CN112269880B - Sweet text classification matching system based on linear function - Google Patents
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Abstract
The invention provides a sweet taste text classification matching system based on a linear function. Comprising the following steps: the acquisition module acquires sweet taste characteristic information and establishes a sweet taste characteristic vector set according to the sweet taste characteristic information; the classification module is used for establishing a linear function classification method, classifying the sweet feature vector set according to the linear function classification method, establishing a traditional Chinese medicine sweet feature vector set and a Western medicine sweet feature vector set, and combining the sweet feature vector sets into a sweet feature vector matching model; the computing module is used for establishing a TF-IDF algorithm, acquiring sweet taste text information to be matched, selecting sweet taste feature words and establishing a sweet taste feature vector set to be matched; and the matching module calculates the similarity between the sweet characteristic vector matching model and the sweet characteristic vector set to be matched through the Jacard similarity coefficient, and generates a matching report according to the similarity. According to the text information matching method, the text information can be accurately matched through the linear function classification method, the TF-IDF algorithm and the Jacquard similarity coefficient, and the accuracy of the whole matching process is improved.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a sweet taste text classification matching system based on a linear function.
Background
In common telephone, the nose smells the smell and the tongue tastes five flavors. The sour, sweet, bitter, spicy and salty taste information is transmitted by fine papillae densely distributed on the lingual surface, and taste cells called lingual buds are excited by the cerebral cortex taste center, and the feedback loop nerve humoral system completes the analysis activity of the whole taste, but when people eat, the mouth has peculiar smell or the people feel abnormal taste without eating the oral cavity, which often indicates that a certain disease is possibly obtained.
The existing medical matching means for realizing the sweet taste text information and the corresponding disease information usually comprise the steps of collecting sweet taste text through a clinician, and then performing operation selection on a computer through the clinician, but when the information is matched by the existing technical means, a large amount of information is often subjected to traversal matching, so that the consumed resources are large, the consumed time is long, and the improvement of the existing scheme is needed.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
In view of the above, the invention provides a sweet taste text classification matching system based on a linear function, which aims to solve the technical problem that the prior art cannot classify sweet taste text information through the linear function so as to reduce the resources consumed by data processing.
The technical scheme of the invention is realized as follows:
in one aspect, the present invention provides a linear function-based sweet taste text classification matching system, comprising:
the acquisition module is used for acquiring the sweet characteristic information and establishing a sweet characteristic vector set according to the sweet characteristic information;
the classification module is used for establishing a linear function classification method, classifying the sweet feature vector set according to the linear function classification method, establishing a traditional Chinese medicine sweet feature vector set and a Western medicine sweet feature vector set, and combining the traditional Chinese medicine sweet feature vector set and the Western medicine sweet feature vector set into a sweet feature vector matching model;
the computing module is used for establishing a TF-IDF algorithm, acquiring sweet taste text information to be matched, selecting sweet taste feature words from the sweet taste text information to be matched through the TF-IDF algorithm, and establishing a sweet taste feature vector set to be matched;
and the matching module is used for calculating the similarity between the sweet characteristic vector matching model and the sweet characteristic vector set to be matched through the Jacard similarity coefficient, and generating a matching report according to the similarity.
On the basis of the technical scheme, preferably, the acquisition module comprises a processing module and is used for acquiring the characteristic information of the sweet taste, wherein the characteristic information of the sweet taste is characteristic information of the accompanying symptoms of the sweet taste, a characteristic information integrity verification rule is established, the characteristic information of the accompanying symptoms of the sweet taste is verified according to the characteristic information integrity verification rule, and when verification is passed, a sweet taste characteristic vector set is established according to the characteristic information of the accompanying symptoms of the sweet taste.
On the basis of the above technical solution, preferably, the obtaining module includes an adding module, configured to obtain historical mouth-sweet accompanying symptom feature information, compare the historical mouth-sweet accompanying symptom feature information with mouth-sweet accompanying symptom feature information, screen out historical mouth-sweet accompanying symptom feature information that is not repeated, and add the historical mouth-sweet accompanying symptom feature information to the imported sweet feature vector set.
On the basis of the above technical solution, preferably, the classification module includes a classification calculation module, configured to establish a linear classification function, and set two classification categories: the method comprises the steps of establishing a traditional Chinese medicine sweet taste and a western medicine sweet taste by using a sweet taste feature vector set as a function vector and a classification category as a classification mark, utilizing a linear classification function, and combining the traditional Chinese medicine sweet taste feature vector set and the western medicine sweet taste feature vector set into a sweet taste feature vector matching model.
On the basis of the technical scheme, preferably, the calculation module comprises an algorithm module, wherein the algorithm module is used for establishing a TF-IDF algorithm, acquiring sweet taste text information to be matched, calculating word frequency of each word in the sweet taste text information to be matched through the TF-IDF algorithm, and taking the words with calculated word frequency as words to be screened.
On the basis of the technical scheme, preferably, the calculation module comprises a characteristic word processing module, a common word stock and a word frequency threshold are set, words to be screened are screened according to the common word stock, after common words are screened, the word frequency of the rest words to be screened is compared with the word frequency threshold, words to be screened meeting the word frequency threshold are selected to be used as sweet characteristic words, and a sweet characteristic vector set to be matched is established.
On the basis of the technical scheme, preferably, the matching module comprises a matching report generating module, wherein the matching report generating module is used for establishing Jacquard similarity coefficients, calculating the similarity between the sweet characteristic vector matching model and the sweet characteristic vector set to be matched through the Jacquard similarity coefficients, and generating a corresponding matching report according to the similarity.
Still further preferably, the linear function-based sweet text classification matching apparatus includes:
the acquisition unit is used for acquiring the characteristic information of sweet taste and the characteristic information of diseases, and respectively establishing a characteristic vector set of sweet taste and a characteristic vector set of diseases according to the characteristic information of sweet taste and the characteristic information of diseases;
the classification unit is used for establishing a linear function classification method, classifying the sweet feature vector set according to the linear function classification method, establishing a traditional Chinese medicine sweet feature vector set and a Western medicine sweet feature vector set, and combining the traditional Chinese medicine sweet feature vector set and the Western medicine sweet feature vector set into a sweet feature vector matching model;
the computing unit is used for establishing a TF-IDF algorithm, acquiring sweet taste text information to be matched, selecting sweet taste feature words from the sweet taste text information to be matched through the TF-IDF algorithm, and establishing a sweet taste feature vector set to be matched;
and the matching unit is used for calculating the similarity between the sweet characteristic vector matching model and the sweet characteristic vector set to be matched through the Jacard similarity coefficient, and generating a matching report according to the similarity.
Compared with the prior art, the sweet taste text classification matching system based on the linear function has the following beneficial effects:
(1) The method has the advantages that the accuracy of the extracted feature words can be improved by utilizing the linear function classification method and the TF-IDF algorithm to extract the feature words, the matching of subsequent information is facilitated, meanwhile, the feature vector set is classified by utilizing the linear function classification method, the resource consumption during information matching is greatly reduced, and the resource matching speed is improved;
(2) By calculating the similarity of the information text by using the Jacquard similarity coefficient, the accuracy of information matching can be improved, and meanwhile, the speed of information matching can be improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a first embodiment of the sweet text classification matching system based on a linear function of the present invention;
FIG. 2 is a block diagram of a second embodiment of a linear function based sweet text classification matching system of the present invention;
FIG. 3 is a block diagram of a third embodiment of a linear function based sweet text classification matching system of the present invention;
FIG. 4 is a block diagram of a fourth embodiment of a sweet taste text classification matching system based on a linear function of the present invention;
FIG. 5 is a block diagram of a fifth embodiment of a linear function based sweet text classification matching system of the present invention;
fig. 6 is a block diagram of a linear function-based sweet text classification matching apparatus according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Referring to fig. 1, fig. 1 is a block diagram illustrating a first embodiment of the sweet taste text classification matching system based on a linear function according to the present invention. The sweet taste text classification matching system based on the linear function comprises: an acquisition module 10, a classification module 20, a calculation module 30 and a matching module 40.
The acquisition module 10 is used for acquiring the sweet taste characteristic information and establishing a sweet taste characteristic vector set according to the sweet taste characteristic information;
the classification module 20 is configured to establish a linear function classification method, classify the sweet feature vector set according to the linear function classification method, establish a traditional Chinese medicine sweet feature vector set and a western medicine sweet feature vector set, and combine the traditional Chinese medicine sweet feature vector set and the western medicine sweet feature vector set into a sweet feature vector matching model;
the computing module 30 is configured to establish a TF-IDF algorithm, obtain the mouth-sweet text information to be matched, select a mouth-sweet feature word from the mouth-sweet text information to be matched through the TF-IDF algorithm, and establish a mouth-sweet feature vector set to be matched;
and the matching module 40 is configured to calculate a similarity between the sweet taste feature vector matching model and the sweet taste feature vector set to be matched according to the jaccard similarity coefficient, and generate a matching report according to the similarity.
Further, as shown in fig. 2, a structural block diagram of a second embodiment of the sweet taste text classification matching system based on a linear function according to the present invention is proposed based on the above embodiments, and in this embodiment, the obtaining module 10 further includes:
the processing module 101 is configured to obtain the characteristic information of sweet taste, where the characteristic information of sweet taste is characteristic information of sweet taste accompanying symptoms, establish a characteristic information integrity verification rule, verify the characteristic information of sweet taste accompanying symptoms according to the characteristic information integrity verification rule, and establish a characteristic vector set of sweet taste according to the characteristic information of sweet taste accompanying symptoms when verification passes.
The adding module 102 is configured to obtain historical mouth-sweet accompanying symptom characteristic information, compare the historical mouth-sweet accompanying symptom characteristic information with mouth-sweet accompanying symptom characteristic information, screen out historical mouth-sweet accompanying symptom characteristic information without repetition, and add the historical mouth-sweet accompanying symptom characteristic information to the imported sweet characteristic vector set.
It should be understood that, in this embodiment, the system may acquire the characteristic information of sweet taste, where the characteristic information of sweet taste is characteristic information of sweet taste accompanying symptoms, establish a characteristic information integrity verification rule, verify the characteristic information of sweet taste accompanying symptoms according to the characteristic information integrity verification rule, and when verification passes, establish a sweet taste characteristic vector set according to the characteristic information of sweet taste accompanying symptoms, which is to detect the characteristic words in advance, so as to ensure that the characteristic words can be directly matched when the information is matched, and not cause matching failure due to incomplete characteristic information.
It should be understood that sweet taste is generally accompanied by symptoms of dry mouth with little drinking water, shortness of breath, tiredness, poor appetite, distention of the abdomen of the Anhui, and soft dry stool. Since taste bud cells are all refreshed by surrounding epithelial cells, taste recovery is at least over 10 days. However, treatment must be found early and within one month after the onset of the taste disorder.
It should be understood that, in this embodiment, historical sweet taste symptom feature information is also taken, the historical sweet taste symptom feature information is compared with sweet taste symptom feature information, historical sweet taste symptom feature information without repetition is screened out, and the historical sweet taste symptom feature information is added to the imported sweet feature vector set, which is to further add the sweet taste feature vector set, so as to increase the reliability of information matching.
It should be understood that in this embodiment, all the disease and disease symptom characteristic information corresponding to the characteristic information of the accompanying symptoms of sweet taste is also obtained, and a vector set of the disease and disease symptom characteristic information corresponding to the characteristic information of the accompanying symptoms of sweet taste is established. For example, traditional Chinese medicine considers that the mouth sweetness is mostly caused by gastric dysfunction. Clinically, the sweet food is divided into spleen and stomach heat steaming mouth sweet food and spleen and stomach qi and yin mouth sweet food. The former is caused by excessive consumption of pungent and thick flavor, and internal heat or exogenous pathogenic heat accumulated in the spleen and stomach, which is often due to damp-heat in the spleen and stomach. Can be seen in patients with diabetes who have a sweet and hypertrophic taste with plain taste. The oral liquid is sweet and thirsty, is loved to drink water, is easy to hunger due to excessive eating, or has sore lips and tongue, dry stool, red tongue with dry coating, rapid and powerful pulse and the like; the latter is mainly caused by the injury of spleen and stomach due to the old or chronic diseases, qi and yin impairment, deficiency heat and endogenous production, and burning of spleen and body fluid, and is characterized by symptoms of sweet taste due to qi and yin of spleen and stomach, dry mouth, little drinking water, shortness of breath, tiredness, anorexia, abdominal distention, dry stool and the like.
Further, as shown in fig. 3, a structural block diagram of a third embodiment of the sweet taste text classification matching system based on a linear function according to the present invention is proposed based on the above embodiments, and in this embodiment, the classification module 20 further includes:
the classification calculation module 201 is configured to establish a linear classification function, and set two classification categories: the method comprises the steps of establishing a traditional Chinese medicine sweet taste and a western medicine sweet taste by using a sweet taste feature vector set as a function vector and a classification category as a classification mark, utilizing a linear classification function, and combining the traditional Chinese medicine sweet taste feature vector set and the western medicine sweet taste feature vector set into a sweet taste feature vector matching model.
It should be understood that in this example, a linear classification function is established, classifying the sweet taste into two categories according to the cause of the onset of sweet taste, and classifying the disease according to the characteristic information of symptoms. These two categories are: sweet taste of traditional Chinese medicine and sweet taste of western medicine (such as diabetes, etc.). Each sample consists of a vector (i.e., the vector of those text features) and a label (indicating which class this sample belongs to). Then, the classification category is used as a classification mark, a traditional Chinese medicine mouth sweetness characteristic vector set and a western medicine mouth sweetness characteristic vector set are established by utilizing a linear classification function, and the traditional Chinese medicine mouth sweetness characteristic vector set and the western medicine mouth sweetness characteristic vector set are combined into a mouth sweetness characteristic vector matching model
Further, as shown in fig. 4, a structural block diagram of a fourth embodiment of the sweet taste text classification matching system based on a linear function according to the present invention is proposed based on the above embodiments, and in this embodiment, the calculating module 30 includes:
the algorithm module 301 is configured to establish a TF-IDF algorithm, obtain the mouth sweet text information to be matched, calculate a word frequency of each word in the mouth sweet text information to be matched according to the TF-IDF algorithm, and use the word with the calculated word frequency as a word to be screened.
The feature word processing module 302 sets a common word stock and a word frequency threshold, screens words to be screened according to the common word stock, compares the word frequency of the remaining words to be screened with the word frequency threshold after screening the common words, selects the words to be screened meeting the word frequency threshold as sweet taste feature words, and establishes a sweet taste feature vector set to be matched.
It should be understood that in this embodiment, a TF-IDF algorithm is also established to obtain the sweet taste text information to be matched, the word frequency of each word in the sweet taste text information to be matched is calculated by the TF-IDF algorithm, and the word with the calculated word frequency is used as the word to be screened.
It should be appreciated that the main idea of TF-IDF is: if a word appears in one article with a high frequency TF and in other articles with few occurrences, the word or phrase is considered to have good category discrimination and is suitable for classification. The Term Frequency (TF) represents the frequency with which terms (keywords) appear in text. This number will typically be normalized (typically word frequency divided by the total number of articles) to prevent it from biasing toward long documents.
It should be understood that, in order to select the feature words, the system may also set a common word stock and a word frequency threshold, and screen the words to be screened according to the common word stock. The common word library comprises words such as continuous words, word and punctuation marks, after the common words are screened, the word frequency of the rest words to be screened is compared with a word frequency threshold value, the words to be screened meeting the word frequency threshold value are selected to be used as sweet taste feature words, and a sweet taste feature vector set to be matched is established.
Further, as shown in fig. 5, a structural block diagram of a fifth embodiment of the sweet taste text classification matching system based on a linear function according to the present invention is proposed based on the above embodiments, and in this embodiment, the matching module 40 includes:
the matching report generating module 401 is configured to establish a jaccard similarity coefficient, calculate a similarity between the sweet feature vector matching model and the sweet feature vector set to be matched according to the jaccard similarity coefficient, and generate a corresponding matching report according to the similarity.
It should be appreciated that the final system establishes a Jack-like coefficient by which the similarity between the sweet-mouth feature vector matching model and the set of sweet-mouth feature vectors to be matched is calculated while setting a corresponding similarity range, and then compares the calculated similarity to the similarity range, and finally generates a corresponding matching report, such as, if the mouth is sweet, typically caused by diabetes, or possibly caused by spleen and stomach dysfunction. Especially in the morning, the sensation is more pronounced. Even drinking boiled water can feel very sweet.
It should be noted that the foregoing is merely illustrative, and does not limit the technical solutions of the present application in any way.
As can be easily found from the above description, the present embodiment provides a sweet taste text classification matching system based on a linear function, including: the acquisition module is used for acquiring the sweet characteristic information and establishing a sweet characteristic vector set according to the sweet characteristic information; the classification module is used for establishing a linear function classification method, classifying the sweet feature vector set according to the linear function classification method, establishing a traditional Chinese medicine sweet feature vector set and a Western medicine sweet feature vector set, and combining the traditional Chinese medicine sweet feature vector set and the Western medicine sweet feature vector set into a sweet feature vector matching model; the computing module is used for establishing a TF-IDF algorithm, acquiring sweet taste text information to be matched, selecting sweet taste feature words from the sweet taste text information to be matched through the TF-IDF algorithm, and establishing a sweet taste feature vector set to be matched; and the matching module is used for calculating the similarity between the sweet characteristic vector matching model and the sweet characteristic vector set to be matched through the Jacard similarity coefficient, and generating a matching report according to the similarity. According to the text information matching method, the text information can be accurately matched through a linear function classification method, a TF-IDF algorithm and a Jacquard similarity coefficient, and accuracy of the whole matching process is improved.
In addition, the embodiment of the invention also provides a sweet taste text classification matching device based on a linear function. As shown in fig. 6, the sweet taste text classification matching apparatus based on a linear function includes: an acquisition unit 10, a classification unit 20, a calculation unit 30, and a matching unit 40.
An acquiring unit 10, configured to acquire sweet taste feature information and disease feature information, and respectively establish a sweet taste feature vector set and a disease feature vector set according to the sweet taste feature information and the disease feature information;
the classification unit 20 is configured to establish a linear function classification method, classify the sweet feature vector set according to the linear function classification method, establish a traditional Chinese medicine sweet feature vector set and a western medicine sweet feature vector set, and combine the traditional Chinese medicine sweet feature vector set and the western medicine sweet feature vector set into a sweet feature vector matching model;
the computing unit 30 is configured to establish a TF-IDF algorithm, obtain sweet taste text information to be matched, select a sweet taste feature word from the sweet taste text information to be matched through the TF-IDF algorithm, and establish a sweet taste feature vector set to be matched;
and a matching unit 40 for calculating the similarity between the sweet taste feature vector matching model and the sweet taste feature vector set to be matched by using the Jacard similarity coefficient, and generating a matching report according to the similarity.
In addition, it should be noted that the above embodiment of the apparatus is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select some or all modules according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details which are not described in detail in the present embodiment can be referred to the linear function-based sweet text classification matching system provided in any embodiment of the present invention, and are not described herein.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (5)
1. A linear function based sweet taste text classification matching system, the linear function based sweet taste text classification matching system comprising:
the acquisition module is used for acquiring the sweet characteristic information and establishing a sweet characteristic vector set according to the sweet characteristic information; the acquisition module comprises a processing module and a processing module, wherein the processing module is used for acquiring the characteristic information of the sweet taste, the characteristic information of the sweet taste is characteristic information of the sweet taste accompanying symptoms, a characteristic information integrity verification rule is established, the characteristic information of the sweet taste accompanying symptoms is verified according to the characteristic information integrity verification rule, and when verification passes, a sweet taste characteristic vector set is established according to the characteristic information of the sweet taste accompanying symptoms; the acquisition module comprises an adding module which is used for acquiring historical sweet taste symptom characteristic information, comparing the historical sweet taste symptom characteristic information with sweet taste symptom characteristic information, screening out the historical sweet taste symptom characteristic information which is not repeated, and adding the historical sweet taste symptom characteristic information into an imported sweet characteristic vector set;
the classification module is used for establishing a linear function classification method, classifying the sweet feature vector set according to the linear function classification method, establishing a traditional Chinese medicine sweet feature vector set and a Western medicine sweet feature vector set, and combining the traditional Chinese medicine sweet feature vector set and the Western medicine sweet feature vector set into a sweet feature vector matching model;
the computing module is used for establishing a TF-IDF algorithm, acquiring sweet taste text information to be matched, selecting sweet taste feature words from the sweet taste text information to be matched through the TF-IDF algorithm, and establishing a sweet taste feature vector set to be matched;
and the matching module is used for calculating the similarity between the sweet characteristic vector matching model and the sweet characteristic vector set to be matched through the Jacard similarity coefficient, and generating a matching report according to the similarity.
2. The linear function based sweet text classification matching system of claim 1, wherein: the classification module comprises a classification calculation module and is used for establishing a linear classification function and setting two classification categories: the method comprises the steps of establishing a traditional Chinese medicine sweet taste and a western medicine sweet taste by using a sweet taste feature vector set as a function vector and a classification category as a classification mark, utilizing a linear classification function, and combining the traditional Chinese medicine sweet taste feature vector set and the western medicine sweet taste feature vector set into a sweet taste feature vector matching model.
3. The linear function based sweet text classification matching system of claim 2, wherein: the computing module comprises an algorithm module which is used for establishing a TF-IDF algorithm, obtaining sweet taste text information to be matched, computing word frequency of each word in the sweet taste text information to be matched through the TF-IDF algorithm, and taking the words with the computed word frequency as words to be screened.
4. The linear function based sweet text classification matching system of claim 3, wherein: the computing module comprises a characteristic word processing module, a common word stock and a word frequency threshold are set, words to be screened are screened according to the common word stock, after the common words are screened, the word frequency of the rest words to be screened is compared with the word frequency threshold, the words to be screened meeting the word frequency threshold are selected to be used as sweet characteristic words, and a sweet characteristic vector set to be matched is established.
5. The linear function based sweet text classification matching system of claim 4, wherein: the matching module comprises a matching report generating module which is used for establishing Jacquard similarity coefficients, calculating the similarity between the sweet characteristic vector matching model and the sweet characteristic vector set to be matched through the Jacquard similarity coefficients, and generating a corresponding matching report according to the similarity.
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