, active ingredient retention rate, and energy consumption in the multi-objective optimization process; Establishing a relationship model between the crushing parameters and the particle size distribution, active ingredient retention rate, and energy consumption according to the Chinese medicine characteristic parameters; Inputting the relationship model, the deviation, and the optimization weight parameters into a preset multi-objective optimization algorithm; Calculating the optimal adjustment values for the rotational speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed under the current optimization weight through the multi-objective optimization algorithm; Comparing the optimal adjustment values with a preset safety parameter range to ensure that the adjusted parameters do not exceed the safe operating range.

4. A method for crushing a drug according to claim 3, characterized in that, The step of calculating the optimal adjustment values for the rotational speed of the crushing main machine, the pressure in the crushing chamber, and the feeding speed under the current optimization weight through the multi-objective optimization algorithm includes: Obtaining the thermal sensitivity value of the active ingredient in the Chinese medicine characteristic parameters; Setting a temperature change threshold and a temperature monitoring time interval according to the thermal sensitivity value of the active ingredient; Obtaining the temperature data in the crushing chamber within each temperature monitoring time interval and calculating the temperature change rate; When the temperature change rate exceeds the temperature change threshold, triggering a temperature control strategy, including: Reducing the rotational speed of the crushing main machine to reduce heat generation by friction; Increasing the feeding speed to enhance heat dissipation; Adjust the pressure in the crushing chamber to optimize the heat distribution; According to the multi-objective optimization algorithm and the temperature control strategy, calculate the optimal adjustment values of the main crusher speed, the pressure in the crushing chamber, and the feeding speed under the current optimization weight.

5. A method for crushing a drug according to claim 3, characterized in that, The step of obtaining the optimization weight parameters input by the operator includes: According to the current type information and batch information of traditional Chinese medicine, retrieve the corresponding recommended optimization weight parameters from a preset traditional Chinese medicine characteristic database. The recommended optimization weight parameters include particle size distribution weight, active ingredient retention rate weight, and energy consumption weight; Display the recommended optimization weight parameters to the operator; Receive the adjustment input of the operator to the recommended optimization weight parameters; Generate the final optimization weight parameters according to the adjustment input.

6. A method for crushing a drug according to claim 3, characterized in that, The step of comparing the optimal adjustment values with a preset safety parameter range to ensure that the adjusted parameters do not exceed the safe operating range includes: According to the type information and batch information of the traditional Chinese medicine, obtain the corresponding safety parameter range from a preset traditional Chinese medicine safety parameter database. The safety parameter range includes the main crusher speed range, the pressure range in the crushing chamber, and the feeding speed range; Compare the calculated optimal adjustment values of the main crusher speed, the pressure in the crushing chamber, and the feeding speed with the corresponding safety parameter ranges respectively; When any optimal adjustment value exceeds the corresponding safety parameter range, perform the following steps: Set the adjustment value that exceeds the range to the boundary value of the safety parameter range close to the original adjustment value; Based on the corrected adjustment value, recalculate the adjustment values of other parameters to maintain the balance between crushing effect and safety; Update the safety parameter range in the traditional Chinese medicine safety parameter database and record the current adjustment situation.

7. A method for crushing a drug according to claim 4, characterized in that, The step of setting the temperature change threshold and the temperature monitoring time interval according to the heat sensitivity value of the active ingredient includes: Obtain the current environmental humidity data and the fiber structure data in the traditional Chinese medicine characteristic parameters; Based on the heat sensitivity value of the active ingredient, the environmental humidity data, and the fiber structure data, calculate the dynamic temperature change threshold through a heat conduction model; Determine the correction coefficient of the temperature monitoring time interval according to the fiber structure data; Perform a weighted calculation on the dynamic temperature change threshold and a preset basic temperature change threshold to obtain the final temperature change threshold; Adjust the preset basic temperature monitoring time interval based on the correction coefficient to obtain the final temperature monitoring time interval.

8. A method for crushing a drug according to claim 2, characterized in that, The step of retrieving the corresponding traditional Chinese medicine characteristic parameters from a preset traditional Chinese medicine characteristic database that is part of the preset traditional Chinese medicine characteristic model according to the type information and batch information includes: Send a query request containing type information and batch information to the traditional Chinese medicine characteristic database; According to the query request, receive the query results including the hardness of the medicinal material, the fiber structure, the water content, and the heat sensitivity of the active ingredient; Compare the query results with a preset parameter range; When any parameter in the query results exceeds the preset parameter range, trigger a parameter correction process, including: Obtain the actual crushing data of the current batch of traditional Chinese medicine; Based on the actual crushing data, perform a correction calculation on the parameter that exceeds the preset parameter range. Update the corrected parameters to the traditional Chinese medicine property database; Use the finally determined traditional Chinese medicine property parameters for subsequent adjustment of the crushing parameters.

9. A method for crushing a drug according to claim 1, characterized in that The traditional Chinese medicine property model at least includes a dynamic particle size distribution prediction model and a multi-objective optimization control model. The step of generating a crushing parameter adjustment plan based on the deviation and the preset traditional Chinese medicine property model includes: Based on the real-time particle size distribution information and process parameter information, use the dynamic particle size distribution prediction model to predict the particle size distribution at the next moment, where: The dynamic particle size distribution prediction model is: P(d,t) = A * exp(-((d-μ(t))^2) / (2*σ(t)^2)) * W(t); μ(t) = k1*v(t) + k2*p(t) + k3*f(t) + k4*v(t)*p(t) + k5*dT(t) / dt; σ(t) = c1*v(t) + c2*p(t) + c3*f(t) + c4*H(t) + c5*∫T(t)dt; W(t) = 1 + w1*sin(2πt / τ) + w2*exp(-t / τ); Where, P(d,t) is the probability density of the particle size d at time t, d is the particle diameter, t is the time, A is the normalization coefficient, μ(t) is the average particle size, σ(t) is the particle size standard deviation, W(t) is the time-varying weight function, v(t) is the rotational speed of the crushing main machine, p(t) is the pressure in the crushing chamber, f(t) is the feeding speed, T(t) is the working temperature, H(t) is the relative humidity, τ is the characteristic time constant, k1-k5, c1-c5, w1-w2 are model coefficients; According to the predicted particle size distribution and the current process parameters, use the multi-objective optimization control model to evaluate the deviation and thus calculate the optimal crushing parameter adjustment plan, where: The multi-objective optimization control model is: Objective function: J = min[λ1*(1-Q) + λ2*(1-R(t) ) + λ3*E]; Q = ∫P(d,t)dd (50μm ≤ d ≤ 200μm); R(t) = R0 * exp(-α*T(t)) * exp(-β*H(t)); E = η1*v(t)^2 + η2*p(t) + η3*f(t); Constraints: 50μm ≤ μ(t) ≤ 200μm; R(t) ≥ 95%; T(t) ≤ Tmax; v(t) ≤ vmax; p(t) ≤ pmax; Where, J is the objective function, Q is the proportion of particles in the target particle size range, R(t) is the effective ingredient retention rate, E is the energy consumption function, R0 is the initial retention rate, α is the temperature influence coefficient, β is the humidity influence coefficient, λ1-λ3 are optimization weight coefficients, η1-η3 are energy consumption coefficients, Tmax is the maximum allowable temperature, vmax is the maximum allowable rotational speed, pmax is the maximum allowable pressure; The calculated optimal crushing parameter adjustment plan is used as the crushing parameter adjustment plan for automatically adjusting the working parameters of the crushing equipment.

10. A drug crushing device, characterized in that, It includes: A target setting module for setting a target crushing state, where the target crushing state includes a target particle size distribution range and a target effective ingredient retention rate; A data acquisition module for obtaining real-time particle size distribution information and process parameter information during the Chinese medicine crushing process, where the process parameter information includes the temperature, humidity, and pressure in the crushing chamber; A calculation module for calculating the deviation between the current crushing state and the target crushing state based on the real-time particle size distribution information, process parameter information, and target crushing state; A plan generation module for generating a crushing parameter adjustment plan based on the deviation and a preset Chinese medicine characteristic model that combines at least one characteristic parameter of the hardness, fiber structure, water content, and effective ingredient thermosensitivity of the Chinese medicine. The crushing parameter adjustment plan includes adjustment values for the rotation speed of the main crushing machine, the pressure in the crushing chamber, and the feeding speed; A parameter adjustment module for automatically adjusting the working parameters of the crushing equipment according to the crushing parameter adjustment plan.

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