Syndrome Differentiation Analysis Method and System Based on the Principles of Yin-Yang and Five Elements in Traditional Chinese Medicine

Abstract

The present invention relates to a syndrome differentiation analysis method and system based on the principles of traditional Chinese medicine's yin-yang and five elements, comprising: inputting first data and second data, where the first data is discrete numerical values in the frequency domain or continuous signals in the time domain; the second data is discrete numerical values in the frequency domain; through time-frequency conversion or frequency-time conversion, the first data and the second data are uniformly converted into frequency-domain data or time-domain data; superimposing the frequency-domain data or time-domain data to obtain a first result; based on the first result, according to the entropy value formula, obtaining an overall information entropy characterizing the degree of chaos of the human body's disease information. The beneficial effects of the present invention are: quantifying the complex logic of yin-yang and five elements to assist users in objectively evaluating.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly relates to a syndrome differentiation analysis method and system based on the principles of traditional Chinese medicine's yin-yang and five elements. Background Art

[0002] The theory of yin-yang and five elements is an important theoretical basis and thinking logic in traditional Chinese medicine, used to explain the symptoms of various diseases. For example, yin deficiency can cause symptoms such as dry throat, dry mouth, and night sweats, while yang deficiency can cause symptoms such as fatigue and shortness of breath. Many diseases can cause yin-yang imbalance. By combining yin, yang, excess, and deficiency in pairs, it can be divided into four manifestations: yang excess with yin excess, yang deficiency with yin excess, yin deficiency with yang excess, and yin-yang deficiency. Different manifestations are treated with different treatment methods, which have been recognized by the general public for thousands of years and have helped countless patients relieve their pain.

[0003] So far, clinical judgment still has to rely on experienced traditional Chinese medicine doctors, and there are few instruments that can assist doctors in diagnosis, that is, there is a lack of quantitative data for doctors to make objective evaluations. Summary of the Invention

[0004] An object of the present invention is to at least solve one of the technical problems existing in the prior art, and provide a syndrome differentiation analysis method and system based on the principles of traditional Chinese medicine's yin-yang and five elements to assist users in making objective evaluations.

[0005] The technical solution of the present invention includes a syndrome differentiation analysis method and system based on the principles of traditional Chinese medicine's yin-yang and five elements. The syndrome differentiation analysis method based on the principles of traditional Chinese medicine's yin-yang and five elements includes: inputting first data and second data, where the first data is the correlation probability between the human body's vital signs and yin-yang, and the first data is discrete values in the frequency domain or continuous signals in the time domain; the second data is the correlation probability between diseases and the five elements, and the second data is discrete values in the frequency domain; based on the first data and the second data, through time-frequency conversion or frequency-time conversion, the first data and the second data are uniformly converted into frequency-domain data or time-domain data; superimposing the frequency-domain data or the time-domain data to obtain a first result; based on the first result, according to the entropy formula, the overall information entropy in the time domain or frequency domain is obtained, and the overall information entropy represents the degree of chaos of the human body's disease information.

[0006] According to the syndrome differentiation analysis method based on the principles of traditional Chinese medicine's yin-yang and five elements, it further includes: constructing a yin-yang orthogonal complex plane based on the cold, heat, deficiency, and excess of yin-yang; obtaining the information entropy of each component in the first data based on the projection of the first data on the yin-yang orthogonal complex plane; obtaining the information entropy of each component in the second data based on the projection of the second data on the yin-yang orthogonal complex plane.

[0007] According to the dialectical analysis method based on the principles of traditional Chinese medicine's yin-yang and five elements, the first correlation probability is a signal continuous in the time domain. The obtaining of the information entropy of each component in the first data includes: after performing time-frequency conversion on the first data, projecting it onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain a second result; based on the second result, according to the entropy value formula, obtaining the information entropy of the yin and yang components in the frequency domain.

[0008] According to the dialectical analysis method based on the principles of traditional Chinese medicine's yin-yang and five elements, the first correlation probability is a discrete value in the frequency domain. The obtaining of the information entropy of each component in the first data includes: after performing frequency-time conversion on the first data, projecting it onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain a third result; based on the third result, according to the entropy value formula, obtaining the information entropy of the yin and yang components in the time domain.

[0009] According to the dialectical analysis method based on the principles of traditional Chinese medicine's yin-yang and five elements, the obtaining of the information entropy of each component in the second data includes: after performing frequency-time conversion on the second data and projecting it onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain a fourth result; based on the fourth result, according to the entropy value calculation formula, obtaining the information entropy of the five components of metal, wood, water, fire, and earth in the time domain.

[0010] The technical solution of the present invention further includes a dialectical analysis system based on the principles of traditional Chinese medicine's yin-yang and five elements, including: a first module for converting data in the time domain into data in the frequency domain, or converting data in the frequency domain into data in the time domain; a second module, connected to the first module, for performing frequency domain synthesis on the data in the frequency domain, or performing time domain synthesis on the data in the time domain; a third module, connected to the second module, for using the result of the frequency domain synthesis as a parameter of the entropy value calculation formula to calculate the overall information entropy.

[0011] The technical solution of the present invention further includes an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The characteristic is that when the processor executes the computer program, it implements any of the above method steps.

[0012] The technical solution of the present invention further includes a computer-readable storage medium storing a computer program. The characteristic is that when the computer program is executed by a processor, it implements any of the above method steps.

[0013] The beneficial effect of the present invention is that users can obtain customized dialectical analysis results based on the information entropy on the yin-yang orthogonal complex plane, quantify the complex logic of yin-yang and five elements, and assist users in making objective evaluations. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the drawings and embodiments;

[0015] Figure 1 Shown is a block diagram of an embodiment of a system environment according to an embodiment of the present invention;

[0016] Figure 2 Shown is a flowchart according to an embodiment of the present invention;

[0017] Figure 3 Shown is a detailed flowchart according to an embodiment of the present invention;

[0018] Figure 4 Shown is a schematic diagram according to an embodiment of the present invention;

[0019] Figure 5 Shown is a system structure diagram according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the drawings. The role of the drawings is to supplement the description of the text part of the specification, enabling people to intuitively and vividly understand each technical feature and the overall technical solution of the present invention. However, it should not be construed as a limitation on the protection scope of the present invention.

[0021] In the description of the present invention, unless otherwise clearly defined, words such as "set" should be understood in a broad sense. Those skilled in the art can reasonably determine the specific meaning of the above words in the present invention in combination with the specific content of the technical solution.

[0022] As Figure 1 shown, the server may include one or more computing, storage, web, application, and / or other processing servers. The server may be located in one or more different data centers. The server may be instructed to collect disease information, statistically calculate the correlation probability between disease information and yin-yang and five elements, calculate entropy values, etc. At least a part of the processing work performed by the server may include hosting and / or executing processing associated with end-user requests. For example, providing the requested data / content to a computing system. Users access the services provided by one or more servers. For example, when a user inputs a certain case of disease information, at least a part of the processing work performed by the server may include hosting and / or executing processing associated with end-user requests and returning component entropy or overall information entropy. Examples of users include personal computers, laptop computers, tablet computers, mobile devices, display devices, user input devices, and any other computing devices.

[0023] Although only a limited number of component instances are shown for simplicity of the diagram, additional instances of any of the components shown in Figure 1 may exist. Components not shown in Figure 1 may also exist. The components shown communicate with each other over a network. Examples of networks include one or more of the following: direct or indirect physical communication connections, mobile communication networks, the Internet, intranets, local area networks, wide area networks, storage area networks, and any other form that connects two or more systems, components, or storage devices together.

[0024] As Figure 2 shown, a syndrome differentiation analysis method based on the principles of yin-yang and five elements in traditional Chinese medicine includes the following steps:

[0025] S100, input a first data and a second data, the first data being a first correlation probability characterizing the human body's vital signs and yin-yang, the first correlation probability being a discrete value in the frequency domain or a continuous signal in the time domain; the second data being a second correlation probability characterizing the disease and the five elements, the second correlation probability being a discrete value in the frequency domain;

[0026] S200, based on the first data and the second data, through time-frequency conversion or frequency-time conversion, uniformly convert the first data and the second data into frequency-domain data or time-domain data; superimpose the frequency-domain data or the time-domain data to obtain a first result;

[0027] S300, based on the first result, according to the entropy formula, obtain the overall information entropy in the time domain or the frequency domain, the overall information entropy characterizing the degree of chaos of the human body's disease information.

[0028] Details of the implementation of the above steps are described in multiple embodiments below, as Figure 3 shown.

[0029] S100, input a first data and a second data, the first data being the correlation probability characterizing the human body's vital signs and yin-yang, the first data being a discrete value in the frequency domain or a continuous signal in the time domain; the second data being the correlation probability characterizing the disease and the five elements, the second data being a discrete value in the frequency domain, specifically including:

[0030] In traditional Chinese medicine theory, yin and yang are respectively: yin deficiency, yang deficiency, excessive yin, excessive yang; the five elements are respectively: metal, wood, water, fire, earth, and it is considered that any thing in the world moves and changes according to the laws of yin-yang and five elements, and yin-yang and five elements have the laws of generation and restriction, and all the disease information obtained based on the traditional Chinese medicine diagnosis methods of inspection, auscultation and olfaction, interrogation, and palpation belongs to yin-yang and five elements.

[0031] Human vital signs include continuous signals in the time domain collected by modern medical devices. For example, through electrocardiogram, pulse and other devices, continuous signals such as heartbeat and pulse in the time domain are obtained; human vital signs also include obtaining disease information through methods such as medical history taking. For example, pale complexion and low voice.

[0032] S110. When the first data is discrete values in the frequency domain, both the first data and the second data are discrete values in the frequency domain obtained based on the correlation probability table of disease information and yin-yang and five elements, specifically including:

[0033] S111. Collect disease information, and based on the knowledge graph or correlation table of disease information and yin-yang and five elements, statistically calculate the correlation probability of disease information and yin-yang and five elements to obtain the correlation probability table of disease information and yin-yang and five elements. Among them, disease information mainly includes oral symptom information and examination information. Oral information is mainly obtained through the methods of listening and asking, and examination information is mainly obtained through the methods of observation and palpation such as tongue diagnosis, inspection and pulse diagnosis. For example, the obtained disease information is: chest and hypochondrium distension and fullness, bitter taste in the mouth and dry throat, red eyes, yellow urine, and constipation.

[0034] (1) Convert the collected disease information into word vectors in a unified format. Disease information is generally text data and has different formats. To help the server understand complex semantics, the present invention converts disease information into word vectors in a unified format through a word segmentation algorithm. Specifically, the present invention divides disease information according to the word segmentation algorithm and converts disease information into a word vector composed of at least one basic disease vector. Among them, the basic disease is the smallest unit of disease description. For example, chest and hypochondrium, red eyes, and constipation can be used to construct a dictionary of disease descriptions based on the basic diseases, and disease information is segmented based on the dictionary. The process of word segmentation of disease information is a basic step in text processing. The present invention can use existing word segmentation algorithms to divide disease information. For example, the Jieba word segmentation algorithm is used to segment the following disease information:

[0035] Disease information: "chest and hypochondrium distension and fullness, bitter taste in the mouth and dry throat, red eyes, yellow urine, and constipation";

[0036] After word segmentation: [[chest and hypochondrium distension and fullness], [bitter taste in the mouth and dry throat], [red eyes], [yellow urine], [constipation]]

[0037] (2) Taking the disease information as rows and the five elements of yin and yang as columns, construct a correlation probability table based on the disease information and the five elements of yin and yang. The correlation probability table includes at least one probability value; the disease information includes at least one basic disease vector. Let the first basic disease vector be any one of the basic disease vectors; the five elements of yin and yang include yin deficiency, yang deficiency, excessive yin, excessive yang, metal, wood, water, fire, and earth. The first object is any one of the five elements of yin and yang; the first basic disease vector and the first object determine the first probability value in the correlation probability table; the first probability value represents the probability that the disease in the first basic disease vector belongs to the first object.

[0038] Based on the knowledge graph or correlation table of disease information and the five elements of yin and yang, the probability value that a certain disease belongs to a specific object in the five elements of yin and yang can be queried. For example, suppose 1000 cases of disease information are collected. Among them, based on the knowledge graph or correlation table, it is obtained that there are 500 cases where red eyes belong to wood. Then the probability that red eyes belong to wood is 0.5. For example, as shown in Table 1, it is a statistical correlation probability table based on disease information and the five elements of yin and yang. It should be noted that the present invention is not limited to using the correlation probability table to describe the correlation between disease information and the five elements of yin and yang, and can also be in any form such as a graph, vector, etc.

[0039] S112, input the first disease information. Based on the correlation probability table of disease information and the five elements of yin and yang, obtain the correlation probability between the first disease information and yin and yang to get the first data, and obtain the yin and yang probability value corresponding to the first disease information to get the second data. Among them, the first disease information can be test data or disease information input by the user. For example, the first disease information is: [[chest and hypochondrium distension and oppression], [bitter taste in the mouth and dryness in the throat], [red eyes]]. Based on Table 1, the first data obtained is vector A 1 , where A 1 = [[a(1,6), a(1,7), a(1,8), a(1,9)], [a(2,6), a(2,7), a(2,8), a(2,9)], [a(3,6), a(3,7), a(3,8), a(3,9)]]; based on Table 1, the second data obtained is vector B, where B = [[a(1,1), a(1,2), a(1,3), a(1,4), a(1,5)], [a(2,1), a(2,2), a(2,3), a(2,4), a(2,5)], [a(3,1), a(3,2), a(3,3), a(3,4), a(3,5)]].

[0040] Table 1. Correlation table of disease information and the five elements of yin and yang

[0041]

[0042] S120. When the first data is a continuous signal in the time domain, the acquisition of the second data is as shown in steps S111 - S112. The acquisition of the first data includes: classifying the signal value corresponding to each moment in the continuous signal representing human vital signs in the time domain into the category of yin or yang to obtain a continuous probability function related to yin and yang.

[0043] In one embodiment, the acquisition of the first data includes: combining the collected continuous signal f(t) in the time domain representing human vital signs such as pulse and electrocardiogram with knowledge in the field of traditional Chinese medicine, such as technical means like knowledge graph reasoning or LSTM classification, etc., it can be classified into two categories of yin and yang. For each moment t of the signal f(t), its probability related to yin and yang can be obtained through reasoning or classification, denoted as g(t); then g(t) is the probability of the original signal f(t) related to yin and yang at moment t. Since the vital sign signal itself is continuous, the probability related to yin and yang is also continuous.

[0044] S200. Based on the first data and the second data, through time - frequency conversion or frequency - time conversion, the first data and the second data are uniformly converted into frequency - domain data or time - domain data; the frequency - domain data or time - domain data are superimposed to obtain a first result, specifically including:

[0045] S210. When the first data is discrete numerical values in the frequency domain, since the second data is also discrete numerical values in the frequency domain, the first data and the second data in the frequency domain are superimposed, and based on the superimposed calculation in the frequency domain, a vector C1 is obtained.

[0046] For example, based on Table 1, the first data A 1 = [[a(1,6), a(1,7), a(1,8), a(1,9)], [a(2,6), a(2,7), a(2,8), a(2,9)], [a(3,6), a(3,7), a(3,8), a(3,9)]]; the second data obtained based on Table 1 is the vector B = [[a(1,1), a(1,2), a(1,3), a(1,4), a(1,5)], [a(2,1), a(2,2), a(2,3), a(2,4), a(2,5)], [a(3,1), a(3,2), a(3,3), a(3,4), a(3,5)]]. Based on the superimposed calculation in the frequency domain, the vector C is obtained 1=[[a(1,6),a(1,7),a(1,8),a(1,9)],[a(2,6),a(2,7),a(2,8),a(2,9)],[a(3,6),a(3,7),a(3,8),a(3,9)],[a(1,1),a(1,2),a(1,3),a(1,4),a(1,5)],[a(2,1),a(2,2),a(2,3),a(2,4),a(2,5)],[a(3,1),a(3,2),a(3,3),a(3,4),a(3,5)]]。

[0047] S220. When the first data is a continuous signal in the time domain, since the second data is discrete values in the frequency domain, it is necessary to convert the first data and the second data into frequency domain data or time domain data through time-frequency conversion or frequency-time conversion, and superimpose the frequency domain data or time domain data to obtain the first result. Among them, time-frequency conversion can convert a continuous signal in the time domain into discrete values in the frequency domain. Time-frequency conversion can use methods such as Fourier transform and wavelet transform to convert the time domain signal into a frequency domain signal. Frequency-time conversion can use methods such as inverse Fourier transform and inverse wavelet transform to convert the frequency domain signal into a time domain signal. Specifically, it includes:

[0048] S221. Through time-frequency conversion, convert the first data into discrete values in the frequency domain to obtain vector A 2 , since the second data is also discrete values B in the frequency domain, for vector A in the frequency domain 2 and vector B are superimposed, and based on the superimposition calculation in the frequency domain, vector C is obtained 2 .

[0049] S222. Through frequency-time conversion, convert the second data into a continuous signal in the time domain to obtain the wave function b(t). Since the first data is also a continuous signal a 1 (t) in the time domain, for the continuous signal a 1 (t) and b(t) in the time domain are superimposed to obtain the wave function c(t).

[0050] S300. Based on the first result, according to the entropy value formula, obtain the overall information entropy in the time domain or frequency domain. The overall information entropy characterizes the degree of chaos of human disease information.

[0051] Traditional Chinese medicine theory believes that the human body is a closed-loop energy system, and its movement law conforms to the principle of entropy increase. The present invention uses the overall information entropy to characterize the degree of chaos of human disease information. The greater the overall information entropy, the greater the degree of chaos of human disease information.

[0052] S310. Based on the first result, according to the entropy value formula, obtain the overall information entropy in the frequency domain. Specifically, it includes:

[0053] S311. Use the superimposed result in the frequency domain as the input to the entropy formula to calculate the overall information entropy in the frequency domain. The superimposed result is a vector C ∈ {C 1 , C 2}. Suppose the size of C is M * N, then the overall information entropy H 1 is as shown in Formula 1.

[0054]

[0055] where c′ ij is the probability value of the i-th row and j-th column of the vector C, i = 1,... M, j = 1,... N.

[0056] S320. Based on the first result, obtain the overall information entropy in the time domain according to the entropy formula. Among them, in the existing technology, when calculating the information entropy of a continuous signal, methods such as approximate entropy, sample entropy, fuzzy entropy based on conditional probability, power spectrum entropy based on power spectrum calculation, singular spectrum entropy calculated based on singular value decomposition, and energy entropy calculated based on the energy of signal decomposition components can usually be used. The present invention can adopt any one of the calculation methods to calculate the overall information entropy H 2 in the time domain.

[0057] After step S300, it further includes:

[0058] S400. Based on the cold, heat, deficiency, and excess of yin and yang, construct a yin-yang orthogonal complex plane; based on the projection of the first data on the yin-yang orthogonal complex plane, obtain the information entropy of each component in the first data; based on the projection of the second data on the yin-yang orthogonal complex plane, obtain the information entropy of each component in the second data, specifically including:

[0059] S410. Obtain the information entropy of each component in the first data, including:

[0060] S411. When the first data is a continuous signal in the time domain, to obtain the information entropy of each component in the first data, it includes: after performing time-frequency conversion on the first data, project it onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain a second result; based on the second result, according to the entropy formula, obtain the information entropy of the yin and yang components in the frequency domain.

[0061] Traditional Chinese medicine theory believes that the changes of yin and yang are continuous, and the impact on the human body represents the dynamic change trajectory of physical energy. Yin and yang are in opposition and unity. The present invention uses a continuous complex variable space to describe the laws of yin and yang. For example, taking yang as the real axis and yin as the imaginary axis, the positive half-axis of the real axis is yang excess / strength, the negative half-axis is yang deficiency / weakness, the positive half-axis of the imaginary axis is yin excess / strength, and the negative half-axis is yin deficiency / weakness, to construct the yin-yang orthogonal complex plane.

[0062] For example, the first data is a continuous signal a in the time domain 1(t), the discrete function F[a 1 (t)] in the frequency domain is obtained through Fourier transform, as shown in formula (2);

[0063]

[0064] where F[a 1 (t)], as shown in formula (3), where Re(w) represents the real part of the yin-yang orthogonal complex plane, and Im(w) represents the imaginary part of the yin-yang orthogonal complex plane.

[0065] F[a 1 (t)] = Re 1 (w) + jIm 1 (w) (3)

[0066] The continuous signal a(t) in the time domain, after time-frequency conversion, is projected onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain the second result F[a 1 (t)], where Re 1 (w), Im 1 (w) are discrete numerical values. According to the entropy calculation formula (1), the information entropy of the real part and the imaginary part is calculated respectively to obtain the information entropy of the yin component The information entropy of the yang component is

[0067] S412. The first data is discrete numerical values in the frequency domain. The information entropy of each component in the first data is obtained, including: after frequency-time conversion of the first data, it is projected onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain the third result; based on the third result, according to the entropy formula, the information entropy of the yin and yang components in the time domain is obtained.

[0068] For example, the first data is the discrete numerical value A 1 , denoted as the function A' 1 (w), and the function a 2 (t) in the time domain is obtained through inverse Fourier transform, as shown in formula (4);

[0069]

[0070] The first data, after frequency-time conversion, is projected onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain the second result a 2 (t). The approximate entropy is calculated based on conditional probability, and the information entropy of the real part and the imaginary part is calculated respectively to obtain the information entropy of the yin component The information entropy of the yang component is As shown in Table 2.

[0071] S420. Obtain the information entropy of each component in the second data, including: project the second data onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane through frequency-time conversion to obtain a fourth result; based on the fourth result, obtain the information entropy of the five components of metal, wood, water, fire, and earth in the time domain according to the entropy value calculation formula.

[0072] For example, the second data is the discrete numerical value B in the frequency domain, where the vector B = [b 1 , b 2 , b 3 , b 4 , b 5 , where the vectors b 1 , b 2 , b 3 , b 4 , b 5 are the discrete numerical values of metal, wood, water, fire, and earth in the frequency domain respectively. As shown in formula (4), the vector b k is transformed into the function b' k (t) in the time domain through the inverse Fourier transform, where k = 1,..., 5; b k after frequency-time conversion, is projected onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain the fourth result b' k (t). For example, the approximate entropy is calculated based on conditional probability to calculate the information entropy of b' k (t), and the information entropy of the k-th component is obtained. Thus, the information entropy of the five components of metal, wood, water, fire, and earth in the time domain can be obtained, as shown in Table 2. In addition, through the mapping results of the components in the yin-yang orthogonal complex plane, the yin and yang entropy values of the components can be calculated respectively. For example, b k is projected onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain the fourth result b' k (t), and the approximate entropy of the real part and the imaginary part is calculated based on conditional probability to obtain the yin and yang entropy values of the k-th component.

[0073] Table 2. Information Entropy of the Five Elements of Yin and Yang

[0074]

[0075] S500. Obtain a customized dialectical analysis result based on the component information entropy on the yin-yang orthogonal complex plane; in one embodiment, the user can customize that the positive half-axis of the real axis in the yin-yang orthogonal complex plane represents heat, the negative half-axis represents cold, the positive half-axis of the imaginary axis represents excess, and the negative half-axis represents deficiency, as Figure 4 shown. For example, based on the information entropy calculated in step S400 for each component, as shown in Table 3, the entropy value vector of the five elements [0.15, 0.37, 0, 0, 0.48] and the entropy value vector of yin and yang [0.15, 0.27, 0.15, 0.37].

[0076] Table 3. Component Information Entropy of Yin-Yang and Five Elements

[0077] Gold Wood Water Fire Earth Yin + Yin - Yang + Yang - 0.15 0.37 0 0 0.48 0.15 0.27 0.15 0.37

[0078] According to the entropy values in Table 3, two effective vectors S1 = (Yang+) - (Yang-) = -0.22 and S2 = (Yin+) - (Yin-) = -0.12 can be calculated. It is obtained that (Yin-) is 0.12 and (Yang-) is 0.22, denoted as vector S = (-0.22, -0.12), or expressed in complex form as R(z = 0.75, θ = 181). Vector S or R is in the third quadrant of the Yin-Yang coordinate. Based on the user's definition that the third quadrant is for both Yin and Yang being virtual and representing deficiency-cold, the self-defined syndrome differentiation analysis result is "deficiency-cold".

[0079] In one embodiment, the user defines that the entropy value of the five-element components represents the degree of pathological chaos. The larger the entropy value, the higher the degree of pathological chaos. For example, the entropy value vector of the five-element components is [0.15, 0.37, 0, 0, 0.48], and it can be seen that the pathological chaos degree of earth is the largest.

[0080] In one embodiment, the user defines that the entropy value of the five-element components represents the degree of pathological chaos. The larger the entropy value, the higher the degree of pathological chaos. On the self-defined Yin-Yang orthogonal complex plane, the positive semi-axis of the real axis represents heat, the negative semi-axis represents cold, the positive semi-axis of the imaginary axis represents excess, and the negative semi-axis represents deficiency. Through the mapping results of each component in the five elements on the Yin-Yang orthogonal complex plane, the entropy value vectors of the Yin and Yang of the components are calculated respectively, and the Yin-Yang included angles of the components are obtained. For example, the entropy value vector of the five-element components is [0.4, 0.8, 0.5, 0.6, 0.5], and the Yin-Yang included angles of the five-element components are [35, 330, 25, 240, 50]. It can be seen that the pathological chaos degrees of wood and fire are the largest. The Yin-Yang included angle of wood is 330 degrees, in the fourth quadrant, representing yang excess, and the Yin-Yang included angle of fire is 240 degrees, in the third quadrant, representing deficiency-cold. That is, the syndrome differentiation result is obtained: wood - yang excess, fire - deficiency-fire.

[0081] The present invention takes the time-domain continuous or frequency-domain discrete data representing the human body's life characteristics as input. Through time-frequency or frequency-time conversion, the data is unified into data in the time domain or frequency domain. Through the corresponding entropy value formula, the overall information entropy representing the degree of chaos of the human body as a whole is obtained. Based on the nature of Yin and Yang, a Yin-Yang orthogonal complex plane is constructed, and each component of Yin-Yang and Five Elements is mapped onto this complex plane to obtain the component information entropy. The user can obtain the self-defined syndrome differentiation analysis result based on the component information entropy on the Yin-Yang orthogonal complex plane. The present invention quantifies the complex logic of Yin-Yang and Five Elements to assist the user in objective evaluation.

[0082] As Figure 5 shown, a syndrome differentiation analysis system based on the principles of traditional Chinese medicine's Yin-Yang and Five Elements includes:

[0083] The first module is used to convert data in the time domain into data in the frequency domain, or convert data in the frequency domain into data in the time domain;

[0084] The second module is connected to the first module and is used to perform frequency domain synthesis on data in the frequency domain, or perform time domain synthesis on data in the time domain;

[0085] The third module is connected to the second module and is used to use the result of the frequency domain synthesis as a parameter of the entropy value calculation formula to calculate the overall information entropy.

[0086] In one embodiment, the first data and the second data are input into the first module. The first module is connected to the second module to perform time-frequency conversion on the first data to obtain data in the time domain, or perform frequency-time conversion on the second data to obtain data in the frequency domain, and output the obtained conversion result; The second module is connected to the third module. The second module is used to perform time domain synthesis on the first data and the data in the time domain output by the first module, or perform frequency domain synthesis on the second data and the data in the frequency domain output by the first module, and output the synthesis result; The third module, which is connected to the second module, is used to use the output result of the second module as a parameter of the entropy value calculation formula to calculate the information entropy corresponding to each input quantity.

[0087] It should be recognized that the method steps in the embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or computer instructions stored in a non-transitory computer-readable memory. The method can use standard programming techniques. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if necessary, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose, the program can run on a dedicated integrated circuit programmed for this purpose.

[0088] In addition, the operations of the processes described herein can be performed in any suitable order, unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) can be executed under the control of one or more computer systems configured with executable instructions and can be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed jointly on one or more processors, by hardware, or a combination thereof. The computer program includes multiple instructions that can be executed by one or more processors.

[0089] Further, the method can be implemented in any type of computing platform operatively connected to a suitable one, including but not limited to personal computers, minicomputers, mainframes, workstations, network or distributed computing environments, separate or integrated computer platforms, or communicating with charged particle tools or other imaging devices, etc. Aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into the computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it can be read by a programmable computer and, when the storage medium or device is read by the computer, can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the above-described steps in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

[0090] A computer program can be applied to input data to perform the functions described herein, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the transformed data represents physical and tangible objects, including a specific visual depiction of the physical and tangible objects produced on a display.

[0091] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those of ordinary skill in the art.

Claims

1. A dialectical analysis method based on the principles of yin-yang and the five elements in traditional Chinese medicine, characterized in that, it includes: Input the first data and the second data, where the first data is the correlation probability between the human body's vital signs and yin-yang, and the first data is discrete values in the frequency domain or a continuous signal in the time domain; the second data is the correlation probability between diseases and the five elements, and the second data is discrete values in the frequency domain; Based on the first data and the second data, through time-frequency conversion or frequency-time conversion, uniformly convert the first data and the second data into frequency-domain data or time-domain data; superimpose the frequency-domain data or the time-domain data to obtain a first result; Based on the first result, according to the entropy formula, obtain the overall information entropy in the time domain or frequency domain, and the overall information entropy characterizes the degree of chaos of the human body's disease information; The method further includes: constructing a yin-yang orthogonal complex plane based on the cold, heat, deficiency, and excess of yin-yang; obtaining the information entropy of each component in the first data based on the projection of the first data on the yin-yang orthogonal complex plane; obtaining the information entropy of each component in the second data based on the projection of the second data on the yin-yang orthogonal complex plane, where the positive semi-axis of the real axis in the yin-yang orthogonal complex plane represents heat, the negative semi-axis represents cold, the positive semi-axis of the imaginary axis represents excess, and the negative semi-axis represents deficiency; When the first correlation probability is a continuous signal in the time domain, the obtaining of the information entropy of each component in the first data includes: after time-frequency conversion of the first data, project it onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain a second result; based on the second result, according to the entropy formula, obtain the information entropy of the yin and yang components in the frequency domain; When the first correlation probability is discrete values in the frequency domain, the obtaining of the information entropy of each component in the first data includes: after frequency-time conversion of the first data, project it onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain a third result; based on the third result, according to the entropy formula, obtain the information entropy of the yin and yang components in the time domain; The obtaining of the information entropy of each component in the second data includes: after frequency-time conversion of the second data, project it onto the real axis and the imaginary axis of the yin-yang orthogonal complex plane to obtain a fourth result; based on the fourth result, according to the entropy calculation formula, obtain the information entropy of the five components of metal, wood, water, fire, and earth in the time domain.

2. An electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the method steps described in claim 1.

3. A computer-readable storage medium, which stores a computer program, characterized in that, when the computer program is executed by a processor, it implements the method steps described in claim 1.

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