Simulation method and system for separation and extraction of effective components of plant allelopathy

Abstract

The invention provides a simulation method and system for separation and extraction of effective components of plant allelopathy. The method comprises the following steps: determining concentration distribution of various effective components in plant allelochemicals in each simulation stage; determining a dynamic dependency relationship among the separation condition parameters according to the concentration distribution of each effective component in each simulation stage and the separation efficiency coefficient in each simulation stage; performing constraint analysis on the separation state of each effective component between the adjacent simulation stages to obtain the constraint control quantity of each effective component in the separation state of the adjacent simulation stages; determining the simulation fitness of the concentration distribution of each effective component in each simulation stage through the dynamic dependency relationship and the constraint control quantity of the separation state of each adjacent simulation stage; and carrying out dynamic simulation on the extraction purity of various effective components based on all simulation fitness. By adopting the scheme of the invention, the extraction purity of the effective components can be dynamically simulated under the influence condition of the non-uniform diffusion effect.

Description

Technical Field

[0001] This application relates to the technical field of simulation modeling, and more specifically, to a simulation method and system for the separation and extraction of active components of allelopathy in plants. Background Art

[0002] Simulation modeling reproduces the behavior of real systems through computer simulation and is widely used in fields such as engineering, chemistry, and physics. Its basic steps are to abstract the process of the actual system into a mathematical model, which usually includes variables, equations, and constraints, representing various phenomena of the system. By using numerical methods or computer software, these equations are solved to simulate the dynamic behavior and response of the system. Simulation modeling can be used to predict the performance of the system under different conditions, helping with analysis, optimization, and decision-making. For example, in chemical engineering, simulation modeling can analyze mass transfer, thermodynamic equilibrium, etc. in a reactor and optimize operating parameters. The advantage of this technology is that it can be tested without actually operating the system, saving costs, reducing risks, and providing optimization solutions. Therefore, simulation modeling provides a powerful analysis tool for various industries, promoting technological progress and innovation.

[0003] In the process of simulation modeling for the separation and extraction of allelochemicals in plants, the diffusion behavior of solvents is a key factor affecting the separation efficiency of allelochemicals in plants and the final concentration distribution. However, due to the significant non-uniformity of the microstructure of plant tissues, such as differences in cell wall permeability, uneven distribution of intercellular spaces, and the selective adsorption effect of solvents on allelochemicals in plants, the diffusion effect of solvents during the separation process exhibits highly non-uniform characteristics. In simulation modeling, this non-uniform diffusion effect will cause the local solute migration rate to be unstable, affecting the concentration evolution of active components, and further affecting the extraction purity of active components, making it difficult for traditional uniform diffusion models to accurately predict the separation and extraction process. Therefore, how to dynamically simulate the extraction purity of active components under the influence of non-uniform diffusion effects has become a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides a simulation method and system for the separation and extraction of active components of allelopathy in plants, which can dynamically simulate the extraction purity of active components under the influence of non-uniform diffusion effects.

[0005] In a first aspect, this application provides a simulation method for the separation and extraction of active components of allelopathy in plants, including the following steps: Obtain the separation and extraction data of allelochemicals in plants, and then determine the concentration distribution of various active components in the allelochemicals at each simulation stage through the separation and extraction data; Determine the dynamic dependence relationship between the separation condition parameters in the separation and extraction simulation process of the allelochemicals according to the concentration distribution of each active ingredient in each simulation stage and the separation efficiency coefficient of the allelochemicals in each simulation stage; Conduct a constraint analysis on the separation state of each active ingredient between adjacent simulation stages to obtain the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages; Determine the simulation fitness of the concentration distribution of each active ingredient in each simulation stage through the dynamic dependence relationship between the separation condition parameters in the separation and extraction simulation process of the allelochemicals and the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages; Perform dynamic simulation on the extraction purity of various active ingredients based on all the simulation fitnesses.

[0006] In some embodiments, determining the concentration distribution of various active ingredients in the allelochemicals in each simulation stage through the separation and extraction data specifically includes: Select an active ingredient in the allelochemicals as the selected active ingredient; Perform fitting and correction on the various characteristic data of the selected active ingredient in the separation and extraction data to obtain the various fitting characteristic data of the selected active ingredient; Determine the concentration distribution of the selected active ingredient in the allelochemicals in each simulation stage according to the dissolution change characteristics and diffusion change characteristics of the various fitting characteristic data of the selected active ingredient; Continue to determine the concentration distribution of the remaining active ingredients in the allelochemicals in each simulation stage.

[0007] In some embodiments, determining the dynamic dependence relationship between the separation condition parameters in the separation and extraction simulation process of the allelochemicals according to the concentration distribution of each active ingredient in each simulation stage and the separation efficiency coefficient of the allelochemicals in each simulation stage specifically includes: Determine the separation efficiency coefficient of the allelochemicals in each simulation stage; Conduct correlation analysis on the concentration distribution of each active ingredient in each simulation stage through all the separation efficiency coefficients to obtain the correlation response coefficient of the concentration distribution of each active ingredient in each simulation stage; Determine the dynamic dependence relationship between the separation condition parameters in the separation and extraction simulation process of the allelochemicals according to all the correlation response coefficients.

[0008] In some embodiments, conducting a constraint analysis on the separation state of each active ingredient between adjacent simulation stages to obtain the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages specifically includes: Determine the diffusion transfer relationship of each active ingredient between adjacent simulation stages through the change matrix of the separation state of each active ingredient at each simulation stage; Perform boundary constraints on the diffusion transfer relationship of each active ingredient between adjacent simulation stages to obtain the confidence boundaries of the diffusion of each active ingredient between adjacent simulation stages; Determine the constraint control quantity of the separation state of each active ingredient at each adjacent simulation stage according to the confidence boundaries of the diffusion of each active ingredient between adjacent simulation stages.

[0009] In some embodiments, determining the simulation fitness of the concentration distribution of each active ingredient at each simulation stage through the dynamic dependence relationship between each separation condition parameter in the separation and extraction simulation process of the allelochemical and the constraint control quantity of the separation state of each active ingredient at each adjacent simulation stage specifically includes: Determine the simulation control quantity of the concentration distribution of each active ingredient at each simulation stage according to the constraint control quantity of the separation state of each active ingredient at each adjacent simulation stage; Determine the simulation fitness of the concentration distribution of each active ingredient at each simulation stage through the dynamic dependence relationship between each separation condition parameter in the separation and extraction simulation process of the allelochemical and the simulation control quantity of the concentration distribution of each active ingredient at each simulation stage.

[0010] In some embodiments, dynamically simulating the extraction purity of various active ingredients based on all the simulation fitnesses specifically includes: Determine the dynamic simulation conditions of the extraction purity of each active ingredient according to all the simulation fitnesses; Simulate the extraction purity of various active ingredients through the dynamic simulation conditions of the extraction purity of each active ingredient.

[0011] In some embodiments, the types of the active ingredients include alkaloids, flavonoids, terpenoids, phenols, sterols, quinones, and organic acids.

[0012] In a second aspect, the present application provides a separation and extraction simulation system for allelopathic active ingredients of plants, including: An acquisition module, configured to acquire the separation and extraction data of allelochemicals, and further determine the concentration distribution of various active ingredients in the allelochemicals at each simulation stage through the separation and extraction data; A processing module, configured to determine the dynamic dependence relationship between each separation condition parameter in the separation and extraction simulation process of the allelochemical according to the concentration distribution of each active ingredient at each simulation stage and the separation efficiency coefficient of the allelochemical at each simulation stage; The processing module is further configured to perform constraint analysis on the separation states of each active ingredient between adjacent simulation stages, and obtain the constraint control quantities of the separation states of each active ingredient between adjacent simulation stages; The processing module is further configured to determine the simulation fitness of the concentration distribution of each active ingredient in each simulation stage through the dynamic dependence relationship between the separation condition parameters in the separation and extraction simulation process of the allelochemical and the constraint control quantities of the separation states of each active ingredient between adjacent simulation stages; The execution module is configured to perform dynamic simulation on the extraction purity of various active ingredients based on all the simulation fitnesses.

[0013] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned separation and extraction simulation method for the active ingredients of allelopathy.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned separation and extraction simulation method for the active ingredients of allelopathy is implemented.

[0015] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects: In the separation and extraction simulation method and system for the active ingredients of allelopathy provided by the present application, by obtaining the separation and extraction data of the allelochemical, and then determining the concentration distribution of various active ingredients in the allelochemical at each simulation stage through the separation and extraction data; determining the dynamic dependence relationship between the separation condition parameters in the separation and extraction simulation process of the allelochemical according to the concentration distribution of each active ingredient at each simulation stage and the separation efficiency coefficient of the allelochemical at each simulation stage; performing constraint analysis on the separation states of each active ingredient between adjacent simulation stages, and obtaining the constraint control quantities of the separation states of each active ingredient between adjacent simulation stages; determining the simulation fitness of the concentration distribution of each active ingredient in each simulation stage through the dynamic dependence relationship between the separation condition parameters in the separation and extraction simulation process of the allelochemical and the constraint control quantities of the separation states of each active ingredient between adjacent simulation stages; performing dynamic simulation on the extraction purity of various active ingredients based on all the simulation fitnesses.

[0016] It can be seen that in this application, the simulation fitness of the concentration distribution of each active ingredient in each simulation stage can be determined through the dynamic dependence relationship between the separation condition parameters in the simulation process of separating and extracting the allelochemicals and the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage. Among them, first, the concentration distribution of the active ingredient in each simulation stage can reflect the actual state where the migration rate of the active ingredient is different in different regions due to the hindrance of the microscopic structure of the plant tissue to the diffusion of the solvent. Second, determine the dynamic dependence relationship between the separation condition parameters in the simulation process of separating and extracting the allelochemicals. The dynamic dependence relationship represents the parameter values that affect each other over time among the separation condition parameters within the simulation stage. Through the dynamic dependence relationship, the coupling effect between the concentration distribution of each active ingredient in each simulation stage and each separation condition parameter can be reflected, so that the simulation result is more in line with the actual diffusion characteristics. Furthermore, conduct a constraint analysis on the separation state of each active ingredient between adjacent simulation stages to obtain the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage. The constraint analysis helps to accurately regulate the extraction process under the influence of the non-uniform diffusion effect and ensure that the dynamic simulation can truly reflect the migration and enrichment characteristics of the active ingredient. The constraint control quantity represents the parameter that controls the change of the separation state of the active ingredient in adjacent simulation stages. Through the constraint control quantity, the significant difference in the separation state of the active ingredient in different simulation stages can be defined, preventing unreasonable mutations or deviations from occurring in the simulation process, so that the simulation result is more in line with the actual situation of non-uniform diffusion. Then, determine the simulation fitness of the concentration distribution of each active ingredient in each simulation stage. The simulation fitness can accurately evaluate the influence of the non-uniform diffusion effect on the extraction process, thereby measuring whether the concentration distribution conforms to the actual diffusion law and dynamically adjusting the simulation parameters to make the simulation more in line with the actual extraction process. Finally, based on all the simulation fitnesses, conduct a dynamic simulation on the extraction purity of various active ingredients. In summary, the solution of this application can realize the dynamic simulation of the extraction purity of active ingredients under the influence of the non-uniform diffusion effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flowchart of a method for simulating the separation and extraction of active ingredients of allelopathy according to some embodiments of the present application; Figure 2 is a schematic flowchart for determining the dynamic dependence relationship between each separation condition parameter according to some embodiments of the present application; Figure 3 is a schematic flowchart for determining the constraint control quantity according to some embodiments of the present application; Figure 4It is a schematic structural diagram of a simulation system for the separation and extraction of allelopathic active ingredients shown in some embodiments of the present application; Figure 5 It is a schematic structural diagram of a computer device for implementing a simulation method for the separation and extraction of allelopathic active ingredients shown in some embodiments of the present application. Specific embodiments

[0018] To better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the specification drawings and specific embodiments.

[0019] Reference Figure 1 , which is an exemplary flowchart of a simulation method for the separation and extraction of allelopathic active ingredients shown in some embodiments of the present application. The simulation method 100 for the separation and extraction of allelopathic active ingredients mainly includes the following steps: In step 101, separation and extraction data of allelopathic substances are obtained, and then the concentration distribution of various active ingredients in the allelopathic substances at each simulation stage is determined through the separation and extraction data.

[0020] It should be noted that there are various active ingredients in the allelopathic substances in the present application. Among them, the types of active ingredients include alkaloids, flavonoids, terpenoids, phenolic compounds, sterol compounds, quinone compounds, and organic acids, etc.

[0021] It should be noted that the separation and extraction data in the present application represents a set composed of characteristic data of various active ingredients in allelopathic substances at different separation and extraction stages. Among them, the separation and extraction stages sequentially include a solvent extraction stage, a component diffusion stage, a separation and purification stage, and a concentration and drying stage, etc. Among them, there is a sequential order between different stages, and there are characteristic data of active ingredients in each separation and extraction stage. The characteristic data represents an ordered data composed of the concentration, solubility, and diffusion rate of active ingredients at different time points in the separation and extraction stage. Among them, the concentration, solubility, and diffusion rate of active ingredients are measured once every minute. In addition, each separation and extraction stage corresponds to a simulation stage and the sequential order of the simulation stage is the same as that of the separation and extraction stage.

[0022] In some embodiments, the determination of the concentration distribution of various active ingredients in the allelopathic substances at each simulation stage through the separation and extraction data can be achieved by the following steps: Select an active ingredient in the allelopathic substances as the selected active ingredient; Perform fitting correction on each characteristic data of the selected active ingredient in the separation and extraction data to obtain each fitting characteristic data of the selected active ingredient; Determine the concentration distribution of the selected active ingredients in each simulation stage within the allelochemicals according to the dissolution change characteristics and diffusion change characteristics of each fitting characteristic data of the selected active ingredients; Continue to determine the concentration distribution of the remaining active ingredients in each simulation stage within the allelochemicals.

[0023] When specifically implemented, fitting corrections are performed on each characteristic data of the selected active ingredients in the separation and extraction data. The following method can be used to obtain each fitting characteristic data of the selected active ingredients, that is: for each characteristic data of the selected active ingredients in the separation and extraction data, the existing linear fitting algorithm (such as the least squares support vector machine algorithm) is used to perform linear fitting on all concentrations, solubilities, and diffusion rates in the characteristic data respectively, so as to obtain multiple concentration fitting values, multiple solubility fitting values, and multiple diffusion rate fitting values. Then, each concentration fitting value, each solubility fitting value, and each diffusion rate fitting value are respectively used to replace the corresponding concentration, solubility, and diffusion rate. Thus, the ordered data composed of all the concentration fitting values, solubility fitting values, and diffusion rate fitting values obtained after replacement is used as the fitting characteristic data of the selected active ingredients, so as to obtain each fitting characteristic data of the selected active ingredients. In other embodiments, other methods can also be used to implement this, which will not be elaborated here.

[0024] It should be noted that the fitting characteristic data in this application represents the characteristic data of the active ingredients after fitting correction in the separation and extraction stage.

[0025] In addition, it should also be noted that each fitting characteristic data in this application corresponds to a separation and extraction stage, and each separation and extraction stage corresponds to a simulation stage, that is, each fitting characteristic data corresponds to a simulation stage; it should also be noted that the dissolution change characteristic in this application represents a parameter for measuring the change in the solubility of the active ingredient, and the diffusion change characteristic represents a parameter for measuring the change in the diffusion rate of the active ingredient.

[0026] In specific implementation, to determine the concentration distribution of the selected active ingredient in the allelochemical at each simulation stage according to the dissolution change characteristics and diffusion change characteristics of the fitting characteristic data of the selected active ingredient, the following method can be adopted, that is: select a fitting characteristic data from the fitting characteristic data of the selected active ingredient as the selected fitting characteristic data, take the variances of all solubility fitting values and all diffusion velocity fitting values in the selected fitting characteristic data as the dissolution change characteristic and diffusion change characteristic of the selected fitting characteristic data respectively, then take the quotient of the dissolution change characteristic and the diffusion change characteristic as the concentration distribution adjustment coefficient, then normalize all the concentration fitting values in the selected fitting characteristic data, multiply the normalized values by the concentration distribution adjustment coefficient, arrange all the multiplied values in chronological order, and take the arranged sequence as the concentration distribution of the selected fitting characteristic data of the selected active ingredient corresponding to the simulation stage. Continue to determine the concentration distribution of the remaining fitting characteristic data of the selected active ingredient corresponding to the simulation stage. In other embodiments, other methods can also be used to achieve this, which will not be elaborated here.

[0027] It should be noted that the concentration distribution described in this application represents the trend of the spatial concentration of the active ingredient changing with time during the simulation stage.

[0028] In step 102, according to the concentration distribution of each active ingredient at each simulation stage and the separation efficiency coefficient of the allelochemical at each simulation stage, determine the dynamic dependence relationship between the various separation condition parameters during the separation and extraction simulation process of the allelochemical.

[0029] In some embodiments, as shown in Figure 2 This figure is a schematic flow chart for determining the dynamic dependence relationship between the various separation condition parameters in some embodiments of this application. In this embodiment, to determine the dynamic dependence relationship between the various separation condition parameters during the separation and extraction simulation process of the allelochemical according to the concentration distribution of each active ingredient at each simulation stage and the separation efficiency coefficient of the allelochemical at each simulation stage, the following steps can be adopted: First, in step 1021, determine the separation efficiency coefficient of the allelochemical at each simulation stage; Secondly, in step 1022, perform correlation analysis on the concentration distribution of each active ingredient at each simulation stage through all the separation efficiency coefficients to obtain the correlation response coefficient of the concentration distribution of each active ingredient at each simulation stage; Then, in step 1023, determine the dynamic dependence relationship between the various separation condition parameters during the separation and extraction simulation process of the allelochemical according to all the correlation response coefficients.

[0030] It should be noted that the separation efficiency coefficient described in this application represents a parameter for measuring the separation efficiency of all active components of allelochemicals during the simulation stage. As a preferred embodiment, the separation efficiency coefficient of the allelochemicals in each simulation stage can be determined in the following manner: The mean value of the recovery rates of each active component of the allelochemicals in each separation and extraction stage can be used as the separation efficiency coefficient of the allelochemicals in the corresponding simulation stage.

[0031] When specifically implemented, the concentration distribution of each active component in each simulation stage is correlated and analyzed through all the separation efficiency coefficients. The correlation response coefficient of the concentration distribution of each active component in each simulation stage can be implemented in the following manner, that is: First, all the separation efficiency coefficients are arranged in sequence according to the order of the separation and extraction stages, and the obtained sequence is used as the separation efficiency coefficient sequence. Then, an active component is selected as the selected active component. Based on the existing sliding window technology, the separation efficiency coefficient sequence is set as the sliding window, the sliding step size is set to 1, and then a simulation stage is selected from all the simulation stages of the selected active component as the selected simulation stage. The sliding window is aligned with the first position of the concentration distribution of the selected active component in the selected simulation stage, and the cosine similarity between all the separation efficiency coefficients in the sliding window at the first position and all the values covered by the first position of the concentration distribution of the selected active component in the selected simulation stage is calculated, and the obtained cosine similarity is used as the correlation coefficient at the first position. The sliding window is moved sequentially, so as to sequentially determine the correlation coefficients of each position of the concentration distribution of the selected active component in the selected simulation stage until the tail of the sliding window is aligned with the last position of the concentration distribution of the selected active component in the selected simulation stage and stops moving. Further, the mean value of all the obtained correlation coefficients is calculated, and the obtained mean value is used as the correlation response coefficient of the concentration distribution of the selected active component in the selected simulation stage. Then, the correlation response coefficient of the concentration distribution of the selected active component in the remaining simulation stages is continuously determined, and further the correlation response coefficients of the concentration distributions of the remaining active components in each simulation stage are determined. In other embodiments, other methods can also be used for implementation, which are not limited here.

[0032] It should be noted that the correlation response coefficient described in this application represents a parameter for measuring the degree of correlation between each separation efficiency coefficient and the concentration distribution of the active component under the simulation stage.

[0033] It should also be noted that the separation condition parameters described in this application represent operation parameters that affect the separation and extraction effect by control and adjustment during the simulation stage, specifically including temperature value, stirring rate value, pH value, pressure value, solvent volume value, etc. In addition, the separation and extraction simulation process of the allelochemicals in the application includes multiple different simulation stages, and there are multiple separation condition parameters in each simulation stage.

[0034] In specific implementation, to determine the dynamic dependency relationship between the various separation condition parameters during the simulation process of separating and extracting the allelochemicals based on all the associated response coefficients, the following method can be adopted, that is: select a simulation stage during the simulation process of separating and extracting the allelochemicals as the selected simulation stage, sort the various separation condition parameters within the selected simulation stage in ascending order, and use the obtained sequence as the separation condition parameter sequence. Then, sum the associated response coefficients of the concentration distributions of various active ingredients within the selected simulation stage, and use the obtained sum value as the stage dependency value. Next, expand the number of the obtained stage dependency values to be the same as the number of separation condition parameters in the separation condition parameter sequence. Furthermore, form a sequence composed of all the stage dependency values as the stage dependency value sequence. Further calculate the cosine similarity between the stage dependency value sequence and the separation condition parameter sequence, and use the obtained cosine similarity as the dynamic dependency relationship between the various separation condition parameters within the selected simulation stage. Continue to determine the dynamic dependency relationship between the various separation condition parameters within the remaining simulation stages, so as to obtain the dynamic dependency relationship between the various separation condition parameters during the simulation process of separating and extracting the allelochemicals. In other embodiments, other methods can also be used to implement this, which will not be elaborated here.

[0035] It should be noted that the dynamic dependency relationship described in this application represents the parameter values that interact with each other over time between the various separation condition parameters of the allelochemicals within the simulation stage.

[0036] In step 103, perform a constraint analysis on the separation states of each active ingredient between adjacent simulation stages to obtain the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages.

[0037] In some embodiments, as Figure 3 shown, this figure is a schematic flowchart of determining the constraint control quantity in some embodiments of this application. In this embodiment, to perform a constraint analysis on the separation states of each active ingredient between adjacent simulation stages to obtain the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages, the following steps can be adopted: Determine the diffusion transfer relationship of each active ingredient between adjacent simulation stages through the change matrix of the separation state of each active ingredient at each simulation stage; Perform boundary constraints on the diffusion transfer relationship of each active ingredient between adjacent simulation stages to obtain the confidence boundary of the diffusion of each active ingredient between adjacent simulation stages; Determine the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages according to the confidence boundary of the diffusion of each active ingredient between adjacent simulation stages.

[0038] It should be noted that the change matrix described in this application represents the matrix of the separation state of the active ingredient changing dynamically with time in the simulation stage. As a preferred embodiment, the change matrix of the separation state of each active ingredient in each simulation stage can be determined in the following manner: select one active ingredient as the selected active ingredient, and then select one simulation stage from each simulation stage of the selected active ingredient as the selected simulation stage. Perform differential processing on all concentrations, all solubilities, and all diffusion rates within the corresponding characteristic data of the selected active ingredient in the selected simulation stage, so as to obtain a concentration difference sequence, a solubility difference sequence, and a diffusion rate difference sequence. Then, form a matrix with the concentration difference sequence, the solubility difference sequence, and the diffusion rate difference sequence, and use the obtained matrix as the change matrix of the separation state of the selected active ingredient in the selected simulation stage (for example: change matrix = [concentration difference sequence solubility difference sequence diffusion rate difference sequence]). Continue to determine the change matrix of the separation state of the selected active ingredient in the remaining simulation stages, and then determine the change matrix of the separation state of the remaining active ingredients in each simulation stage. In other embodiments, other methods can also be used to implement this, which will not be elaborated here.

[0039] When specifically implemented, the diffusion transfer relationship between each active ingredient in each adjacent simulation stage can be determined through the change matrix of the separation state of each active ingredient in each simulation stage in the following manner: select one active ingredient as the selected active ingredient, multiply the change matrix of the separation state of the selected active ingredient in the first simulation stage by the change matrix of the separation state of the selected active ingredient in the second simulation stage, then calculate the trace of the multiplied matrix, and use the obtained value of the trace as the diffusion transfer relationship between the selected active ingredient in the first simulation stage and the second simulation stage. Determine the diffusion transfer relationship between the selected active ingredient in the second simulation stage and the third simulation stage in turn, and continue to determine the diffusion transfer relationship between the selected active ingredient in the remaining adjacent simulation stages. For the last simulation stage, no processing is performed. Then, determine the diffusion transfer relationship between the remaining active ingredients in each adjacent simulation stage. In other embodiments, other methods can also be used to implement this, and no limitation is made here.

[0040] It should be noted that the diffusion transfer relationship in this application represents a parameter for measuring the degree of diffusion migration of the active ingredient in adjacent simulation stages.

[0041] In specific implementation, boundary constraints are imposed on the diffusion transfer relationship of each active ingredient between adjacent simulation stages. The confidence boundary of the diffusion of each active ingredient between adjacent simulation stages can be implemented in the following manner: Select an active ingredient as the selected active ingredient, calculate the mean of the diffusion transfer relationship of the selected active ingredient between adjacent simulation stages, and use the obtained mean as the diffusion boundary threshold. Then, select an adjacent simulation stage from the adjacent simulation stages of the selected active ingredient as the selected adjacent simulation stage, and use the minimum value between the diffusion transfer relationship between the selected adjacent simulation stages and the diffusion boundary threshold as the lower confidence boundary, and use the maximum value between the diffusion transfer relationship between the selected adjacent simulation stages and the diffusion boundary threshold as the upper confidence boundary. Thus, the range composed of the lower confidence boundary and the upper confidence boundary is used as the confidence boundary of the diffusion of the selected active ingredient between the selected adjacent simulation stages. Continue to determine the confidence boundary of the diffusion of the selected active ingredient between the remaining adjacent simulation stages, and then continue to determine the confidence boundary of the diffusion of the remaining active ingredients between adjacent simulation stages. In other embodiments, other methods may also be used for implementation, which are not limited herein.

[0042] It should be noted that the confidence boundary described in this application represents the range of the diffusion migration degree of the active ingredient between adjacent simulation stages.

[0043] In specific implementation, the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage can be determined according to the confidence boundary of the diffusion of each active ingredient between adjacent simulation stages in the following manner: For each active ingredient, subtract the lower confidence boundary of the confidence boundary of the diffusion of the active ingredient between adjacent simulation stages from the upper confidence boundary of the confidence boundary of the diffusion of the active ingredient between adjacent simulation stages, and use the obtained value as the boundary constraint value of the separation state of the active ingredient between adjacent simulation stages. Then, divide the diffusion transfer relationship of the active ingredient between adjacent simulation stages by the boundary constraint value, and use the obtained value as the constraint control quantity of the separation state of the active ingredient between adjacent simulation stages. Thus, the constraint control quantity of the separation state of the active ingredient in each adjacent simulation stage is obtained. In other embodiments, other methods may also be used for implementation, which will not be elaborated herein.

[0044] It should be noted that the constraint control quantity described in this application represents the parameter that controls the change of the separation state of the active ingredient in adjacent simulation stages, and reflects the degree of constraint on the diffusion behavior of the active ingredient between adjacent simulation stages.

[0045] In step 104, the simulation fitness of the concentration distribution of each active ingredient in each simulation stage is determined based on the dynamic dependence relationship between the separation condition parameters in the separation and extraction simulation process of the plant allelochemicals and the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage.

[0046] In some embodiments, the simulation fitness of the concentration distribution of each active ingredient at each simulation stage can be realized by the following steps according to the dynamic dependence between the separation condition parameters in the separation and extraction simulation process of the allelochemicals and the constraint control quantity of the separation state of each active ingredient at each adjacent simulation stage: Determine the simulation control quantity of the concentration distribution of each active ingredient at each simulation stage according to the constraint control quantity of the separation state of each active ingredient at each adjacent simulation stage; Determine the simulation fitness of the concentration distribution of each active ingredient at each simulation stage according to the dynamic dependence between the separation condition parameters in the separation and extraction simulation process of the allelochemicals and the simulation control quantity of the concentration distribution of each active ingredient at each simulation stage.

[0047] Specifically, when implemented, determining the simulation control quantity of the concentration distribution of each active ingredient at each simulation stage according to the constraint control quantity of the separation state of each active ingredient at each adjacent simulation stage can be realized in the following manner, that is: select an active ingredient as the selected active ingredient, then select a simulation stage from the simulation stages of the selected active ingredient as the selected simulation stage, calculate the mean value of the concentration distribution of the selected active ingredient at the selected simulation stage, then divide the obtained value by the sum of the constraint control quantities of the separation states of the selected active ingredient at each adjacent simulation stage, and use the obtained quotient as the simulation control quantity of the concentration distribution of the selected active ingredient at the selected simulation stage. Then continue to determine the simulation control quantity of the concentration distribution of the selected active ingredient at the remaining simulation stages, and further continue to determine the simulation control quantity of the concentration distribution of the remaining active ingredients at each simulation stage. In other embodiments, other methods can also be used for implementation, which is not limited here.

[0048] It should be noted that the simulation control quantity described in this application represents the control parameter for simulating the concentration distribution of the active ingredient under the influence of the non-uniform diffusion effect.

[0049] In specific implementation, the simulation fitness of the concentration distribution of each active ingredient at each simulation stage can be determined by the dynamic dependence relationship between the separation condition parameters in the simulation process of separating and extracting the allelochemicals and the simulation control quantity at each simulation stage of the concentration distribution of each active ingredient, which can be realized in the following way, that is: select an active ingredient as the selected active ingredient, calculate the simulation fitness of the selected active ingredient at each simulation stage as the negative exponential function with the natural logarithm e as the base, then multiply each value obtained after calculating the negative exponential function with the natural logarithm e as the base by the dynamic dependence relationship between the separation condition parameters, and take each value obtained by multiplication as the simulation fitness of the concentration distribution of the selected active ingredient at the corresponding simulation stage, so as to obtain the simulation fitness of the concentration distribution of the selected active ingredient at each simulation stage, and continue to determine the simulation fitness of the concentration distribution of the remaining active ingredients at each simulation stage. In other embodiments, other methods can also be used to implement this, which will not be elaborated here.

[0050] It should be noted that the simulation fitness described in this application represents an index for measuring the matching degree between the simulation result of the concentration distribution of the active ingredient in the simulation process and the true concentration distribution of the active ingredient.

[0051] In step 105, dynamic simulation is performed on the extraction purity of various active ingredients based on all the simulation fitness values.

[0052] In some embodiments, the dynamic simulation of the extraction purity of various active ingredients based on all the simulation fitness values can be realized by the following steps: Determine the dynamic simulation conditions for the extraction purity of each active ingredient according to all the simulation fitness values; Simulate the extraction purity of various active ingredients through the dynamic simulation conditions for the extraction purity of each active ingredient.

[0053] In specific implementation, determining the dynamic simulation conditions for the extraction purity of each active ingredient according to all the simulation fitness values can be realized in the following way, that is: select an active ingredient as the selected active ingredient, normalize all the solubilities in the characteristic data corresponding to each simulation stage of the selected active ingredient, then calculate the average value of all the values obtained after normalization, and take the obtained value as the solubility standard value. Further, normalize the simulation fitness of the selected active ingredient at each simulation stage, then divide each value obtained after normalization by the solubility standard value, and take all the values obtained by division as the dynamic simulation values. Further, sort all the dynamic simulation values in the order of the simulation stages, and take the sorted sequence as the dynamic simulation conditions for the extraction purity of the selected active ingredient. Continue to determine the dynamic simulation conditions for the extraction purity of the remaining active ingredients. In other embodiments, other methods can also be used to implement this, which is not limited here.

[0054] It should be noted that the dynamic simulation conditions described in this application represent a sequence of dynamic parameters for simulating the influence of non-uniform diffusion effects on the extraction purity of active ingredients.

[0055] When specifically implemented, the extraction purity of various active ingredients can be simulated through the dynamic simulation conditions of the extraction purity of each active ingredient, which can be achieved in the following manner: First, use existing chemical engineering simulation software (such as Aspen Plus), select appropriate process units in the chemical engineering simulation software, such as extraction towers, solvent extraction devices, or other related equipment, to establish a basic process simulation framework, thereby constructing a dynamic simulation model. Then, set the material information in the dynamic simulation model, such as the physical properties of active ingredients and solvents, such as solubility, distribution coefficient, boiling point, density, etc., and input data such as the initial concentration of active ingredients, the volume, temperature, and pressure of the solvent into the dynamic simulation model. Second, define the reaction and mass transfer processes. For example, in the solvent extraction process, define the distribution behavior of active ingredients and solvents, the relationship between solubility and concentration, and the mass transfer rate, etc., and then input the dynamic simulation conditions of each active ingredient as variables into the dynamic simulation model. Then, call the dynamic module in the chemical engineering simulation software to perform time-step simulation on the dynamic simulation model. After the time-step simulation ends, the extraction purity of each active ingredient is obtained. In other embodiments, other methods can also be used to achieve this, which will not be elaborated here.

[0056] In addition, on the other hand of this application, in some embodiments, this application provides a simulation system for separating and extracting active ingredients of allelopathy, referring to Figure 4 , this figure is a schematic structural diagram of a simulation system for separating and extracting active ingredients of allelopathy according to some embodiments of this application. The simulation system 400 for separating and extracting active ingredients of allelopathy includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described as follows: Acquisition module 401: In this application, the acquisition module 401 is mainly used to acquire the separation and extraction data of allelochemicals, and then determine the concentration distribution of various active ingredients in the allelochemicals at each simulation stage through the separation and extraction data; Processing module 402: In this application, the processing module 402 is used to determine the dynamic dependence relationship between the separation condition parameters during the separation and extraction simulation of the allelochemicals according to the concentration distribution of each active ingredient at each simulation stage and the separation efficiency coefficient of the allelochemicals at each simulation stage; It should be noted that the processing module 402 in this application is also used to perform constraint analysis on the separation states of each active ingredient between adjacent simulation stages, and obtain the constraint control quantity of the separation states of each active ingredient between adjacent simulation stages; In addition, the processing module 402 described in this application is further configured to determine the simulation fitness of the concentration distribution of each active ingredient in each simulation stage by means of the dynamic dependence relationship between the separation condition parameters during the simulation process of phytochemical allelochemical separation and extraction and the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage; An execution module 403. In this application, the execution module 403 is mainly configured to perform dynamic simulation on the extraction purity of various active ingredients based on all the simulation fitness values.

[0057] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned separation and extraction simulation method for the active ingredients of phytochemical allelopathy.

[0058] In some embodiments, referring to Figure 5 , this figure is a schematic structural diagram of a computer device for implementing the separation and extraction simulation method for the active ingredients of phytochemical allelopathy according to some embodiments of this application. The separation and extraction simulation method for the active ingredients of phytochemical allelopathy in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0059] The processor 501 can be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more for controlling the execution of the separation and extraction simulation method for the active ingredients of phytochemical allelopathy in this application.

[0060] The communication bus 502 can be used to transmit information between the above components.

[0061] The memory 503 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 can exist independently and be connected to the processor 501 through the communication bus 502. The memory 503 can also be integrated with the processor 501.

[0062] Among them, the memory 503 is used to store the program code for implementing the solution of this application and is controlled by the processor 501 for execution. The processor 501 is used to execute the program code stored in the memory 503. The program code can include one or more software modules. The methods described in the above method embodiments can be implemented by one or more software modules in the program code of the processor 501 and the memory 503.

[0063] The communication interface 504 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0064] In a specific implementation, as an embodiment, the computer device can include multiple processors, and each of these processors can be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0065] The above computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0066] In addition, the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the simulation method for separating and extracting the effective components of allelopathy is implemented as described above.

[0067] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A simulation method for separating and extracting the active ingredients of allelopathy of plants, characterized in that, It includes the following steps: Obtain the separation and extraction data of allelochemicals, and then determine the concentration distribution of various active ingredients in the allelochemicals at each simulation stage based on the separation and extraction data; Determine the dynamic dependence relationship between various separation condition parameters during the separation and extraction simulation of the allelochemicals according to the concentration distribution of each active ingredient at each simulation stage and the separation efficiency coefficient of the allelochemicals at each simulation stage; Conduct a constraint analysis on the separation state of each active ingredient between adjacent simulation stages to obtain the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages; Determine the simulation fitness of the concentration distribution of each active ingredient at each simulation stage through the dynamic dependence relationship between various separation condition parameters during the separation and extraction simulation of the allelochemicals and the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages; Dynamically simulate the extraction purity of various active ingredients based on all the simulation fitnesses.

2. The method according to claim 1, wherein Determining the concentration distribution of various active ingredients in the allelochemicals at each simulation stage based on the separation and extraction data specifically includes: Select one active ingredient in the allelochemicals as the selected active ingredient; Perform fitting correction on each characteristic data of the selected active ingredient in the separation and extraction data to obtain each fitting characteristic data of the selected active ingredient; Determine the concentration distribution of the selected active ingredient in the allelochemicals at each simulation stage according to the dissolution change characteristics and diffusion change characteristics of each fitting characteristic data of the selected active ingredient; Continue to determine the concentration distribution of the remaining active ingredients in the allelochemicals at each simulation stage.

3. The method according to claim 1, wherein Determining the dynamic dependence relationship between various separation condition parameters during the separation and extraction simulation of the allelochemicals according to the concentration distribution of each active ingredient at each simulation stage and the separation efficiency coefficient of the allelochemicals at each simulation stage specifically includes: Determine the separation efficiency coefficient of the allelochemicals at each simulation stage; Conduct a correlation analysis on the concentration distribution of each active ingredient at each simulation stage through all the separation efficiency coefficients to obtain the correlation response coefficient of the concentration distribution of each active ingredient at each simulation stage; Determine the dynamic dependence relationship between various separation condition parameters during the separation and extraction simulation of the allelochemicals according to all the correlation response coefficients.

4. The method according to claim 1, wherein Conduct a constraint analysis on the separation state of each active ingredient between adjacent simulation stages to obtain the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages specifically includes: Determine the diffusion transfer relationship of each active ingredient between adjacent simulation stages through the change matrix of the separation state of each active ingredient at each simulation stage; Conduct boundary constraints on the diffusion transfer relationship of each active ingredient between adjacent simulation stages to obtain the confidence boundary of the diffusion of each active ingredient between adjacent simulation stages; Determine the constraint control quantity of the separation state of each active ingredient between adjacent simulation stages according to the confidence boundary of the diffusion of each active ingredient between adjacent simulation stages.

5. The method according to claim 1, characterized in that Determining the simulation fitness of the concentration distribution of each active ingredient in each simulation stage through the dynamic dependence relationship between the separation condition parameters in the simulation process of plant allelochemical separation and extraction and the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage specifically includes: Determining the simulation control quantity of the concentration distribution of each active ingredient in each simulation stage according to the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage; Determining the simulation fitness of the concentration distribution of each active ingredient in each simulation stage through the dynamic dependence relationship between the separation condition parameters in the simulation process of plant allelochemical separation and extraction and the simulation control quantity of the concentration distribution of each active ingredient in each simulation stage.

6. The method according to claim 1, wherein Performing dynamic simulation on the extraction purity of various active ingredients based on all the simulation fitnesses, specifically including: Determining the dynamic simulation conditions for the extraction purity of each active ingredient according to all the simulation fitnesses; Simulating the extraction purity of various active ingredients through the dynamic simulation conditions for the extraction purity of each active ingredient.

7. The method according to claim 1, characterized in that The types of the active ingredients include alkaloids, flavonoids, terpenoids, phenols, sterols, quinones, and organic acids.

8. A simulation system for the separation and extraction of effective components of allelopathy in plants, characterized in that, Including: An acquisition module, configured to acquire the separation and extraction data of plant allelochemicals, and then determine the concentration distribution of various active ingredients in the plant allelochemicals in each simulation stage through the separation and extraction data; A processing module, configured to determine the dynamic dependence relationship between the separation condition parameters in the simulation process of plant allelochemical separation and extraction according to the concentration distribution of each active ingredient in each simulation stage and the separation efficiency coefficient of the plant allelochemicals in each simulation stage; The processing module is further configured to perform constraint analysis on the separation state between each adjacent simulation stage of each active ingredient to obtain the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage; The processing module is further configured to determine the simulation fitness of the concentration distribution of each active ingredient in each simulation stage through the dynamic dependence relationship between the separation condition parameters in the simulation process of plant allelochemical separation and extraction and the constraint control quantity of the separation state of each active ingredient in each adjacent simulation stage; An execution module, configured to perform dynamic simulation on the extraction purity of various active ingredients based on all the simulation fitnesses.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the separation and extraction simulation method of the active ingredients of plant allelopathy described in any one of claims 1 to 7.

10. A computer-readable storage medium stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the separation and extraction simulation method of the active ingredients of plant allelopathy described in any one of claims 1 to 7.

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