Raw material component content measuring method and system and dendrobe extraction method

Abstract

The invention discloses a raw material component content measuring method, a raw material component content measuring system and a dendrobium extraction method, and relates to the field of component content measurement. According to the raw material component content measuring method, raw material storage environment influence factor data and raw material component content data detection frequency are considered, and the raw material component content is measured by adjusting the influence factor data and the detection frequency; according to the method, the deterioration rate of the raw materials can be controlled to be optimal after the raw materials are stored, and the high quality of the raw materials supplied in the subsequent extraction process is ensured, so that the quality and the drug effect of an extract are ensured; a current storage raw material component content influence factor initial data set and a current storage component content initial detection frequency data set are subjected to multiple times of iterative adjustment by adopting a myxomycete optimization algorithm, and the deterioration rate at the end of storage of the corresponding raw materials is used as a fitness function; therefore, along with iteration, the deterioration rate of the raw materials at the end of storage becomes smaller and smaller, and the requirements of subsequent extraction are finally met.

Description

Technical Field

[0001] The present invention belongs to the field of component content measurement, and in particular, relates to a method and system for measuring the content of raw material components and a dendrobium extraction method. Background Art

[0002] Chinese patent publication number CN113198207B discloses a Huoshan Dendrobium deep processing stock solution extraction process, which solves the problem that during overall squeezing, the inner Dendrobium segments are less likely to be squeezed than the outer Dendrobium segments, resulting in difficulty in completely squeezing the juice out of the Dendrobium segments, thereby reducing the juice yield of the Dendrobium segments. The transfer and injection method elsewhere, on the one hand, takes a certain amount of time to transfer the juice, which may affect the freshness. On the other hand, the juice may spill out during injection. Some severely depressed areas are difficult to restore to their original shape by applying pressure, thereby reducing the fit and the bonding area, thereby affecting the molding quality of the steel-plastic composite pipe.

[0003] Chinese patent publication number CN101019909B discloses a method for supercritical extraction of safflower, comprising a first extraction of the raw material using supercritical carbon dioxide as an extractant; a two-stage vacuum separation to obtain plant wax and safflower volatile oil; a second extraction of the safflower extract to obtain a macromolecular non-polar substance; and a third extraction of the safflower extract in the presence of an entrainer and carbon dioxide, followed by vacuum separation to obtain a polar substance and an entrainer.

[0004] Before extracting Dendrobium, it is usually necessary to measure the content of Dendrobium components. By measuring the content of active ingredients in Dendrobium, it can be determined whether the quality of Dendrobium meets the standards, so that high-quality Dendrobium can be selected for extraction to ensure the quality and efficacy of the extract. Understanding the content of different components in Dendrobium allows us to select appropriate extraction methods, solvents, time and other conditions according to the properties and content of the target components, thereby improving extraction efficiency and selectivity and reducing interference from impurities. In addition, by analyzing the content of Dendrobium components, we can fully utilize the active ingredients in Dendrobium, avoid waste of resources and reduce production costs.

[0005] Chinese patent publication number CN110954579B discloses a method for detecting a target object, transmitting at least one transmission signal to the target object according to a signal transmission rule, and receiving a return signal corresponding to at least one transmission signal; obtaining a backscatter parameter value and a time difference corresponding to the target object based on the at least one transmission signal and the return signal corresponding to the at least one transmission signal; and determining the component content of the target object using a functional relationship between the dielectric constant and the backscatter parameter based on the backscatter parameter value and the time difference. This embodiment transmits a signal to the object, analyzes the dielectric constant of the object in combination with the return signal, and then obtains the component content of the object, achieving the effect of continuously and non-destructively monitoring the component content of the object. The obtained component content of the object is highly accurate and practical.

[0006] Chinese patent publication number CN108195724B discloses a method and device for measuring the content of grain components, which calculates corresponding other parameter information based on parameters such as the effective volume, weight, and density of the target.

[0007] Chinese patents CN110954579B and CN108195724B only measure the component content of raw materials (fruits, vegetables or seeds) without considering that the component content in the raw materials is constantly changing with external conditions and time. In particular, when the raw materials are stored, the storage conditions have a significant impact on the component content of the raw materials. The stability of the active ingredients of the raw materials is affected by the combined effects of factors such as temperature, humidity and storage time in the storage environment. Different storage environments have different effects on the active ingredients of the raw materials. When measuring the component content of the raw materials, there is no dynamic adjustment of the strategy for measuring the component content of the Dendrobium raw materials (such as measurement frequency) according to different storage environments. As a result, the active ingredients of the raw materials undergo significant changes when storage is completed, thereby affecting the accuracy of the measurement. Summary of the Invention

[0008] In response to the problems in the related art, the present invention proposes a method and system for measuring the content of raw material components and a dendrobium extraction method to overcome the above-mentioned technical problems existing in the existing related technology.

[0009] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0010] The present invention is a method for measuring the content of raw material components, comprising the following steps:

[0011] S1. The raw material component type setting module sets several raw material component types to obtain a raw material component type set; sets several storage environment influencing factor types that have an impact on the raw material components to obtain a raw material component content influencing factor type set;

[0012] S2. Based on the raw material component type set and the raw material component content influencing factor type set, the component content measurement frequency corresponding to the raw material component content measurement in history, the raw material deterioration rate at the end of storage, the various types of component content influencing factors during the raw material storage process, and the raw material storage time data are collected to construct a final raw material storage deterioration rate mapping equation;

[0013] S3. Collecting data on factors affecting the content of various types of raw materials, storage time, and frequency of testing the content of various types of components during the storage of multiple raw material samples to be tested, and inputting these data into a final raw material storage deterioration rate mapping equation for mapping to obtain initial deterioration rate data of the current raw materials;

[0014] S4. Adjusting the current storage component content initial detection frequency dataset and the current storage raw material component content influencing factor initial dataset based on the current raw material initial deterioration rate data to obtain the current storage raw material component content influencing factor final dataset and the current storage component content final detection frequency dataset;

[0015] This plan starts from the data of influencing factors of the storage environment of raw materials and the detection frequency of raw material component content data. By adjusting the influencing factor data and the detection frequency, the deterioration rate of the raw materials can be controlled to the optimal level after storage is completed, ensuring the high quality of the raw materials provided in the subsequent extraction process, thereby ensuring the quality and efficacy of the extract.

[0016] Preferably, the S1 comprises the following steps:

[0017] S11. Setting several raw material component types to obtain a raw material component type set; setting several storage environment influencing factor types that have an impact on the component content corresponding to each raw material component type in the raw material component type set to obtain a raw material component content influencing factor type set;

[0018] The raw material is preferably dendrobium;

[0019] S12, setting a number of raw material samples to be measured to obtain a set of raw material samples to be measured;

[0020] By measuring the content of active ingredients in Dendrobium, it is possible to determine whether the quality of Dendrobium meets the standards, thereby selecting high-quality Dendrobium for extraction to ensure the quality and efficacy of the extract; by understanding the content of different components in Dendrobium, it is possible to select appropriate extraction methods, solvents, time and other conditions according to the properties and content of the target components, thereby improving extraction efficiency and selectivity and reducing interference from impurities; by analyzing the content of Dendrobium components, it is possible to fully utilize the active ingredients in Dendrobium, avoid waste of resources and reduce production costs; based on the above reasons, this scheme provides a measurement object for subsequent raw material component content measurement by setting a raw material component type set; among them, by considering the influencing factors affecting the content of various active ingredients in the raw materials, a certain basis is provided for setting the frequency of subsequent raw material measurements.

[0021] Preferably, said S2 comprises the following steps:

[0022] S21. Collecting, in conjunction with the raw material component type set and the raw material component content influencing factor type set, component content measurement frequencies corresponding to several groups of historical raw material component content measurements, raw material deterioration rates at the end of raw material storage, various types of component content influencing factors during raw material storage, and raw material storage time data, to obtain a historical raw material component content measurement frequency data matrix, a historical raw material deterioration rate data set at the end of raw material storage, a historical raw material storage component content influencing factor data matrix, and a historical raw material storage time data set;

[0023] S22, constructing a final raw material storage deterioration rate mapping equation using the historical raw material component content measurement frequency data matrix, the historical raw material end storage deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set;

[0024] By constructing a mapping relationship between raw material component content measurement frequency data, raw material storage component content influencing factor data, historical raw material storage time data and raw material deterioration rate data at the end of storage, a mapping tool is provided for subsequently obtaining the corresponding storage deterioration rate based on the current storage data, so as to determine whether it is necessary to adjust the component content measurement frequency during raw material storage and the storage component content influencing factor data in order to minimize the raw material storage deterioration rate, thereby ensuring the quality of the raw material during extraction; wherein, by collecting the frequency data corresponding to each component type to construct a mapping equation, the subsequent adjustment of the component content detection frequency is made more precise and accurate, thereby ensuring the quality of the raw material after storage.

[0025] Preferably, the S22 includes the following steps:

[0026] S221, constructing an initial raw material storage deterioration rate mapping equation based on the historical raw material component content measurement frequency data matrix, the historical raw material end storage deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set;

[0027] S222, substituting the historical raw material component content measurement frequency data matrix, the historical raw material storage end deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set for each set of data on the process of measuring the component content of the raw materials in history into the initial raw material storage deterioration rate mapping equation for mapping, thereby obtaining an initial mapping data set of the historical raw material storage deterioration rate;

[0028] S223, setting a raw material storage deterioration rate mapping error threshold; calculating the error data between the historical raw material storage deterioration rate initial mapping data set and the historical raw material storage end deterioration rate data set to obtain the historical raw material storage deterioration rate initial mapping error data;

[0029] When the historical raw material storage deterioration rate initial mapping error data is greater than or equal to the raw material storage deterioration rate mapping error threshold, the initial raw material storage deterioration rate mapping equation is adjusted until the historical raw material storage deterioration rate initial mapping error data is less than the raw material storage deterioration rate mapping error threshold, thereby obtaining a final raw material storage deterioration rate mapping equation; otherwise, the initial raw material storage deterioration rate mapping equation does not need to be adjusted, and the initial raw material storage deterioration rate mapping equation is used as the final raw material storage deterioration rate mapping equation;

[0030] By substituting multiple groups of historical actual data collected into the initial raw material storage deterioration rate mapping equation for mapping, it is possible to determine whether the mapping accuracy of the initial raw material storage deterioration rate mapping equation meets the requirements based on the error between the mapping data and the historical actual data, and then determine whether the initial raw material storage deterioration rate mapping equation needs to be adjusted so that the mapping accuracy of the final raw material storage deterioration rate mapping equation meets the mapping requirements; among which, by setting the raw material storage deterioration rate mapping error threshold, a quantitative judgment standard is provided for determining whether the mapping accuracy of the initial raw material storage deterioration rate mapping equation meets the requirements.

[0031] Preferably, adjusting the initial raw material storage deterioration rate mapping equation in S223 includes the following steps:

[0032] S2231, setting the value interval of each independent variable coefficient, independent variable index coefficient, and bias in the initial raw material storage deterioration rate mapping equation to obtain a raw material storage deterioration rate mapping independent variable index value interval matrix and a raw material storage deterioration rate mapping bias value interval;

[0033] Construct a raw material storage deterioration rate mapping equation to adjust the slime mold population; set the maximum number of iterations of the raw material storage deterioration rate mapping equation to adjust the slime mold population And the current number of iterations is They are recorded as the maximum number of iterations of the storage-change mapping and the current number of iterations of the storage-change mapping respectively;

[0034] S2232: generating a raw material storage deterioration rate mapping equation based on the raw material storage deterioration rate mapping independent variable index value interval matrix and the raw material storage deterioration rate mapping bias value interval, adjusting the initial position matrix of each slime mold in the slime mold population, and obtaining a first initial position matrix set;

[0035] S2233, constructing the raw material storage deterioration rate mapping equation to adjust the fitness function of the slime mold population;

[0036] S2234. Iteration begins. Before iteration, the current iteration count of the storage-change mapping is set to 1. During the first iteration, the fitness function of the slime mold population is adjusted using the raw material storage deterioration rate mapping equation to calculate the fitness value of the initial position matrix of each slime mold in the first initial position matrix set, thereby obtaining a first fitness value set. The maximum fitness value in the first fitness value set and the initial position matrix of the corresponding slime mold are used as the first global optimal fitness and the first global optimal position, respectively. The initial position matrix of each slime mold in the first initial position matrix set is updated according to the first global optimal fitness and the first global optimal position. After the update is completed, the current iteration count of the storage-change mapping is increased by 1, and the next iteration is entered.

[0037] In each of the remaining iterations, the raw material storage deterioration rate mapping equation is used to adjust the fitness function of the slime mold population, and the fitness value of the position matrix of each slime mold in the slime mold population is calculated by adjusting the raw material storage deterioration rate mapping equation obtained in the previous iteration to obtain a second fitness value set; the maximum fitness value in the second fitness value set and the corresponding slime mold position matrix are used as the second global optimal fitness and the second global optimal position, respectively; the raw material storage deterioration rate mapping equation obtained in the previous iteration is used to adjust the position matrix of each slime mold in the slime mold population, and the position matrix is ​​updated according to the second global optimal fitness and the second global optimal position; after the update is completed, the current iteration number of the storage deterioration rate mapping is increased by 1, and the next iteration is entered;

[0038] S2235, when When , stop the iteration and get the first final global best fitness and the first final global best position; otherwise, continue the iteration until ; using the first final global optimal fitness as the optimized historical raw material storage deterioration rate mapping error data; when the optimized historical raw material storage deterioration rate mapping error data is less than the raw material storage deterioration rate mapping error threshold, substituting each position component of the first final global optimal position into the initial raw material storage deterioration rate mapping equation to obtain the final raw material storage deterioration rate mapping equation; otherwise, returning to S2234 and continuing to iterate until the optimized historical raw material storage deterioration rate mapping error data is less than the raw material storage deterioration rate mapping error threshold;

[0039] By simulating the foraging behavior of slime molds, the slime mold optimization algorithm can conduct extensive exploration in the search space, possessing strong global search capabilities and capable of finding areas closer to the global optimal solution. When approaching a food source, it can adjust its search strategy to conduct a more refined local search, improving the accuracy of the solution. The algorithm is insensitive to the distribution of the initial population and the choice of initial values, and can stably converge to the optimal solution under different initial conditions. Furthermore, it can withstand a certain degree of interference and noise during the search process, maintaining good search performance. Based on these advantages, the slime mold optimization algorithm is used in this scheme to iteratively adjust the independent variable coefficients, independent variable exponential coefficients, and bias of the initial raw material storage deterioration rate mapping equation multiple times, and the mapping accuracy of the initial raw material storage deterioration rate mapping equation is used as the fitness function. Therefore, as the iterations proceed, the mapping accuracy of the initial raw material storage deterioration rate mapping equation increases, ultimately meeting the mapping requirements.

[0040] Preferably, the step S3 includes the following steps:

[0041] S31. During storage of each of the raw material samples to be tested in the current raw material sample set, presetting data on various types of raw material component content influencing factors, storage time, and frequency data for testing various types of component contents in the storage environment in conjunction with the raw material component content influencing factor type set, thereby obtaining an initial dataset of current storage raw material component content influencing factors, current storage time data, and an initial dataset of current storage component content testing frequencies;

[0042] S32, inputting the initial dataset of factors affecting the current storage raw material component content, the current storage time data, and the initial detection frequency dataset of the current storage component content into a final raw material storage deterioration rate mapping equation for mapping, thereby obtaining the current raw material initial deterioration rate data;

[0043] By collecting the initial data set of factors affecting the content of raw material components, the current storage time data, and the initial detection frequency data set of the current storage component content, data support is provided for mapping the deterioration rate of the corresponding raw materials after storage is completed. Before storing the current raw material samples to be tested, according to the mapping results, a basis is provided for subsequent adjustments to the data of factors affecting the content of raw material components and the component detection frequency of various types of storage environments, so that the trend of raw material deterioration can be perceived in a timely manner, and measures can be taken in time to ensure the quality of the raw materials at the end of storage is optimized.

[0044] Preferably, said S4 comprises the following steps:

[0045] S41, setting a current raw material deterioration rate threshold;

[0046] S42. When the current raw material initial deterioration rate data is greater than or equal to the current raw material deterioration rate threshold, adjusting the current stored raw material component content influencing factor initial dataset and the current stored raw material component content initial detection frequency dataset until the current raw material initial deterioration rate data is less than the current raw material deterioration rate threshold, thereby obtaining the current stored raw material component content influencing factor final dataset and the current stored raw material component content final detection frequency dataset;

[0047] By setting the current raw material deterioration rate threshold, a quantitative judgment standard is provided for determining whether the deterioration rate data obtained by the current mapping meets the requirements; by adjusting the component content detection frequency and storage environment influencing factor data, the quality of the current raw material sample to be tested is guaranteed after storage is completed.

[0048] Preferably, adjusting the initial dataset of factors influencing the current storage raw material component content and the initial dataset of detection frequencies of the current storage component content in S42 includes the following steps:

[0049] S421, respectively setting the value interval of each influencing factor data in the initial data set of influencing factors of the current stored raw material component content and each detection frequency data in the initial detection frequency data set of the current stored component content, to obtain a value interval set of the current component content influencing factor and a value interval set of the current component content detection frequency;

[0050] Construct the current raw material storage influencing factor detection frequency adjustment slime mold population; set the current raw material storage influencing factor detection frequency adjustment slime mold population maximum number of iterations to And the current number of iterations is They are recorded as the maximum number of iterations of factor frequency and the current number of iterations of factor frequency respectively;

[0051] S422. Generate an initial position matrix for each slime mold in the slime mold population based on the current raw material storage influencing factor detection frequency adjustment according to the current component content influencing factor value interval set and the current component content detection frequency value interval set, to obtain a second initial position matrix set;

[0052] S423, constructing a fitness function for adjusting the slime mold population by detecting the frequency of the current raw material storage influencing factors;

[0053] S424. Iteration begins. Before iteration, the current iteration number of the factor frequency is set to 1. During the first round of iteration, the fitness function of the slime mold population is adjusted using the current raw material storage influencing factor detection frequency to calculate the fitness value of the initial position matrix of each slime mold in the second initial position matrix set to obtain a third fitness value set. The maximum fitness value in the third fitness value set and the corresponding initial position matrix of the slime mold are respectively used as the third global optimal fitness and the third global optimal position. The initial position matrix of each slime mold in the second initial position matrix set is updated according to the third global optimal fitness and the third global optimal position. After the update is completed, the current iteration number of the factor frequency is increased by 1, and the next round of iteration is entered.

[0054] In each of the remaining iterations, the fitness function of the slime mold population adjusted by the current raw material storage influencing factor detection frequency is used to calculate the fitness value of the position matrix of each slime mold in the slime mold population adjusted by the current raw material storage influencing factor detection frequency, which was updated in the previous iteration, to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding slime mold position matrix are used as the fourth global optimal fitness and the fourth global optimal position, respectively; the position matrix of each slime mold in the slime mold population adjusted by the current raw material storage influencing factor detection frequency, which was updated in the previous iteration, is updated according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, the current iteration number of the factor frequency is increased by 1, and the next iteration is entered;

[0055] S425, when When , stop the iteration and get the second final global best fitness and the second final global best position; otherwise, continue the iteration until until time; using the second final global optimal fitness as the current optimized raw material deterioration rate data; when the current optimized raw material deterioration rate data is less than the current raw material deterioration rate threshold, using the position components of the second final global optimal position in the dimensions of the current storage raw material component content influencing factor initial data set and the current storage component content initial detection frequency data set as the current storage raw material component content influencing factor final data set and the current storage component content final detection frequency data set respectively;

[0056] In this scheme, a slime mold optimization algorithm is used to iteratively adjust the initial dataset of factors affecting the current storage raw material content and the initial detection frequency dataset of the current storage component content. The corresponding raw material deterioration rate at the end of storage is used as the fitness function. Therefore, as the iterations proceed, the deterioration rate of the raw material at the end of storage becomes smaller and smaller, ultimately meeting the needs of subsequent extraction.

[0057] The raw material component content measurement system includes a raw material component type setting module, a raw material component content influencing factor setting module, a raw material storage deterioration rate mapping equation construction module, a current storage raw material sample deterioration rate data mapping module and a current storage component detection frequency factor adjustment module.

[0058] The present invention also discloses a dendrobium extraction method, comprising the following steps:

[0059] S51, collecting dendrobium raw materials, and measuring the component content of the dendrobium raw materials using the above-mentioned raw material component content measurement method; when the component content measurement result shows that the dendrobium raw materials have deteriorated, replacing other dendrobium raw materials for extraction;

[0060] S52, crushing the dendrobium raw material with a tissue crusher, and then refluxing with an organic solvent such as petroleum ether, acetone or ethyl acetate to volatilize and remove the organic solvent to obtain dendrobium residues;

[0061] S53, weighing the dendrobium residue, placing it in an extraction and concentration unit, adding distilled water to extract 3-5 times, and putting the extract obtained after each extraction into a concentration kettle for concentration to obtain a dendrobium concentrate;

[0062] S54, taking the dendrobium concentrate, adding ethanol and letting it stand, then centrifuging, and collecting the precipitate after the centrifugation to obtain a dendrobium precipitate;

[0063] S55. Dissolving the dendrobium precipitate in distilled water to obtain a dendrobium extract solution, centrifuging the dendrobium extract solution to remove protein, and collecting the centrifugal supernatant; concentrating the centrifugal supernatant using an ultrafiltration membrane while removing small molecular impurities such as monosaccharides and oligosaccharides, and collecting the dendrobium extract.

[0064] The present invention has the following beneficial effects:

[0065] 1. The present invention starts from the perspective of the influencing factor data of the storage environment of the raw materials and the detection frequency of the raw material component content data, and adjusts the influencing factor data and the detection frequency, so that the deterioration rate of the raw materials after storage is completed can be controlled to the optimal level, thereby ensuring the high quality of the raw materials provided in the subsequent extraction process, thereby ensuring the quality and efficacy of the extract.

[0066] 2. In the present invention, by collecting the frequency data corresponding to each component type to construct a mapping equation, the subsequent adjustment of the frequency of component content detection is made more precise and accurate, thereby ensuring the quality of the raw materials after storage.

[0067] 3. In the present invention, the slime mold optimization algorithm is used to iteratively adjust the independent variable coefficient, independent variable exponential coefficient, and bias of the initial raw material storage deterioration rate mapping equation multiple times, and the mapping accuracy of the initial raw material storage deterioration rate mapping equation is used as the fitness function; therefore, as the iteration proceeds, the mapping accuracy of the initial raw material storage deterioration rate mapping equation becomes higher and higher, and ultimately meets the mapping requirements.

[0068] 4. In the present invention, a slime mold optimization algorithm is used to iteratively adjust the initial dataset of factors affecting the current storage raw material component content and the initial dataset of detection frequencies of the current storage component content, and the corresponding raw material deterioration rate at the end of storage is used as the fitness function. Therefore, as the iterations proceed, the deterioration rate of the raw material at the end of storage becomes smaller and smaller, ultimately meeting the requirements of subsequent extraction.

[0069] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 Schematic diagram of the process of measuring the content of raw material components of the present invention;

[0072] Figure 2 A schematic diagram of the process for constructing a final raw material storage deterioration rate mapping equation for the present invention;

[0073] Figure 3 Schematic diagram of the process of adjusting the initial detection frequency dataset of the current storage component content and the initial dataset of factors affecting the current storage raw material component content according to the present invention;

[0074] Figure 4 Schematic diagram of the modules of the raw material component content measurement system of the present invention. DETAILED DESCRIPTION

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0076] Example 1

[0077] See also Figure 1-3 , this embodiment is a method for measuring the content of raw material components, comprising the following steps:

[0078] S1. The raw material component type setting module sets several raw material component types to obtain a raw material component type set; sets several storage environment influencing factor types that have an impact on the raw material components to obtain a raw material component content influencing factor type set;

[0079] Said S1 comprises the following steps:

[0080] S11. Set several types of raw material components to obtain a set of raw material component types; taking dendrobium as an example, the raw material component type set includes dendrobium polysaccharides, active polysaccharides, dendrobium alkaloids, dendrobium phenol, bibenzyl compounds and phenanthrene compounds, etc., wherein the raw material components such as dendrobium polysaccharides, active polysaccharides, dendrobium alkaloids, dendrobium phenol, bibenzyl compounds and phenanthrene compounds can be detected by high performance liquid chromatography; set several types of storage environment influencing factors that have an impact on the content of the component corresponding to each raw material component type in the raw material component type set, and obtain a set of raw material component content influencing factor types; the raw material component content influencing factor type set includes temperature, humidity, light, packaging method and storage time, etc., wherein different types of packaging methods are represented by natural numbers;

[0081] The raw material is preferably dendrobium;

[0082] S12, setting a number of raw material samples to be measured to obtain a set of raw material samples to be measured;

[0083] S2. Based on the raw material component type set and the raw material component content influencing factor type set, the component content measurement frequency corresponding to the raw material component content measurement in history, the raw material deterioration rate at the end of storage, the various types of component content influencing factors during the raw material storage process, and the raw material storage time data are collected to construct a final raw material storage deterioration rate mapping equation;

[0084] The S2 comprises the following steps:

[0085] S21, in conjunction with the raw material component type set and the raw material component content influencing factor type set, collect the component content measurement frequency corresponding to several groups of raw material component content measurements in the past, the deterioration rate at the end of raw material storage, the various types of component content influencing factors during the raw material storage process, and the raw material storage time data, to obtain the historical raw material component content measurement frequency data matrix a1, the historical raw material deterioration rate at the end of storage data set Historical raw material storage component content influencing factors data matrix a3 and historical raw material storage time dataset Among them, the way to judge whether the raw materials have deteriorated at the end of storage is that there may be odor during the deterioration process, if the raw materials emit an unpleasant odor, then it may have deteriorated, and its component content data has changed significantly, etc.; 2i 、a 4i They represent the deterioration rate data and storage time data of the raw materials at the end of storage during the i-th group of raw material component content measurement in history, and a1′ represents the total number of groups of raw material component content measurement processes in history; a1 and a3 are as follows:

[0086]

[0087] Among them, a 1ij represents the frequency data of measuring the content of the jth type of raw material component during the process of measuring the content of the raw material component in the history of the i-th group of raw materials, a3′ represents the total number of the set raw material component types; a 3ij represents the jth type of raw material storage component content influencing factor data collected during the i-th group of historical raw material component content measurements, and a′2 represents the total number of set raw material storage component content influencing factor types;

[0088] S22, constructing a final raw material storage deterioration rate mapping equation using the historical raw material component content measurement frequency data matrix, the historical raw material end storage deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set;

[0089] The S22 includes the following steps:

[0090] S221, constructing an initial raw material storage deterioration rate mapping equation based on the historical raw material component content measurement frequency data matrix, the historical raw material storage end deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set; as follows,

[0091]

[0092] in, is the dependent variable of the initial raw material storage deterioration rate mapping equation, representing the deterioration rate data at the end of raw material storage; They are the first The independent variables represent the raw material content measurement frequency data, the j-th type of raw material storage component content influencing factors data, and the raw material storage time data; They are The coefficient of the independent variable, They are The independent variable exponential coefficient of α represents the bias of the raw material storage deterioration rate mapping equation;

[0093] S222, the data matrix of the historical raw material component content measurement frequency, the historical raw material storage end deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set of each set of data on the process of measuring the component content of the raw material in history are respectively substituted into the initial raw material storage deterioration rate mapping equation for mapping, thereby obtaining the initial mapping data set of the historical raw material storage deterioration rate. represents the data obtained by substituting the historical raw material component content measurement frequency data matrix, the historical raw material storage end deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set into the initial raw material storage deterioration rate mapping equation for mapping;

[0094] S223, setting a raw material storage deterioration rate mapping error threshold; calculating the error data between the historical raw material storage deterioration rate initial mapping data set and the historical raw material storage end deterioration rate data set to obtain the historical raw material storage deterioration rate initial mapping error data b; the calculation formula is as follows,

[0095]

[0096] When the historical raw material storage deterioration rate initial mapping error data is greater than or equal to the raw material storage deterioration rate mapping error threshold, the initial raw material storage deterioration rate mapping equation is adjusted until the historical raw material storage deterioration rate initial mapping error data is less than the raw material storage deterioration rate mapping error threshold, thereby obtaining a final raw material storage deterioration rate mapping equation; otherwise, the initial raw material storage deterioration rate mapping equation does not need to be adjusted, and the initial raw material storage deterioration rate mapping equation is used as the final raw material storage deterioration rate mapping equation;

[0097] Adjusting the initial raw material storage deterioration rate mapping equation in S223 includes the following steps:

[0098] S2231, setting the value interval of each independent variable coefficient, independent variable index coefficient and bias in the initial raw material storage deterioration rate mapping equation, and obtaining the raw material storage deterioration rate mapping independent variable index value interval matrix And the value range of the raw material storage deterioration rate mapping bias respectively represent the lower limit and upper limit of the bias amount of the initial raw material storage deterioration rate mapping equation; as follows,

[0099]

[0100] in, They respectively represent the lower limit and upper limit of the value of the j-th independent variable coefficient in the initial raw material storage deterioration rate mapping equation; They respectively represent the lower limit and upper limit of the value of the j-th independent variable exponential coefficient in the initial raw material storage deterioration rate mapping equation;

[0101] Construct a raw material storage deterioration rate mapping equation to adjust the slime mold population; set the maximum number of iterations of the raw material storage deterioration rate mapping equation to adjust the slime mold population And the current number of iterations is The maximum number of iterations of the storage-deterioration mapping and the current number of iterations of the storage-deterioration mapping are recorded respectively; the search space dimension of the slime mold population adjusted by the raw material storage deterioration rate mapping equation is (2·a′2+2·a3′+3);

[0102] S2232: Generate a raw material storage deterioration rate mapping equation based on the raw material storage deterioration rate mapping independent variable index value interval matrix and the raw material storage deterioration rate mapping bias value interval, and adjust the initial position matrix of each slime mold in the slime mold population to obtain a first initial position matrix set. c 1i represents the initial position matrix of the i-th slime mold in the slime mold population adjusted by the raw material storage deterioration rate mapping equation, c1′ represents the scale of the slime mold population adjusted by the raw material storage deterioration rate mapping equation; c 1i as follows,

[0103]

[0104] Among them, c 1i1j 、c 1i2j 、c 1i3 They respectively represent the position components of the j-th independent variable coefficient, the j-th independent variable index coefficient, and the offset dimension of the initial position matrix of the i-th slime mold in the slime mold population adjusted by the raw material storage deterioration rate mapping equation; the generation formulas are as follows:

[0105]

[0106] Where rand 1i1j 、rand 1i2j 、rand 1i3 Respectively for c 1i1j 、c 1i2j 、c 1i3 Generate a random number between 0 and 1;

[0107] S2233, construct the raw material storage deterioration rate mapping equation to adjust the fitness function b1′ of the slime mold population; as follows,

[0108]

[0109] Where c1′ represents the substitution of a set of independent variable coefficients, independent variable index coefficients, and bias obtained in each round of iteration into the initial raw material storage deterioration rate mapping equation, and then the historical raw material component content measurement frequency data matrix, historical raw material storage end deterioration rate data set, historical raw material storage component content influencing factor data matrix, and historical raw material storage time data set of each set of historical raw material component content measurement process data in S222 are substituted into the initial raw material storage deterioration rate mapping equation for mapping to obtain the error between the mapped data set and the actual data; χ1 is a positive number, representing the first protection parameter;

[0110] S2234. Start iteration. Before the iteration, set the current iteration number of the storage-change mapping to 1. During the first round of iteration, use the raw material storage deterioration rate mapping equation to adjust the fitness function b1′ of the slime mold population and calculate the fitness value of the initial position matrix of each slime mold in the first initial position matrix set to obtain a first fitness value set. Use the maximum fitness value in the first fitness value set and the initial position matrix of the corresponding slime mold as the first global optimal fitness and the first global optimal position, respectively. Update the initial position matrix of each slime mold in the first initial position matrix set according to the first global optimal fitness and the first global optimal position. After the update is completed, increase the current iteration number of the storage-change mapping by 1 and enter the next round of iteration.

[0111] In each of the remaining iterations, the raw material storage deterioration rate mapping equation is used to adjust the fitness function b1′ of the slime mold population, calculating the fitness value of the position matrix of each slime mold in the slime mold population adjusted by the raw material storage deterioration rate mapping equation obtained in the previous iteration, to obtain a second fitness value set; the maximum fitness value in the second fitness value set and the corresponding slime mold position matrix are used as the second global optimal fitness and the second global optimal position, respectively; the position matrix of each slime mold in the slime mold population adjusted by the raw material storage deterioration rate mapping equation obtained in the previous iteration is updated according to the second global optimal fitness and the second global optimal position; after the update is completed, the current iteration number of the storage deterioration rate mapping is increased by 1 and the next iteration is entered;

[0112] S2235, when When , stop the iteration and get the first final global best fitness and the first final global best position; otherwise, continue the iteration until ; using the first final global optimal fitness as the optimized historical raw material storage deterioration rate mapping error data; when the optimized historical raw material storage deterioration rate mapping error data is less than the raw material storage deterioration rate mapping error threshold, substituting each position component of the first final global optimal position into the initial raw material storage deterioration rate mapping equation to obtain the final raw material storage deterioration rate mapping equation; otherwise, returning to S2234 and continuing to iterate until the optimized historical raw material storage deterioration rate mapping error data is less than the raw material storage deterioration rate mapping error threshold;

[0113] S3. Collecting data on factors affecting the content of various types of raw materials, storage time, and frequency of testing the content of various types of components during the storage of multiple raw material samples to be tested, and inputting these data into a final raw material storage deterioration rate mapping equation for mapping to obtain initial deterioration rate data of the current raw materials;

[0114] The S3 includes the following steps:

[0115] S31. During storage of each of the raw material samples to be tested in the current raw material sample set, presetting data on various types of raw material component content influencing factors, storage time, and frequency data for testing various types of component contents in the storage environment in conjunction with the raw material component content influencing factor type set, thereby obtaining an initial dataset of current storage raw material component content influencing factors, current storage time data, and an initial dataset of current storage component content testing frequencies;

[0116] S32, inputting the initial dataset of factors affecting the current storage raw material component content, the current storage time data, and the initial detection frequency dataset of the current storage component content into a final raw material storage deterioration rate mapping equation for mapping, thereby obtaining the current raw material initial deterioration rate data;

[0117] S4. Adjusting the current storage component content initial detection frequency dataset and the current storage raw material component content influencing factor initial dataset based on the current raw material initial deterioration rate data to obtain the current storage raw material component content influencing factor final dataset and the current storage component content final detection frequency dataset;

[0118] The S4 comprises the following steps:

[0119] S41, setting a current raw material deterioration rate threshold;

[0120] S42. When the current raw material initial deterioration rate data is greater than or equal to the current raw material deterioration rate threshold, adjusting the current stored raw material component content influencing factor initial dataset and the current stored raw material component content initial detection frequency dataset until the current raw material initial deterioration rate data is less than the current raw material deterioration rate threshold, thereby obtaining the current stored raw material component content influencing factor final dataset and the current stored raw material component content final detection frequency dataset;

[0121] Adjusting the initial dataset of factors influencing the current storage raw material content and the initial dataset of detection frequencies of the current storage component content in S42 includes the following steps:

[0122] S421, respectively set the value interval of each influencing factor data in the initial data set of influencing factors of the current storage raw material content and each detection frequency data in the initial detection frequency data set of the current storage component content, and obtain the value interval set of the current component content influencing factor. And the current component content detection frequency value interval set They are as follows:

[0123]

[0124] in, They represent the lower limit and upper limit of the value of the jth influencing factor data in the initial data set of factors affecting the content of the current stored raw materials; They represent the lower limit and upper limit of the jth detection frequency data in the initial detection frequency data set of the current storage component content;

[0125] Construct the current raw material storage influencing factor detection frequency adjustment slime mold population; set the current raw material storage influencing factor detection frequency adjustment slime mold population maximum number of iterations to And the current number of iterations is They are recorded as the maximum iteration number of factor frequency and the current iteration number of factor frequency respectively; the search space dimension of the slime mold population adjusted by the detection frequency of the current raw material storage influencing factor is (a′2+a3′);

[0126] S422: Generate an initial position matrix of each slime mold in the slime mold population based on the current raw material storage influencing factor detection frequency adjustment according to the current component content influencing factor value interval set and the current component content detection frequency value interval set, and obtain a second initial position matrix set. c 2i represents the initial position matrix of the i-th slime mold in the slime mold population adjusted by the detection frequency of the current raw material storage influencing factors, c′2 represents the size of the slime mold population adjusted by the detection frequency of the current raw material storage influencing factors; c 2i as follows,

[0127]

[0128] Among them, c 2i1j 、c 2i2j They respectively represent the position components of the initial position matrix of the i-th slime mold in the slime mold population adjusted by the detection frequency of the current raw material storage influencing factors, the j-th influencing factor data in the initial dataset of the influencing factors of the current storage raw material component content, and the j-th detection frequency data dimension in the initial detection frequency dataset of the current storage component content; the generation formulas are as follows:

[0129]

[0130] Where rand 2i1j 、rand 2i2j Respectively for c 2i1j 、c 2i2j Generate a random number between 0 and 1;

[0131] S423, constructing the fitness function b2′ of the slime mold population for adjusting the detection frequency of the current raw material storage influencing factors; as follows:

[0132]

[0133] Where c′2 represents the deterioration rate data obtained by mapping a set of current raw material detection frequency data, storage influencing factor data, and current storage time data obtained in each round of iteration into the final raw material storage deterioration rate mapping equation; χ2 is a positive number, representing the second protection parameter;

[0134] S424. Iteration begins. Before iteration, the current iteration number of the factor frequency is set to 1. During the first iteration, the fitness function b2′ of the slime mold population is adjusted using the current raw material storage influencing factor detection frequency to calculate the fitness value of the initial position matrix of each slime mold in the second initial position matrix set to obtain a third fitness value set. The maximum fitness value in the third fitness value set and the corresponding initial position matrix of the slime mold are used as the third global optimal fitness and the third global optimal position, respectively. The initial position matrix of each slime mold in the second initial position matrix set is updated according to the third global optimal fitness and the third global optimal position. After the update is completed, the current iteration number of the factor frequency is increased by 1, and the next iteration is started.

[0135] In each of the remaining iterations, the fitness function b2′ of the slime mold population adjusted by the current raw material storage influencing factor detection frequency is used to calculate the fitness value of the position matrix of each slime mold in the slime mold population adjusted by the current raw material storage influencing factor detection frequency, which was updated in the previous iteration, to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding slime mold position matrix are used as the fourth global optimal fitness and the fourth global optimal position, respectively; the position matrix of each slime mold in the slime mold population adjusted by the current raw material storage influencing factor detection frequency, which was updated in the previous iteration, is updated according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, the current iteration number of the factor frequency is increased by 1 and the next iteration is entered;

[0136] S425, when When , stop the iteration and get the second final global best fitness and the second final global best position; otherwise, continue the iteration until until the time; the second final global optimal fitness is used as the current optimized raw material deterioration rate data; when the current optimized raw material deterioration rate data is less than the current raw material deterioration rate threshold, the position components of the second final global optimal position in the dimensions of the current storage raw material component content influencing factor initial data set and the current storage component content initial detection frequency data set are respectively used as the current storage raw material component content influencing factor final data set and the current storage component content final detection frequency data set.

[0137] Example 2

[0138] See also Figure 4 This embodiment discloses a system for measuring the content of raw materials. The system can implement the method of the above embodiment and includes a raw material component type setting module, a raw material component content influencing factor setting module, a raw material storage deterioration rate mapping equation construction module, a current storage raw material sample deterioration rate data mapping module, and a current storage component detection frequency factor adjustment module.

[0139] The raw material component type setting module sets several raw material component types to obtain a raw material component type set;

[0140] The raw material content influencing factor setting module sets several types of storage environment influencing factors that have an impact on the raw material composition, and obtains a raw material content influencing factor type set;

[0141] The raw material storage deterioration rate mapping equation construction module cooperates with the raw material component type set and the raw material component content influencing factor type set to collect the component content measurement frequency corresponding to the raw material component content measurement process in history, the deterioration rate at the end of raw material storage, the various types of component content influencing factor data during the raw material storage process, and the raw material storage time data to construct a final raw material storage deterioration rate mapping equation;

[0142] The deterioration rate data mapping module for the currently stored raw material samples to be tested collects data on factors affecting the content of various types of raw materials, storage time, and frequency data for testing the content of various types of components during the storage of multiple currently stored raw material samples to be tested, and inputs the data into the final raw material storage deterioration rate mapping equation for mapping, thereby obtaining the current raw material initial deterioration rate data;

[0143] The current storage component detection frequency factor adjustment module adjusts the current storage component content initial detection frequency data set and the current storage raw material component content influencing factor initial data set according to the current raw material initial deterioration rate data to obtain the current storage raw material component content influencing factor final data set and the current storage component content final detection frequency data set.

[0144] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0145] Example 3

[0146] This embodiment discloses a dendrobium extraction method, comprising the following steps:

[0147] S51, collecting dendrobium raw materials, and measuring the content of components of the dendrobium raw materials using the method of the above embodiment; when the component content measurement results show that the dendrobium raw materials have deteriorated, replacing other dendrobium raw materials for extraction;

[0148] S52, crushing the dendrobium raw material with a tissue crusher, and then refluxing with an organic solvent such as petroleum ether, acetone or ethyl acetate to volatilize and remove the organic solvent to obtain dendrobium residues;

[0149] S53, weighing the dendrobium residue, placing it in an extraction and concentration unit, adding distilled water to extract 3-5 times, and putting the extract obtained after each extraction into a concentration kettle for concentration to obtain a dendrobium concentrate;

[0150] S54, taking the dendrobium concentrate, adding ethanol and letting it stand, then centrifuging, and collecting the precipitate after the centrifugation to obtain a dendrobium precipitate;

[0151] S55. Dissolving the dendrobium precipitate in distilled water to obtain a dendrobium extract solution, centrifuging the dendrobium extract solution to remove protein, and collecting the centrifugal supernatant; concentrating the centrifugal supernatant using an ultrafiltration membrane while removing small molecular impurities such as monosaccharides and oligosaccharides, and collecting the dendrobium extract.

[0152] The preferred embodiments of the invention disclosed above are intended only to help illustrate the invention. These preferred embodiments do not exhaust all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for measuring the content of raw material components, characterized in that: The following steps are involved: S1. The raw material component type setting module sets several raw material component types to obtain a raw material component type set; sets several storage environment influencing factor types that have an impact on the raw material components to obtain a raw material component content influencing factor type set; S2. Based on the raw material component type set and the raw material component content influencing factor type set, the component content measurement frequency corresponding to the raw material component content measurement in history, the raw material deterioration rate at the end of storage, the various types of component content influencing factors during the raw material storage process, and the raw material storage time data are collected to construct a final raw material storage deterioration rate mapping equation; S3. Collecting data on factors affecting the content of various types of raw materials, storage time, and frequency of testing the content of various types of components during the storage of multiple raw material samples to be tested, and inputting these data into a final raw material storage deterioration rate mapping equation for mapping to obtain initial deterioration rate data of the current raw materials; S4. Adjust the current storage component content initial detection frequency dataset and the current storage raw material component content influencing factor initial dataset based on the current raw material initial deterioration rate data to obtain the current storage raw material component content influencing factor final dataset and the current storage component content final detection frequency dataset.

2. The method for measuring the content of raw material components according to claim 1, characterized in that: Said S1 comprises the following steps: S11. Setting several raw material component types to obtain a raw material component type set; setting several storage environment influencing factor types that have an impact on the component content corresponding to each raw material component type in the raw material component type set to obtain a raw material component content influencing factor type set; the raw material includes dendrobium; S12. Set a number of raw material samples to be measured to obtain a set of raw material samples to be measured.

3. The method for measuring the content of raw material components according to claim 2, characterized in that: The S2 comprises the following steps: S21. Collecting, in conjunction with the raw material component type set and the raw material component content influencing factor type set, component content measurement frequencies corresponding to several groups of historical raw material component content measurements, raw material deterioration rates at the end of raw material storage, various types of component content influencing factors during raw material storage, and raw material storage time data, to obtain a historical raw material component content measurement frequency data matrix, a historical raw material deterioration rate data set at the end of raw material storage, a historical raw material storage component content influencing factor data matrix, and a historical raw material storage time data set; S22. Constructing a final raw material storage deterioration rate mapping equation using the historical raw material component content measurement frequency data matrix, the historical raw material storage end deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set.

4. The method for measuring the content of raw material components according to claim 3, characterized in that: The S22 includes the following steps: S221, constructing an initial raw material storage deterioration rate mapping equation based on the historical raw material component content measurement frequency data matrix, the historical raw material end storage deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set; S222, substituting the historical raw material component content measurement frequency data matrix, the historical raw material storage end deterioration rate data set, the historical raw material storage component content influencing factor data matrix, and the historical raw material storage time data set for each set of data on the process of measuring the component content of the raw materials in history into the initial raw material storage deterioration rate mapping equation for mapping, thereby obtaining an initial mapping data set of the historical raw material storage deterioration rate; S223, setting a raw material storage deterioration rate mapping error threshold; calculating the error data between the historical raw material storage deterioration rate initial mapping data set and the historical raw material storage end deterioration rate data set to obtain the historical raw material storage deterioration rate initial mapping error data; When the initial mapping error data of the historical raw material storage deterioration rate is greater than or equal to the raw material storage deterioration rate mapping error threshold, the initial raw material storage deterioration rate mapping equation is adjusted until the initial mapping error data of the historical raw material storage deterioration rate is less than the raw material storage deterioration rate mapping error threshold, and the final raw material storage deterioration rate mapping equation is obtained; otherwise, there is no need to adjust the initial raw material storage deterioration rate mapping equation, and the initial raw material storage deterioration rate mapping equation is used as the final raw material storage deterioration rate mapping equation.

5. The method for measuring the content of raw material components according to claim 4, characterized in that: In S223, the initial raw material storage deterioration rate mapping equation is adjusted using a slime mold optimization algorithm.

6. The method for measuring the content of raw material components according to claim 5, characterized in that: The S3 includes the following steps: S31. During storage of each of the raw material samples to be tested in the current raw material sample set, presetting data on various types of raw material component content influencing factors, storage time, and frequency data for testing various types of component contents in the storage environment in conjunction with the raw material component content influencing factor type set, thereby obtaining an initial dataset of current storage raw material component content influencing factors, current storage time data, and an initial dataset of current storage component content testing frequencies; S32: Input the initial data set of factors affecting the current storage raw material component content, the current storage time data, and the initial detection frequency data set of the current storage component content into the final raw material storage deterioration rate mapping equation for mapping to obtain the current raw material initial deterioration rate data.

7. The method for measuring the content of raw material components according to claim 6, characterized in that: The S4 comprises the following steps: S41, setting a current raw material deterioration rate threshold; S42. When the initial deterioration rate data of the current raw material is greater than or equal to the current raw material deterioration rate threshold, the initial data set of factors affecting the current storage raw material component content and the initial detection frequency data set of the current storage component content are adjusted until the initial deterioration rate data of the current raw material is less than the current raw material deterioration rate threshold, thereby obtaining the final data set of factors affecting the current storage raw material component content and the final detection frequency data set of the current storage component content.

8. The method for measuring the content of raw material components according to claim 7, characterized in that: Adjusting the initial dataset of factors influencing the current storage raw material content and the initial dataset of detection frequencies of the current storage component content in S42 includes the following steps: S421, respectively setting the value interval of each influencing factor data in the initial data set of factors affecting the current storage raw material content and each detection frequency data in the initial detection frequency data set of the current storage component content, to obtain a value interval set of the current component content influencing factor and a value interval set of the current component content detection frequency; constructing a slime mold population for adjusting the detection frequency of the current raw material storage influencing factor; setting the maximum number of iterations of the slime mold population for adjusting the detection frequency of the current raw material storage influencing factor to And the current number of iterations is They are recorded as the maximum number of iterations of factor frequency and the current number of iterations of factor frequency respectively; S422. Generate an initial position matrix for each slime mold in the slime mold population based on the current raw material storage influencing factor detection frequency adjustment according to the current component content influencing factor value interval set and the current component content detection frequency value interval set, to obtain a second initial position matrix set; S423, constructing a fitness function for adjusting the slime mold population by detecting the frequency of the current raw material storage influencing factors; S424. Iteration begins; during each iteration, the fitness function of the slime mold population adjusted by the current raw material storage influencing factor detection frequency is used to calculate the fitness value of the position matrix of each slime mold in the slime mold population adjusted by the current raw material storage influencing factor detection frequency, which was updated during the previous iteration, and the position matrix of each slime mold in the slime mold population adjusted by the current raw material storage influencing factor detection frequency, which was updated during the previous iteration, is updated; S425, when When , stop the iteration and get the second final global best fitness and the second final global best position; otherwise, continue the iteration until until the time; the second final global optimal fitness is used as the current optimized raw material deterioration rate data; when the current optimized raw material deterioration rate data is less than the current raw material deterioration rate threshold, the position components of the second final global optimal position in the dimensions of the current storage raw material component content influencing factor initial data set and the current storage component content initial detection frequency data set are respectively used as the current storage raw material component content influencing factor final data set and the current storage component content final detection frequency data set.

9. A system for implementing the method for measuring the content of raw material components according to any one of claims 1 to 8.

10. A method for extracting dendrobium, comprising measuring the content of ingredients in a dendrobium raw material, characterized in that: The content of the raw material component of the dendrobium is measured using the raw material component content measurement method as described in any one of claims 1 to 8.

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