Ideal mathematical model construction method for quantity of prescription medicines recorded in traditional Chinese medicine prescription work and corresponding composition ratio of prescription medicines

Abstract

The invention discloses an ideal mathematical model construction method for the number of prescription medicines recorded in traditional Chinese medicine prescription work and the corresponding composition ratio of the prescription medicines, which is characterized by comprising the following steps of: respectively carrying out statistical analysis on the number of the prescription medicines in 'Five twelve disease prescriptions', 'Taiwan theory', 'Decorative Song' and 'Prescription (textbook)' and the composition ratio of the corresponding prescription medicines; constructing a histogram and a trend line; after a data distribution type is determined based on a histogram and a trend line, an ideal mathematical model is constructed. According to the method, the evolution law of the quantity of the prescription medicines and the corresponding composition ratio of the four traditional Chinese medicine prescriptions in different historical periods is studied. The quantity of different medicines of each work and the composition ratio of the corresponding prescription accord with the law of F distribution, near normal distribution or normal distribution of statistics. A standard normal distribution mathematical model constructed by the method can be used for predicting the number of different medicines and the composition ratio of the corresponding prescriptions recorded in the future prescriptions work. The invention provides an important reference for recording prescriptions of different medicine quantities and clinical application.

Description

Technical Field

[0001] The present invention relates to a method for exploring the evolution law of the number of medicinal ingredients in prescriptions of classic Chinese medicine works in different historical periods and their corresponding prescription composition ratios, and constructing an ideal normal distribution mathematical model. The present invention relates to the fields of prescription science and mathematical statistics. Background Art

[0002] Marx believed that a science can only be truly perfect when it is successfully applied to mathematics. Mathematics is an extremely abstract subject, which provides the driving force for the development of various specific subjects. At the same time, the laws of many subjects can be found in mathematical theory. The theory of traditional Chinese medicine has been questioned by some contemporary Western medical scholars because of its simplicity and macroscopic nature. In recent years, with the rapid development of complex science and system science, the theory of traditional Chinese medicine and its value have gradually been recognized by people of insight. How to tell the story of traditional medicine in modern "scientific language" is an important measure of "making use of the past for the present".

[0003] The history of Chinese medicine prescriptions is long. People in primitive society first used single-flavor medicines. Later, in medical practice, people found that the combination of two or more medicines had better curative effects, so it gradually evolved into multi-flavor prescriptions. With the influence of medical practice and the prescription-making concepts of the Yellow Emperor's Classic of Internal Medicine in the Spring and Autumn Period, the "Seven Prescription Theory of Large Prescriptions, Small Prescriptions, Urgent Prescriptions, Slow Prescriptions, Odd Prescriptions, Even Prescriptions and Repeated (Complex) Prescriptions" was gradually formed. Influenced by the prescription-making concepts of "monarch, minister, assistant and messenger", the number of medicinal ingredients in prescriptions gradually increased.

[0004] Literature: The Fifty-two Prescriptions for Diseases[1] is a silk book unearthed from Mawangdui Tomb No. 3 of the Han Dynasty in Changsha. It was written around the 3rd century BC and contains more than 200 identifiable prescriptions. The book collects prescription documents from ancient times to the time of its writing and is also the earliest prescription document to date. The Treatise on Febrile and Miscellaneous Diseases (Guilin Ancient Edition)[2], compiled by the medical saint Zhang Zhongjing in the late Eastern Han Dynasty, was written between 200 and 210 AD and contains 322 prescriptions. The author "diligently sought ancient teachings and learned from many masters", and it is still regarded as a classic. The Song of Hair Perm by Wang Ang in the Qing Dynasty[3] was written in 1694 and contains a total of 303 prescriptions and supplementary prescriptions, which brings together many classic prescriptions. The Prescriptions (Textbook)[4], a contemporary medical textbook for colleges and universities, compiled by Xu Jiqun et al., was written in 1985 and contains a total of 422 prescriptions and supplementary prescriptions, which select the best prescriptions from ancient and modern times. It should be pointed out that although some prescriptions are repeated in the last three books, the four prescription books represent the high recognition of prescriptions by medical practitioners at the time of publication and do not affect the research on the evolution of the number of medicinal ingredients in prescriptions.

[0005] In recent years, many scholars have conducted beneficial explorations from the perspective of interdisciplinary studies. For example, Professor Xiang Zhenggui of Tsinghua University in China[5] began to explore the use of a three-stage screening algorithm to optimize Chinese medicine formulas 20 years ago; Jiang Junjie et al.[6] explored the use of a deep learning model based on a neural network to construct a Chinese medicine formula map, providing a reference for the rapid formulation and use of Chinese medicine; Chen Xuetong[7] et al. explored the use of disease target information and Chinese medicine target information to construct an association model, and then added the drug pair association degree to select Chinese medicine formulas that match the target disease through a genetic algorithm; Ma Jialin et al.[8] used an artificial intelligence pun theme model method to analyze the roles of "monarch, minister, assistant, and messenger", providing a reference for improving the monarch, minister, assistant, and messenger compatibility method of prescriptions. The above-mentioned artificial intelligence research on formula composition has promoted the modernization of prescription research, but these studies have not studied the laws of the number of medicinal ingredients in prescriptions. Many scholars have advocated that Chinese medicine should enter the precision medicine system as soon as possible[9], and the study of the mathematical laws contained in the number of medicinal ingredients in prescriptions is one of the ways for prescription science to move towards precision.

[0006] References

[0007] 1. Mawangdui Han Tomb Silk Manuscript Compilation Group. Fifty-two Disease Prescriptions, Cultural Relics Publishing House, Beijing: 1st edition, November 1979, 25-129.

[0008] 2. Zhang Zhongjing. Treatise on Febrile and Miscellaneous Diseases (Guilin Ancient Edition), Beijing: China Traditional Chinese Medicine Press, first edition in June 2014, 12th printing in January 2024. P: 25-186.

[0009] 3. Wang Ang. Tangtou Gejue (portable edition), China Health Media Group (China Medical Science and Technology Press), first edition in August 2016, tenth printing in June 2023. p1-139.

[0010] 4. Xu Jiqun, Wang Mianzhi. Traditional Chinese Medicine Formulae, Shanghai Science and Technology Press, 1st edition, June 1985, 59th printing, June 2022, 2-226.

[0011] 5.Xiang ZG.A 3-stage voting algorithm for mining optimal ingredientpattern of traditional Chinese medicine.Journal of Software,2003,14(11):1882-1890.

[0012] 6. Jiang Junjie, Yang Wei, Wu Hongli, et al. A method and system for analyzing the composition of traditional Chinese medicine. Invention Patent Gazette, application publication date July 19, 2024, application publication number: CN 118366686 A.

[0013] 7. Chen Xuetong, Wang Mingjuan, Guo Zihu, et al. Traditional Chinese medicine formula prediction system based on molecular dynamic synergy. Invention Patent Gazette 2024(19), application publication date 2024.03.29. Application publication number: CN 117789857A.

[0014] 8. Ma Jialin, Wang Zhaojun, Guo Hai. Analysis of the compatibility rules of monarch, minister, assistant and envoy in traditional Chinese medicine prescriptions based on pun theme model, Chinese Journal of Traditional Chinese Medicine Information, 2022, (29) 12: 23-29.

[0015] 9. Yuan Bing. Establishing an accurate state description system. Journal of Beijing University of Chinese Medicine, 2016, 39(3): 186-190. Summary of the invention

[0016] The purpose of the present invention is to select prescription classics from different historical periods, namely "Fifty-two Prescriptions for Diseases", "Treatise on Febrile and Miscellaneous Diseases", "Song Tou Ge Jue" and "Prescription Science (Textbook)", conduct mathematical statistics on the number of medicinal ingredients in the prescriptions in the classics, explore the evolution law of the number of medicinal ingredients in classic Chinese medicine prescriptions and their corresponding prescription composition ratios, and construct a mathematical model.

[0017] In order to achieve the above object, the technical solution of the present invention is to disclose a method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book, characterized in that it includes the following steps:

[0018] The number of medicinal ingredients in the prescriptions of "Fifty-two Disease Prescriptions", "Treatise on Febrile Diseases", "Song of Tangtou" and "Prescriptions (Textbook)" and their corresponding prescription composition ratios were statistically analyzed, and bar graphs and trend lines were constructed.

[0019] After determining the data distribution type based on the bar chart and trend line, an ideal mathematical model is constructed;

[0020] Preferably, when constructing a bar chart and a trend line;

[0021] The mean ± standard deviation of the medicinal flavors of each book’s prescription was calculated;

[0022] Construct bar charts and trend lines using prescriptions with different amounts of medicinal ingredients;

[0023] The 95% confidence interval was calculated using μ±1.96σ, where μ was the mean and σ was the standard deviation, to obtain the number of medicinal flavors and their corresponding composition ratios;

[0024] After discarding the prescriptions with smaller composition ratio, use histogram and trend line to compose the chart.

[0025] Preferably, the mean ± standard deviation of the flavors in "Fifty-two Prescriptions for Diseases" is (1.95 ± 1.09), the mean ± standard deviation of the flavors in "Treatise on Febrile Diseases" is (4.56 ± 2.01), the mean ± standard deviation of the flavors in "Song of Tangtou" is (7.06 ± 3.44), and the mean ± standard deviation of the flavors in "Prescriptions (Textbook)" is (6.95 ± 4.08);

[0026] When using μ±1.96σ to calculate the 95% confidence interval, "Fifty-two Prescriptions for Diseases", "Treatise on Febrile Diseases", "Song of Prescription Recipes" and "Prescription Theory (Textbook)" use the normal distribution law in statistics, that is, the coverage areas of μ±σ, μ±1.96σ, μ±2σ, μ±2.58σ and μ±3σ are 68.2%, 95%, 95.7%, 99% and 99.7% respectively, which is the composition ratio of the prescription corresponding to the number of medicinal ingredients.

[0027] Preferably, when constructing bar graphs and trend lines using prescriptions with different numbers of medicinal ingredients: "Fifty-two Prescriptions for Diseases" presents an "F distribution", "Treatise on Febrile Diseases" presents a "near-normal distribution", "Song of Soup Recipes" presents a "near-normal distribution", and "Prescription Studies (Textbook)" presents a "near-normal distribution".

[0028] Preferably, when using histograms and trend lines to compose graphs: "Fifty-two Prescriptions for Diseases" presents a "near normal distribution", "Treatise on Febrile Diseases" presents a "normal distribution", "Song of Prescriptions" presents a "near normal distribution", and "Pharmacopoeia (Textbook)" presents a "near normal distribution".

[0029] Preferably, with reference to "Pharmacopoeia (Textbook)" and the law of normal distribution, the ideal mathematical model constructed is a standard normal distribution mathematical model.

[0030] Preferably, the constructed standard normal distribution mathematical model is used for prediction, and there is:

[0031] It is predicted that the composition ratio of the prescription with 1 herb is the same as that of the prescription with 15 herbs, accounting for 0.1% of the total prescription;

[0032] and / or predict that the composition ratio of the 2-ingredient prescription is the same as that of the 14-ingredient prescription, accounting for 0.35% of the total prescription;

[0033] and / or predict that the composition ratio of the 3-ingredient prescription is the same as that of the 13-ingredient prescription, each accounting for 2% of the total prescription;

[0034] and / or predict that the composition ratio of the 4-ingredient prescription is the same as that of the 12-ingredient prescription, each accounting for 5% of the total prescription;

[0035] and / or predict that the composition ratio of the 5-ingredient prescription is the same as that of the 11-ingredient prescription, each accounting for 8.4% of the total prescription;

[0036] and / or predict that the composition ratio of the 6-ingredient prescription is the same as that of the 10-ingredient prescription, each accounting for 12.2% of the total prescription;

[0037] and / or predict that the composition ratio of the 7-ingredient prescription is the same as that of the 9-ingredient prescription, each accounting for 14.2% of the total prescription;

[0038] And / or predict that the composition ratio of the 8-flavor prescription accounts for 15.4% of the total prescription, which is the central axis of the standard normal distribution mathematical model.

[0039] Preferably, the constructed standard normal distribution mathematical model is used for prediction, and the predicted composition ratio of prescriptions with 1-15 medicinal ingredients accounts for 99.9% of the total prescriptions, and the composition ratio of prescriptions with ≥16 medicinal ingredients accounts for 0.1% of the total prescriptions.

[0040] Preferably, based on the prediction results of the constructed standard normal distribution mathematical model, the number of medicinal ingredients and their corresponding composition ratios in the future classic "Pharmacology" fluctuate around the numerical values ​​of the standard normal distribution mathematical model.

[0041] Preferably, based on the prediction results of the constructed standard normal distribution mathematical model, the number of medicinal ingredients and their corresponding composition ratios in the future classic "Pharmacology" will be infinitely close to the normal distribution value of the constructed standard normal distribution mathematical model, ultimately achieving the unification of the prescription efficacy with the number of medicinal ingredients and their corresponding composition ratio.

[0042] The present invention studies the evolution law of the number of medicinal ingredients and their corresponding composition ratios in four Chinese medicine prescriptions in different historical periods. The number of different medicinal ingredients in each book and the composition ratio of the corresponding prescriptions conform to the statistical "F distribution", "near normal distribution" or "normal distribution" law. The standard normal distribution mathematical model constructed in this way can predict the number of different medicinal ingredients and the composition ratio of the corresponding prescriptions included in future prescription books. The present invention provides an important reference for prescriptions with different numbers of medicinal ingredients and their clinical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It shows that the composition ratio of prescriptions in the "Fifty-two Prescriptions for Diseases" is "F distribution";

[0044] Figure 2 It shows that the composition ratio of 1 to 4 ingredients in the "Fifty-two Prescriptions for Diseases" is "nearly normal distribution";

[0045] Figure 3 It shows that the trend line of the composition ratio of prescriptions in Treatise on Febrile and Miscellaneous Diseases is "nearly normal distribution";

[0046] Figure 4 It shows that the composition ratio of the prescriptions of herbs 1-9 in Treatise on Febrile and Miscellaneous Diseases is "normally distributed";

[0047] Figure 5It shows that the composition ratio of all prescriptions in Wang Ang's "Tang Tou Ge Jue" is "nearly normal distribution";

[0048] Figure 6 It shows that the composition ratio of the first 14 herbs in Wang Ang's "Tang Tou Ge Jue" is "normally distributed";

[0049] Figure 7 It shows that the composition ratio of prescriptions in the “Pharmacopoeia” shows a “near normal distribution”;

[0050] Figure 8 It shows that the composition ratio of the 1-15 ingredients in the "Pharmacology of Pharmacopoeia" is "nearly normal distribution";

[0051] Fig. 9 The area distribution of each region of the standard normal distribution diagram is shown;

[0052] Fig.10 The mathematical model of the number of medicinal flavors and their corresponding composition ratios is illustrated. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0054] The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book disclosed in an embodiment of the present invention includes the following contents:

[0055] 1) Calculation of medicinal taste:

[0056] The number of medicinal ingredients in each prescription in the four books was calculated separately. The number of medicinal ingredients in the "52 Prescriptions for Diseases", "Treatise on Febrile and Miscellaneous Diseases" and "Songtou Gejue" was calculated based on all the medicinal ingredients mentioned in the prescription. The number of medicinal ingredients in the textbook "Prescriptions" was calculated based on the "composition" of the prescription. Ginger, dates, wine, vinegar, rice soup, etc. added later in the "Usage" were not included in the number of medicinal ingredients. The proportion of such prescriptions was not high and had no impact on the statistical results.

[0057] 2) Mathematical statistics methods:

[0058] Microsoft Excel was used to perform prescription means, standard errors, composition ratios (%), and t-tests.

[0059] 3) Mapping:

[0060] The number of medicinal ingredients in the four books and the composition ratio (%) of the corresponding prescriptions were respectively made into bar graphs and trend lines, and the statistical distribution type of the composition ratio was preliminarily determined. The 95% credible interval was calculated using x±1.96S, and the number of medicinal ingredients in the 95% credible interval and the composition ratio of other corresponding prescriptions were retained (the other 5% credible interval medicinal ingredients and prescriptions were discarded), and the histogram and trend line were constructed as "near normal distribution" or "normal distribution". The histogram and trend line of the composition ratio of the medicinal ingredients in the prescriptions were then re-made and determined to be "near normal distribution or normal distribution". According to the historical evolution of the number of medicinal ingredients in the prescriptions, an ideal normal distribution mathematical model was made, and the evolution of the number of medicinal ingredients in the prescriptions in the future was predicted based on the model.

[0061] IV) Results:

[0062] Mathematical statistics of "Fifty-two Prescriptions for Diseases": The original data of "Fifty-two Prescriptions for Diseases" was calculated. The minimum number of ingredients in a prescription is 1, the maximum number of ingredients in a prescription is 8 (there is no prescription with 6 ingredients), the mean number of ingredients in a prescription (μ) is 1.95, and the standard deviation (σ) is 1.09. The number of ingredients corresponds to the number of prescriptions and their composition ratio, see Table 1. Convert the data in this table into a bar chart and draw the trend line of the bar chart. This group of data shows a statistical "F-type distribution", see Figure 1 . Calculate the 95% confidence interval μ±1.96ó=1.95±1.96×1.09≈-0.19~4.09. According to the principle that the number of medicinal ingredients is an integer and cannot be a negative number, the decimal point is rounded off to get the value range of 1, 2, 3, and 4 medicinal ingredients. In this way, 5, 7, and 8 medicinal ingredients are discarded (note: there is no 6-ingredient prescription), and the histogram and trend line are drawn. See Figure 2 , at this time the graph is "near normal distribution". The composition ratios of 1-4 herbs and their corresponding prescriptions are 35.64%, 47.03%, 10.89%, and 2.97% respectively. The composition ratios of these four orders of magnitude herbs and prescriptions account for 96.53%>95%, which basically conforms to the "near normal distribution value".

[0063] Table 1 Statistics of the number of medicinal ingredients in the "Fifty-two Prescriptions for Diseases" corresponding to the number of prescriptions

[0064] Number of medicinal flavors Prescription quantity Composition ratio (%) 1 72 35.64 2 95 47.03 3 22 10.89 4 6 2.97 5 4 1.98 7 2 0.99 8 1 0.50 total 202 100

[0065] Mathematical statistics of prescriptions in Treatise on Febrile and Miscellaneous Diseases: The original data of Treatise on Febrile and Miscellaneous Diseases was calculated. The minimum number of prescriptions with the most ingredients is 1, and the maximum number of prescriptions with the most ingredients is 12. The mean number of ingredients in the prescriptions (μ) is 4.56, and the standard error (σ) is 2.01. The number of prescriptions corresponding to the number of ingredients and their composition ratio are shown in Table 2. The data of the number of ingredients and composition ratio in the table are converted into a bar chart, and the trend line of the bar chart is drawn, see Table 2. Figure 3Calculate the 95% confidence interval μ±1.96σ=4.56±1.96×2.01≈0.62~8.50. According to the principle that the number of medicinal ingredients is an integer and cannot be a negative number, the decimal point is rounded off. The value range of the number of medicinal ingredients is 1 to 9. In this way, 10 ingredients are discarded, and there is a 12-ingredient prescription (note: there is no 11-ingredient prescription). Draw a histogram and trend line, see Figure 4 At this time, the graph is in the shape of "normal distribution". The composition ratio of herbs 1 to 9 is 98.14%>95%, which is in line with the statistical normal distribution value.

[0066] Table 2 Statistics of Treatise on Febrile and Miscellaneous Diseases. Guilin Ancient Edition

[0067] Number of medicinal flavors Prescription quantity Composition ratio (%) 1 15 4.66 2 39 12.11 3 43 13.35 4 78 24.22 5 56 17.39 6 35 10.87 7 31 9.63 8 9 2.80 9 10 3.11 10 4 1.24 12 2 0.62 total 322 100

[0068] Mathematical statistics of Wang Ang's "Tangtou Gejue": Calculating the original data, the minimum number of medicinal ingredients in a prescription is 1, and the maximum number of medicinal ingredients in a prescription is 18. The mean number of medicinal ingredients in a prescription (μ) is 7.06, and the standard deviation (σ) is 3.44. The number of prescriptions corresponding to the number of medicinal ingredients and their composition ratio are shown in Table 3. The composition ratio (%) data corresponding to the number of medicinal ingredients in this table is converted into a bar chart, and the trend line of the bar chart is drawn. This group of data presents a statistical "near normal distribution", see Figure 5 Calculate the 95% confidence interval μ±1.96S, that is, 7.06±1.96×3.44≈0.32~13.80. According to the principle that the number of medicinal ingredients is an integer and cannot be a negative number, the decimal point is rounded off. The value range of the number of medicinal ingredients is 1 to 14. In this way, 15 to 18 medicinal prescriptions are discarded, and the histogram and trend line are drawn. See Figure 6 At this time, the graph is basically "normally distributed". The proportion of the 1-14 orders of magnitude of medicinal ingredients in the prescription accounts for 97.23%>95%, which is consistent with the normal distribution value.

[0069] Table 3 Table of the number of medicinal ingredients and the number of prescriptions in Wang Ang's "Tangtou Gejue"

[0070] Number of medicinal flavors Prescription quantity Composition ratio (%) 1 3 0.99 2 17 5.61 3 22 7.26 4 41 13.53 5 30 9.90 6 34 11.22 7 34 11.222 8 29 9.57 9 19 6.27 10 26 8.58 11 13 4.29 12 14 4.63 13 6 1.98 14 6 1.98 15 4 1.32 16 1 0.33 17 3 0.99 18 1 0.33 total 303 100

[0071] Mathematical statistics results of "Pharmacology (Textbook)": The original data of "Pharmacology (Textbook)" was calculated. The minimum number of medicinal ingredients in a prescription is 1, and the maximum number of medicinal ingredients in a prescription is 49. The mean number of medicinal ingredients in a prescription (μ) is 6.95, and the standard deviation (σ) is 4.08. The number of prescriptions corresponding to the number of medicinal ingredients and their composition ratio are shown in Table 4. The composition ratio values ​​corresponding to the number of medicinal ingredients in the table are converted into a bar graph, and the trend line of the bar graph is drawn. This group of data presents a statistical "near-normal distribution", see Figure 7. Calculate the 95% confidence interval μ±1.96σ=6.95±1.96×4.08≈-1.05~14.95. According to the principle that the number of medicinal ingredients is an integer and cannot be negative, the decimal point is rounded off. The value range of the number of medicinal ingredients is 1 to 15. In this way, 16, 17, 19, 23, 26, and 49 medicinal prescriptions, a total of 6 ingredients, are discarded. Draw a histogram and trend line, see Figure 8 At this time, the graph is "nearly normal distribution". The composition ratio of 1 to 15 herbs accounts for 97.38%>95%, which is in line with the normal distribution value.

[0072] Table 4 The number of medicinal ingredients and the number of prescriptions in "Pharmacology"

[0073] Number of medicinal flavors Prescription quantity Composition ratio% 1 1 0.24 2 21 4.98 3 46 10.9 4 59 13.98 5 51 12.09 6 39 9.24 7 45 10.66 8 46 10.90 9 23 5.45 10 36 8.53 11 13 3.08 12 16 3.79 13 7 1.66 14 3 0.71 15 5 1.18 16 4 0.95 17 3 0.71 19 1 0.24 23 1 0.24 26 1 0.24 49 1 0.24 total 422 100.00

[0074] Comparison of the number of medicinal ingredients in the prescriptions of the four books: According to the order of publication, the statistical difference test of the number of medicinal ingredients in the prescriptions was conducted. The average number of medicinal ingredients in the prescriptions of "Wu Er Bing Fang" was 1.95, and the average number of medicinal ingredients in each prescription of "Shang Han Za Bing Lun" was 4.56. During this period, the average number of medicinal ingredients in each prescription increased by more than 1 times, but the comparison of the original data between the two groups was t=1.523, P>0.05, and there was no significant statistical difference. The average number of medicinal ingredients in each prescription of "Tang Tou Ge Jue" was 7.06, which was significantly higher than the average number of medicinal ingredients in each prescription of "Shang Han Za Bing Lun". The comparison of the original data between the two groups was t=2.631, p<0.01, and there was a significant statistical difference. The average number of medicinal ingredients in each prescription of "Fang Ji Xue" was 6.95, which was similar to the number of medicinal ingredients in each prescription of "Tang Tou Ge Jue". The comparison of the original data between the two groups was t=0.684, P>0.05, and there was no significant statistical difference.

[0075] A mathematical model was established to predict the number and composition ratio of future prescriptions: the standard "normal distribution" stipulates that the coverage areas of μ±σ, μ±1.96σ, μ±2σ, μ±2.58σ and μ±3σ are 68.2%, 95%, 95.4%, 99% and 99.7% respectively. Fig. 9 According to the reference of the number of ingredients in the prescription of "Pharmacology (Textbook)", the range is mainly 1-15. Let the 8-ingredient prescription be the central axis (μ) of the normal distribution, and the remaining 14 ingredients are symmetrically distributed on both sides of the neutral axis, with 7 orders of magnitude on each side. When the standard deviation σ=2, the number of ingredients can be reasonably distributed to the integer area of ​​the coordinate X-axis.

[0076] The distribution of the number of medicinal flavors and their corresponding composition ratios in the μ±σ region: Among the number of medicinal flavors and their corresponding composition ratios in the set of μ±σ=8±2={6,7,8,9,10}, the composition ratio according to the "normal distribution" accounts for 68.2%. Referring to the trend lines of the composition ratio values ​​in "Pharmacology (Textbook)" and "Normal Distribution", repeated fitting finally determined that the number of 8 medicinal flavors and their corresponding composition ratio of 15.4% is the correct value. The composition ratio of 7 medicinal flavors and the symmetrical 9 medicinal flavors is the same, both 14.2%. The composition ratio of 6 medicinal flavors and the symmetrical 10 medicinal flavors is the same, both 12.2%. The total composition ratio of prescriptions in this region is [15.4%+(14.2%×2)+(12.2%×2)]=68.2%.

[0077] In the region [(μ±1.96σ)-(μ±σ)], the distribution of the number of medicinal flavors and their corresponding prescription composition ratio is: that is, [(μ±1.96σ)-(μ±σ)]=[(8±1.96×2)-(8±2)]=[(4.08~11.92)-(6~10)]. Therefore, taking integers and rounding off, the coverage area of ​​the medicinal flavors of 4, 5, 11, 12 and their corresponding prescription composition ratios is 95%-68.2%=26.80%. Repeated fitting finally determined that the prescription composition ratios of 4 herbs and symmetrical 12 herbs are the same, both 5%; the prescription composition ratios of 5 herbs and symmetrical 11 herbs are the same, both 8.4%. The total prescription composition ratio in this region is =[(5%×2)+(8.4%×2)]=26.8%.

[0078] In the area [(μ±2.58σ)-(μ±1.96σ)], the distribution of the number of medicinal ingredients and the ratio of prescription ingredients is: [(μ±2.58σ)-(μ±1.96σ)] = [(2.84~13.16)-(4.08~11.92)], rounded to the nearest integer, = {3,4,5,6,7,8,9,10,11,12,13}-{4,5,6,7,8,9,10,11,12,} = {3,13}. The composition ratio covers an area of ​​99%-95% = 4%. The proportion of the 3-flavor prescription and its symmetrical 13-flavor prescription is set to be the same, both 2%. The total composition ratio of the prescriptions in this area is (2%×2) = 4%.

[0079] The distribution of the number of medicinal ingredients and their composition ratio in the region [(μ±3σ)-(μ±2.58σ)] is as follows: [(μ±3σ)-(μ±2.58σ)]=[(8±3×2)-(8±2.58×2)]=[(2~14)-(2.84~13.16)]. Taking integers and rounding off, {2,3,4,5,6,7,8,9,10,11,12,13,14}-{3,4,,5,6,7,8,9,10,11,12,13}={2,14}. The coverage area of ​​the composition ratio is 99.7%-99%=0.7%. Assume that the composition ratio of the 2-ingredient prescription and the 14-ingredient prescription is the same, both of which are 0.35%. The total composition ratio of the prescriptions in this region is (0.35%×2)=0.7%.

[0080] The distribution of the number of remaining prescriptions and their composition ratios: The total area of ​​the normal distribution is 100%, 100%-99.7%=0.3%. Assuming that the composition ratios of 1, 15, and ≥16 are the same, the composition ratios are 0.3% / 3=0.1% respectively.

[0081] In summary, the number of medicinal flavors and their corresponding composition ratios are shown in Table 5. The histogram and trend line are constructed to obtain the mathematical model diagram, as shown in Fig. 9 As shown in the figure, this is a "standard normal distribution". According to this mathematical model, the number of medicinal flavors included in the classic Chinese medicine prescription books and their corresponding prescription composition ratios will be a standard "normal distribution", but this will take a long time, at least several hundred years or more.

[0082] Table 5 Mathematical model constructed (the number of medicinal ingredients in a Chinese medicine prescription and its corresponding composition ratio %)

[0083]

[0084] The histograms and trend lines of the above four works in the 95% credible interval all show that the area is "near-normal distribution or normal distribution", which is in line with the general distribution law of things. Which prescriptions should be selected into the classic works, the only criterion for the editor to screen must be clinical efficacy, and the number of medicinal ingredients in the prescription will not be considered! However, the composition ratio of the number of medicinal ingredients in these classic prescriptions conforms to the "normal distribution" law of mathematical statistics. We do not think it is a coincidence, but the objective efficacy fits the mathematical law! Based on this, the present invention has established an ideal "normal distribution" model. It is speculated that in the next few decades or hundreds of years, the average number of medicinal ingredients in the selected prescriptions in the classic "Pharmacology" will be infinitely close to Fig.10 According to the values ​​shown, in the future, the proportion of prescriptions with 1-15 herbs will be 99.7% in total, and the proportion of prescriptions with ≥16 herbs will be 0.1% in total.

Claims

1. A method for constructing an ideal mathematical model of the number of medicinal ingredients in a prescription and their corresponding composition ratios included in a Chinese medicine prescription book, characterized in that: The following steps are involved: The number of medicinal ingredients in the prescriptions of "Fifty-two Disease Prescriptions", "Treatise on Febrile Diseases", "Song of Tangtou" and "Prescriptions (Textbook)" and their corresponding prescription composition ratios were statistically analyzed, and bar graphs and trend lines were constructed. After determining the data distribution type based on the histogram and trend line, an ideal mathematical model is constructed.

2. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 1, characterized in that: When constructing histograms and trend lines: The mean ± standard deviation of the medicinal flavors in each book’s prescription was calculated; Construct bar charts and trend lines using prescriptions with different amounts of medicinal ingredients; The 95% confidence interval was calculated using μ±1.96σ, where μ was the mean and σ was the standard deviation, to obtain the composition ratio; After discarding the prescriptions with smaller composition ratio, use histogram and trend line to compose the chart.

3. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 2, characterized in that: The average ± standard deviation of the number of medicinal flavors in "Wuerbing Fang" is (1.95±1.09), the average ± standard deviation of the number of medicinal flavors in "Shanghan Lun" is (4.56±2.01), the average ± standard deviation of the number of medicinal flavors in "Tangtou Ge" is (7.06±3.44), and the average ± standard deviation of the number of medicinal flavors in "Fangqixue (Textbook)" is (6.95±4.08). When using μ±1.96σ to calculate the 95% confidence interval, "Fifty-two Prescriptions for Diseases", "Treatise on Febrile Diseases", "Song of Prescription Recipes" and "Prescription Theory (Textbook)" use the normal distribution law in statistics, that is, the coverage areas of μ±σ, μ±1.96σ, μ±2σ, μ±2.58σ and μ±3σ are 68.2%, 95%, 95.7%, 99% and 99.7% respectively, which is the composition ratio of the prescription corresponding to the number of medicinal ingredients.

4. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 3, characterized in that: When constructing bar charts and trend lines using prescriptions with different numbers of medicinal ingredients: "Fifty-two Prescriptions for Diseases" presents an "F distribution", "Treatise on Febrile Diseases" presents a "near-normal distribution", "Song of Prescriptions" presents a "near-normal distribution", and "Prescription Studies (Textbook)" presents a "near-normal distribution".

5. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 4, characterized in that: When using histograms and trend lines to compose graphs: "Fifty-two Prescriptions for Diseases" presents a "near normal distribution", "Treatise on Febrile Diseases" presents a "normal distribution", "Song of Prescriptions" presents a "near normal distribution", and "Pharmacology (Textbook)" presents a "normal distribution".

6. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 5, characterized in that: With reference to the "Pharmacology (Textbook)" and the law of normal distribution, the ideal mathematical model constructed is the standard normal distribution mathematical model.

7. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 6, characterized in that: The constructed standard normal distribution mathematical model is used for prediction, and we have: It is predicted that the composition ratio of the prescription with 1 herb is the same as that of the prescription with 15 herbs, accounting for 0.1% of the total prescription; and / or predict that the composition ratio of the 2-ingredient prescription is the same as that of the 14-ingredient prescription, accounting for 0.35% of the total prescription; and / or predict that the composition ratio of the 3-ingredient prescription is the same as that of the 13-ingredient prescription, each accounting for 2% of the total prescription; and / or predict that the composition ratio of the 4-ingredient prescription is the same as that of the 12-ingredient prescription, each accounting for 5% of the total prescription; and / or predict that the composition ratio of the 5-ingredient prescription is the same as that of the 11-ingredient prescription, each accounting for 8.4% of the total prescription; and / or predict that the composition ratio of the 6-ingredient prescription is the same as that of the 10-ingredient prescription, each accounting for 12.2% of the total prescription; and / or predict that the composition ratio of the 7-ingredient prescription is the same as that of the 9-ingredient prescription, each accounting for 14.2% of the total prescription; And / or predict that the composition ratio of the 8-flavor prescription accounts for 15.4% of the total prescription, which is the central axis of the standard normal distribution mathematical model.

8. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 7, characterized in that: The constructed standard normal distribution mathematical model was used for prediction, and it was predicted that the composition ratio of prescriptions with 1-15 medicinal ingredients accounted for 99.9% of the total prescriptions, and the composition ratio of prescriptions with ≥16 medicinal ingredients accounted for 0.1% of the total prescriptions.

9. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 8, characterized in that: Based on the prediction results of the constructed standard normal distribution mathematical model, the number of medicinal ingredients and their corresponding composition ratios in the future classic "Pharmacology" will fluctuate around the numerical values ​​of the standard normal distribution mathematical model.

10. The method for constructing an ideal mathematical model of the number of medicinal ingredients and their corresponding composition ratios included in a Chinese medicine prescription book as claimed in claim 9, characterized in that: Based on the prediction results of the constructed standard normal distribution mathematical model, the number of medicinal ingredients and their corresponding composition ratios in the future classic "Pharmacology" will be infinitely close to the normal distribution values ​​of the constructed standard normal distribution mathematical model, ultimately achieving the unification of the prescription efficacy with the number of medicinal ingredients and their corresponding composition ratios.

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