AI teaching method based on traditional Chinese medicine culture and related device

Abstract

The invention discloses an AI teaching method based on traditional Chinese medicine culture and a related device. The method comprises the steps of obtaining a target traditional Chinese medicine course and a target teaching plan; obtaining a target traditional Chinese medicine database corresponding to the target traditional Chinese medicine course; constructing a target traditional Chinese medicine AI model according to the target traditional Chinese medicine database and the target teaching plan; obtaining a target medical problem and personal information; inputting the target medical problem and the personal information into the target traditional Chinese medicine AI model to obtain first reply content; obtaining first feedback content corresponding to the first reply content; determining whether the target object meets a first preset condition according to the first feedback content; when the target object meets a first preset condition, generating a medical cases based on the target medical problem; obtaining a feedback contents corresponding to the a medical cases; and when the a feedback contents meet a second preset condition, determining that the target object has mastered the traditional Chinese medicine knowledge corresponding to the target medical problem. According to the embodiment of the invention, the efficiency of traditional Chinese medicine teaching is improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to an AI teaching method and related devices based on traditional Chinese medicine culture. Background Art

[0002] As one of the important representatives of traditional culture, TCM culture covers many fields such as Chinese herbal medicine planting, processing, medicine trade, TCM diagnosis and treatment, etc. At present, the theory and experience of TCM often rely on master-apprentice inheritance and ancient books and documents. The dissemination and inheritance of knowledge have certain limitations. In addition, the theory and terminology of TCM are often obscure and difficult for ordinary people to understand, resulting in low efficiency of TCM teaching.

[0003] Therefore, how to improve the efficiency of TCM teaching has become an urgent problem to be solved. Summary of the invention

[0004] The embodiments of the present application provide an AI teaching method and related devices based on TCM culture, which improve the efficiency of TCM teaching.

[0005] In a first aspect, an embodiment of the present application provides an AI teaching method based on traditional Chinese medicine culture, the method comprising: Obtain a target TCM course for a target object and a target teaching plan for the target TCM course; the target TCM course includes one of the following: a Zhangshu TCM course, a Chinese medicine course, a TCM basic theory course, or an acupuncture and massage course; Acquire a target TCM database corresponding to the target TCM course; Constructing a TCM AI model according to the target TCM database and the target teaching plan to obtain a target TCM AI model; Obtaining target medical issues for the target TCM course and personal information of the target subject; Input the target medical question and the personal information into the target TCM AI model to obtain a first answer content; Obtaining first feedback content from the target object in response to the first reply content; Determining whether the target object meets a first preset condition according to the first feedback content; When the target object meets the first preset condition, a corresponding medical case is generated based on the target medical problem to obtain a medical case; a is an integer greater than 1; Obtaining feedback content of the target object for each of the a medical cases to obtain a feedback content; Detecting whether the a feedback contents meet a second preset condition; When the a pieces of feedback content meet the second preset condition, it is determined that the target object has mastered the traditional Chinese medicine knowledge corresponding to the target medical problem.

[0006] In a second aspect, the embodiment of the present application provides an AI teaching device based on traditional Chinese medicine culture, the device comprising: an acquisition unit, a model building unit, and a teaching unit; wherein: The acquisition unit is used to acquire a target TCM course of a target object and a target teaching plan for the target TCM course; the target TCM course includes one of the following: Zhangshu TCM course, Chinese medicine course, basic TCM theory course, acupuncture and massage course; and acquire a target TCM database corresponding to the target TCM course; The model building unit is used to build a TCM AI model according to the target TCM database and the target teaching plan to obtain a target TCM AI model; The acquisition unit is further used to acquire the target medical question for the target TCM course and the personal information of the target subject; The teaching unit is used to input the target medical question and the personal information into the target TCM AI model to obtain a first reply content; obtain the first feedback content of the target object for the first reply content; determine whether the target object meets the first preset condition based on the first feedback content; when the target object meets the first preset condition, generate a corresponding medical case based on the target medical question to obtain a medical case; a is an integer greater than 1; obtain the feedback content of the target object for each of the a medical cases to obtain a feedback content; detect whether the a feedback content meets the second preset condition; when the a feedback content meets the second preset condition, determine that the target object has mastered the TCM knowledge corresponding to the target medical question.

[0007] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present invention.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present invention.

[0009] In a fifth aspect, an embodiment of the present invention provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present invention. The computer program product may be a software installation package.

[0010] The implementation of this application has the following beneficial effects: It can be seen that the AI ​​teaching method based on TCM culture described in this application obtains the first reply content by inputting the target object's personal information together with the target medical question into the model. The addition of personal information enables the reply to fully consider the characteristics of the target object, such as age, cultural background, knowledge base and other factors, thereby making personalized replies to help the target object better understand TCM knowledge, reduce the time for understanding, and thus improve the efficiency of TCM teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0012] Figure 1 It is a structural schematic diagram of a traditional Chinese medicine teaching system provided in an embodiment of the present application; Figure 2 This is a flow chart of an AI teaching method based on TCM culture provided in an embodiment of the present application; Figure 3 This is a functional unit composition block diagram of an AI teaching device based on traditional Chinese medicine culture provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0015] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0016] The electronic devices described in the embodiments of the present application may include a traditional Chinese medicine teaching system, a smart phone (such as an Android phone, an iOS phone, a Windows Phone phone, etc.), a tablet computer, a PDA, a laptop computer, a video matrix, a monitoring platform, a mobile Internet device (mobile internet devices, MID) or a wearable device, etc. The above are only examples and not exhaustive, including but not limited to the above devices. Of course, the above electronic devices can also be servers, for example, a cloud server.

[0017] The following is an explanation of some professional terms involved in this application: Chinese medicine: refers to traditional medicine, which has a unique theoretical style and rich experience in diagnosis and treatment. It uses the theory of Yin and Yang and the Five Elements as a philosophical basis to explain the physiological functions and pathological changes of the human body, and to guide the diagnosis and treatment of diseases. For example, the five internal organs of the human body (heart, liver, spleen, lungs, and kidneys) correspond to the five elements (fire, wood, earth, metal, and water), and there is a relationship of mutual generation and mutual restraint between the five elements, such as wood generates fire, fire restrains metal, etc. This relationship also reflects the mutual connection and restriction between the internal organs. Under pathological conditions, for example, lesions in the liver (wood) may affect the heart (fire), which is a manifestation of the Chinese medicine theory of explaining the transmission of diseases based on the mutual generation and mutual restraint of the five elements.

[0018] TCM keywords: refers to words that are of great significance in the field of TCM. These words are key elements in understanding and spreading TCM knowledge. For example, the commonly said "cold" can be expressed in TCM through TCM keywords such as wind-cold cold, wind-heat cold, etc. TCM doctors can quickly diagnose diseases based on these TCM keywords.

[0019] See also Figure 1 , Figure 1 is a schematic diagram of the structure of a TCM teaching system provided in an embodiment of the present application, such as Figure 1 As shown, the TCM teaching system includes: a user terminal device, a server, and a TCM teaching module. The AI ​​teaching method based on TCM culture provided in the embodiment of the present application can be applied to the TCM teaching system, wherein: The user-end device is used to receive the target TCM course, the target teaching plan for the course and other related information input by the target object. For example, the target object can choose whether he wants to learn Zhangshu TCM course, Chinese medicine course or other courses on the user-end device (such as a mobile phone, tablet or computer), and can also input the teaching schedule, learning focus and other teaching plan related content he expects. At the same time, the target object also inputs the target medical questions and personal information for the target TCM course through this device, so that the system can provide personalized teaching services based on these inputs.

[0020] The server is used to store the target TCM database corresponding to the target TCM course. This database contains a wealth of TCM knowledge, such as ancient TCM literature, modern TCM research results, clinical cases, TCM information, acupuncture and massage knowledge, and other data. At the same time, the server also stores the user's personal information, learning records, feedback information and other data for comprehensive management and analysis of the user's learning process. For example, the server saves the target medical questions asked by the target object each time, the answers given by the system, and the feedback from the target object, so as to optimize the subsequent teaching services based on these data.

[0021] The TCM teaching module is used to build a TCM AI model based on the target TCM database and target teaching plan input by the user-end device, and to answer the target object's questions through the TCM AI model to help the target object better understand and apply TCM knowledge.

[0022] See also Figure 2 , Figure 2 : is a flow chart of an AI teaching method based on TCM culture provided in an embodiment of the present application; the method may include the following steps: S201, obtaining a target TCM course of a target object and a target teaching plan for the target TCM course; the target TCM course includes one of the following: Zhangshu TCM course, Chinese medicine course, basic TCM theory course, and acupuncture and massage course.

[0023] In the embodiment of the present application, the target teaching plan may include the scope of TCM knowledge that the target object needs to learn, as well as the teaching objectives. It should be explained that the AI ​​teaching method based on TCM culture provided in the embodiment of the present application can be applied to Figure 1 The TCM teaching system shown.

[0024] In a specific embodiment, a plurality of different types of TCM courses can be provided for the target object to choose from, and the TCM course selected by the target object is taken as the target TCM course. Then, a target teaching plan for the target TCM course can be formulated. Specifically, the learning purpose of the target object to learn the target TCM course can be obtained, and then a suitable teaching plan can be formulated based on the learning purpose to obtain a target teaching plan. For example, assuming that the learning purpose is to understand the basic knowledge of Zhangshu TCM, then it can be determined that the scope of TCM knowledge that the target object needs to learn is Zhangshu TCM, and the teaching goal is to make the target object familiar with Zhangshu TCM knowledge and able to use the learned Zhangshu TCM knowledge to solve some simple Zhangshu TCM problems. S202: Acquire a target TCM database corresponding to the target TCM course.

[0025] In the embodiment of the present application, the target TCM database contains all TCM knowledge that the target subject needs to learn in the target TCM course, for example, TCM theoretical knowledge, TCM diagnostic methods, TCM knowledge, etc.

[0026] In a specific embodiment, a mapping relationship between preset TCM courses and TCM databases may be pre-stored, and a target TCM database corresponding to a target TCM course may be determined based on the mapping relationship.

[0027] S203, constructing a TCM AI model according to the target TCM database and the target teaching plan to obtain a target TCM AI model.

[0028] In the embodiment of the present application, the target TCM AI model may include one of the following: a deep learning model, a neural network model, a knowledge graph model, etc., which is not limited here.

[0029] In a specific embodiment, a target TCM AI model can be constructed based on a target TCM database and a target teaching plan.

[0030] Optionally, step S203, constructing a TCM AI model according to the target TCM database and the target teaching plan to obtain the target TCM AI model may include the following steps: S31, determining target data characteristics of the TCM data in the target TCM database; S32, selecting an AI model corresponding to the characteristics of the target data from a preset AI model library to obtain an initial AI model; S33, selecting a first data set from the target traditional Chinese medicine database according to the target teaching plan; S34, obtaining the target learning progress of the target subject for the target TCM course; S35, dividing the first data set into a second data set and a third data set based on the target learning progress; the second data set is the TCM knowledge that the target subject has already learned, and the third data set is the TCM knowledge that the target subject has not yet learned; S36, dividing the second data set and the third data set into a training set and a test set according to a preset ratio; S37. Training the initial AI model according to the training set to obtain a first AI model; S38. Testing the first AI model using the test set to obtain a test result, and determining a first accuracy rate according to the test result; S39. When the first accuracy rate is greater than a first preset accuracy rate, the first AI model is used as the target TCM AI model.

[0031] In the embodiment of the present application, the target data characteristics may include at least one of the following: language characteristics, rich association rules, multimodal characteristics (including data in multiple modalities such as text, images, sounds, etc.), etc., which are not limited here; the preset ratio and the first preset accuracy rate can be preset or defaulted in advance.

[0032] In a specific embodiment, target data characteristics of the TCM data in the target TCM database are determined. Specifically, the target data characteristics may be language characteristics. Several TCM data may be randomly selected from the target TCM database, and the sentence structures of the several TCM data may be analyzed. If the analysis determines that there are more sentence structures of classical Chinese, then the language characteristics may be determined to be classical language, which is the target data characteristics. Then, an AI model corresponding to the target data characteristics may be selected from a preset AI model library to obtain an initial AI model. For example, a mapping relationship between preset data characteristics and AI models may be pre-stored, and the initial AI model corresponding to the target data characteristics is determined based on the mapping relationship. Then, the scope of TCM knowledge to be learned by the target object may be determined according to the target teaching plan, and corresponding data may be selected from the target TCM database according to the scope of TCM knowledge to obtain a first data set.

[0033] Next, the target learning progress of the target object for the target TCM course can be obtained. Specifically, the learning record of the target object can be obtained, and the target learning progress can be determined based on the learning record; then, based on the target learning progress, the first data set is divided into TCM knowledge that the target object has learned to obtain a second data set, and TCM knowledge that the target object has not yet learned to obtain a third data set; then, the second data set and the third data set can be divided into a training set and a test set according to a preset ratio. For example, assuming that the preset ratio is 7:3, 70% of the data in the second data set and the third data set can be used as a training set, and the remaining 30% of the data can be used as a test set; then, the initial AI model can be trained according to the training set to obtain a first AI model; then, the first AI model is tested with the test set to obtain a test result, and the number of correct results output by the first AI model in the test result is obtained. The number of correct results is divided by the total number of test results to obtain a first accuracy rate; when the first accuracy rate is greater than the first preset accuracy rate, the first AI model is used as the target TCM AI model.

[0034] When the first accuracy is not greater than the first preset accuracy, it means that the accuracy of the first AI model is low and cannot meet the usage requirements. It is necessary to further train the first AI model, or rebuild an AI model and then test it until the first accuracy is greater than the first preset accuracy to obtain the target TCM AI model.

[0035] In this way, the data set is divided into learned knowledge (second data set) and unlearned knowledge (third data set) based on the learning progress of the target object, and then further divided into training set and test set. This enables model training to be closely integrated with the actual learning situation of the target object. For the knowledge that has been learned, the model can strengthen the consolidation training; for the knowledge that has not been learned, it can provide targeted preview or preliminary learning guidance. For example, in the basic theory course of traditional Chinese medicine, if the target object has already learned the theory of yin and yang and the five elements, the model can use the data related to this part of knowledge for intensive training during training, and at the same time provide appropriate learning guidance for the subsequent unlearned knowledge such as the theory of qi, blood and body fluids.

[0036] S204: Obtain target medical questions for the target traditional Chinese medicine course and personal information of the target subject.

[0037] In the embodiment of the present application, target medical questions for the target TCM course and personal information of the target subject are obtained. Specifically, the target medical questions and personal information can be actively input into the TCM teaching system by the target subject.

[0038] S205. Input the target medical question and the personal information into the target TCM AI model to obtain a first reply content.

[0039] In an embodiment of the present application, the target medical question and personal information can be input into the target TCM AI model, and the target TCM AI model outputs the first reply content.

[0040] Optionally, step S205, inputting the target medical question and the personal information into the target TCM AI model to obtain the first reply content, may include the following steps: A1. extract keywords from the target medical question to obtain m keywords; the m keywords are used to indicate the inquiry intention of the target medical question; m is a positive integer; A2, determining the synonym set corresponding to each of the m keywords in the target TCM database, and obtaining m synonym sets; each synonym set includes a plurality of TCM keywords; A3, replacing the m keywords based on the m synonym sets to obtain m TCM keywords; A4. Determine the inquiry intention of the target medical question according to the m TCM keywords to obtain the target inquiry intention; A5. Generate a response content corresponding to the target inquiry intention, and obtain a reference first response content; A6. Adjust the reference first reply content according to the personal information to obtain the first reply content.

[0041] In an embodiment of the present application, keywords in the target medical problem can be extracted to obtain m keywords. Specifically, a Chinese word segmentation tool (such as Jieba word segmentation) can be used to segment the target medical problem. For example, for the question "How does traditional Chinese medicine treat cough caused by colds", after word segmentation, m keywords such as "traditional Chinese medicine", "how", "treatment", "cold", "cause", and "cough" are obtained. Then, the synonym set corresponding to each keyword in the target traditional Chinese medicine database can be determined to obtain m synonym sets. Specifically, for each keyword in the m keywords, the word vector similarity (such as cosine similarity) between it and the traditional Chinese medicine keyword in the target traditional Chinese medicine database is calculated, and a similarity threshold is set. The words with a similarity higher than the threshold are used as synonyms, thereby obtaining m synonym sets. For example, for the keyword "ginseng", it is found through word vector calculation that words such as "dangshen" and "American ginseng" are close to "ginseng" in the semantic vector space, and the cosine similarity is greater than the similarity threshold. These words can be included in the synonym set of "ginseng".

[0042] Furthermore, m keywords can be replaced based on m synonym sets to obtain m TCM keywords; then, the inquiry intent of the target medical question can be determined based on the m TCM keywords to obtain the target inquiry intent. Specifically, the subject, action, object and other information of the target medical question can be determined based on the m TCM keywords, and the arrangement order and grammatical structure of the m TCM keywords can be observed to understand the intent under the Chinese grammatical rules to obtain the target inquiry intent. For example, "Chinese herbal prescriptions", "formulation principles", and "treatment of cough" may be "formulation principles of Chinese herbal prescriptions for treating cough" according to normal grammar and logic. The word order reflects the focus and sequence of the questioner's attention to a certain extent, which is helpful to determine the inquiry intent; the target TCM AI model can be used to generate a reply content corresponding to the target inquiry intent to obtain a reference first reply content; finally, the reference first reply content can be adjusted according to personal information to obtain the first reply content.

[0043] In this way, by determining the synonym set corresponding to each keyword and replacing it, it is equivalent to broadening the dimension of semantic understanding. Since there are a large number of synonymous or near-synonymous expressions in the TCM knowledge system, such as "epigastric pain" and "stomach pain", they often express similar symptoms. When analyzing questions, incorporating synonyms can avoid the deviation in understanding the question caused by the questioner using different but similar words, making the grasp of the core intention of the question more comprehensive and accurate, thereby outputting more accurate answer content.

[0044] Optionally, step A3, replacing the m keywords based on the m synonym sets to obtain m TCM keywords, may include the following steps: B1. Obtain a target synonym set and a target keyword corresponding to the target synonym set; the target synonym set is any one of the m synonym sets; the target synonym set includes n TCM keywords; n is a positive integer; B2, calculating the semantic similarity between each TCM keyword in the target synonym set and the target keyword to obtain n semantic similarities; B3, determining the semantic similarities greater than the first preset similarity threshold among the n semantic similarities, and obtaining i semantic similarities; i is a positive integer less than or equal to n; B4, determining the TCM keywords corresponding to the i semantic similarities in the target synonym set, and obtaining i TCM keywords; B5, determining a TCM keyword associated with the target medical problem among the i TCM keywords, and obtaining at least one TCM keyword; B6. Determine the TCM keyword with the greatest semantic similarity among the at least one TCM keyword according to the i semantic similarities, and obtain the TCM keyword corresponding to the target synonym set.

[0045] In the embodiment of the present application, the first preset similarity threshold may be preset in advance or defaulted.

[0046] In a specific embodiment, a target synonym set and a target keyword corresponding to the target synonym set can be obtained; then, the semantic similarity between each TCM keyword in the target synonym set and the target keyword can be calculated to obtain n semantic similarities. Specifically, the TCM keywords and the target keywords in the target synonym set can be converted into word vectors through a preset word vector model, and a suitable vector similarity calculation method (for example, cosine similarity) is used to calculate the semantic similarity between each TCM keyword in the target synonym set and the target keyword to obtain n semantic similarities, wherein the preset word vector model can be preset or defaulted in advance; then, the semantic similarity greater than the first preset similarity threshold among the n semantic similarities can be found to obtain i semantic similarities.

[0047] Furthermore, the TCM keywords corresponding to i semantic similarities in the target synonym set can be determined to obtain i TCM keywords; the TCM keywords associated with the target medical problem among the i TCM keywords can be determined to obtain at least one TCM keyword. Specifically, the inquiry intention of the target medical problem can be determined based on the above m keywords to obtain an initial inquiry intention, and at least one TCM keyword can be selected from the i TCM keywords based on the initial inquiry intention. For example, assuming that the i TCM keywords are: "cough and reverse", "sputum", "dry cough", "wet cough", etc., and the initial inquiry intention is "how to treat cough with phlegm", then it can be determined that "sputum" and "wet cough" among the i TCM keywords are associated with the target medical problem to obtain at least one TCM keyword; then, at least one semantic similarity corresponding to at least one TCM keyword can be extracted from the i semantic similarities, the maximum value among the at least one semantic similarity can be determined, and the TCM keyword corresponding to the maximum value can be used as the TCM keyword corresponding to the target synonym set.

[0048] In this way, by calculating the semantic similarity between each TCM keyword in the target synonym set and the target keyword, and screening out the parts that are greater than the first preset similarity threshold, we can accurately focus on those TCM keywords that are semantically similar to the target keyword, thereby improving analysis efficiency.

[0049] Optionally, in step A6, the personal information includes: target education information and target age information, and the first reply content is adjusted according to the personal information to obtain the first reply content, including: C1. Extract the TCM keywords in the reference first reply content to obtain j TCM keywords; j is a positive integer; C2. Determine a comprehension difficulty parameter of each of the j TCM keywords according to the personal information to obtain j comprehension difficulty parameters; C3. determining a first comprehension difficulty parameter according to the j comprehension difficulty parameters; C4. Determine a second comprehension difficulty parameter acceptable to the target object according to the target educational background information; C5. when the first comprehension difficulty parameter is greater than the second comprehension difficulty parameter, determining a difference between the first comprehension difficulty parameter and the second comprehension difficulty parameter to obtain a target comprehension difficulty difference; C6. Selecting k comprehension difficulty parameters from the j comprehension difficulty parameters based on the target comprehension difficulty difference; the sum of the k comprehension difficulty parameters is greater than or equal to the target comprehension difficulty difference; k is a positive integer less than or equal to j; C7, determining the TCM keywords corresponding to the k comprehension difficulty parameters among the j TCM keywords, and obtaining k TCM keywords; C8. Selecting TCM keyword annotations corresponding to the k TCM keywords from the target TCM database to obtain k TCM keyword annotations; C9, replacing the k TCM keywords in the reference first reply content with the k TCM keyword annotations to obtain a second reply content; C10, determining a target response form corresponding to the target age information; C11. Adjust the second reply content according to the target reply form to obtain the first reply content.

[0050] In the embodiment of the present application, the understanding difficulty parameter is used to measure the difficulty of the target object to understand a traditional Chinese medicine keyword.

[0051] In a specific embodiment, the TCM keywords in the reference first reply content can be extracted to obtain j TCM keywords. Specifically, the method for obtaining j TCM keywords can be the same as the method for obtaining m keywords, which will not be repeated here; then, the understanding difficulty parameter of each TCM keyword in the j TCM keywords can be determined according to the personal information to obtain j understanding difficulty parameters. Specifically, the TCM teaching textbook corresponding to the target TCM course can be obtained first, and the chapter position of each TCM keyword in the TCM teaching textbook can be determined to obtain j chapter positions. According to the j chapter positions, j reference understanding difficulty parameters are determined. The mapping relationship between the preset chapter position and the reference understanding difficulty parameter can be pre-stored. The j reference understanding difficulty parameters corresponding to the j chapter positions are determined based on the mapping relationship. The later the chapter position is, the greater the reference understanding difficulty parameter is. Then, the target understanding ability parameter of the target object can be determined according to the target academic information in the personal information. For example, the mapping relationship between the preset academic information and the understanding ability parameter can be pre-stored. The target understanding ability parameter corresponding to the target academic information is determined based on the mapping relationship. The j reference understanding difficulty parameters are adjusted according to the target understanding ability parameter to obtain j understanding difficulty parameters. The specific adjustment formula is as follows: First comprehension difficulty parameter = first reference comprehension difficulty parameter × (1-(target comprehension parameter-preset comprehension parameter) / preset comprehension parameter); Among them, the preset comprehension parameter can be preset or defaulted in advance, the first reference comprehension difficulty parameter is any reference comprehension difficulty parameter among j reference comprehension difficulty parameters; the first comprehension difficulty parameter is the comprehension difficulty parameter corresponding to the first reference comprehension difficulty parameter among the j comprehension difficulty parameters.

[0052] It should be explained that the comprehension parameter is a parameter that represents the target object's ability to understand the TCM language. The larger the comprehension parameter is, the easier it is for the target object to understand the TCM language.

[0053] Next, a first comprehension difficulty parameter can be determined based on the j comprehension difficulty parameters. Specifically, an average comprehension difficulty parameter of the j comprehension difficulty parameters, i.e., the first comprehension difficulty parameter, can be calculated. Then, a second comprehension difficulty parameter acceptable to the target object can be determined based on the target educational background information. Specifically, a mapping relationship between preset educational background information and comprehension difficulty parameters can be pre-stored, and the second comprehension difficulty parameter corresponding to the target educational background information can be determined based on the mapping relationship. When the first comprehension difficulty parameter is greater than the second comprehension difficulty parameter, it indicates that the comprehension difficulty of the j TCM keywords is too great. At this time, the second comprehension difficulty parameter can be subtracted from the first comprehension difficulty parameter to obtain a target comprehension difficulty difference. Then, based on the target comprehension difficulty difference, the second comprehension difficulty parameter can be subtracted from the first comprehension difficulty parameter to obtain a target comprehension difficulty difference. Select k comprehension difficulty parameters. Specifically, j comprehension difficulty parameters can be sorted from large to small to obtain a first comprehension difficulty parameter sequence. The first parameter sum is initialized to 0, and the comprehension difficulty parameters in the first comprehension difficulty parameter sequence are taken out in turn. Each time an comprehension difficulty parameter is taken out, it is added to the first parameter sum, and the size relationship between the latest first parameter sum and the target comprehension difficulty difference is compared. If the first parameter sum is greater than or equal to the target comprehension difficulty difference, stop taking out the comprehension difficulty parameters in the first comprehension difficulty parameter sequence to obtain k comprehension difficulty parameters. Conversely, if the first parameter sum is less than the target comprehension difficulty difference, continue to take out the comprehension difficulty parameters in the first comprehension difficulty parameter sequence until the first parameter sum is greater than or equal to the target comprehension difficulty difference.

[0054] Further, k TCM keywords corresponding to the k TCM keywords in the j TCM keywords with respect to the comprehension difficulty parameters can be found to obtain k TCM keywords; then, TCM keyword annotations corresponding to the k TCM keywords can be selected from the target TCM database to obtain k TCM keyword annotations, each TCM keyword corresponding to a TCM keyword annotation. Specifically, the index of each TCM keyword in the k TCM keywords can be obtained to obtain k indexes, and the target TCM database can be searched according to the k indexes to obtain k TCM keyword annotations; then, the k TCM keywords in the referenced first reply content can be replaced by the k TCM keyword annotations to obtain the second reply content; then, the target reply form corresponding to the target age information can be determined, for example, a mapping relationship between preset age information and reply form can be pre-stored, and the target reply form corresponding to the target age information can be determined based on the mapping relationship. Finally, the second reply content can be adjusted according to the target reply form to obtain the first reply content. Specifically, the reply form of the second reply content can be changed to the target reply form to obtain the first reply content; the target reply form can include one of the following: knowledge explanation type, operation guidance type, comparative analysis type, etc., which are not limited here.

[0055] In this way, by customizing the response content based on the target object's personal information (including comprehension ability, education level, age, etc.), we can avoid providing stereotyped, general but untargeted responses, thereby enabling the target object to acquire and use Chinese medicine knowledge more efficiently.

[0056] S206: Obtain first feedback content from the target object in response to the first reply content.

[0057] In the embodiment of the present application, the first feedback content is used to indicate whether the target object understands the first reply content.

[0058] In a specific embodiment, the first reply content may be displayed by the user terminal device, so that the target object can read the first reply content, and then the feedback content input by the target object in the user terminal device, that is, the first feedback content, is obtained.

[0059] S207: Determine whether the target object meets a first preset condition according to the first feedback content.

[0060] In the embodiment of the present application, the first preset condition can be preset or defaulted in advance.

[0061] In a specific embodiment, it can be determined whether the target object understands the first reply content according to the first feedback content. If the target object understands the first reply content, it is determined that the target object meets the first preset condition.

[0062] If the target object does not understand the content of the first reply and still has questions, it is determined that the target object does not meet the first preset condition, and the first reply content is further explained through the target TCM AI model until the target object understands the reply content output by the target TCM AI model, that is, the target object meets the first preset condition.

[0063] S208. When the target object meets the first preset condition, generate corresponding medical cases based on the target medical problem to obtain a medical cases; a is an integer greater than 1.

[0064] In an embodiment of the present application, when the target object meets the first preset condition, the target question type corresponding to the target medical question is determined, and a corresponding medical case is generated based on the target question type to obtain a medical cases. Specifically, medical cases corresponding to the target question type can be screened out from the preset case library to obtain a medical cases.

[0065] S209: Obtain feedback content of the target object for each of the a medical cases to obtain a feedback content.

[0066] In the embodiment of the present application, a medical cases may be displayed on the user terminal device to obtain the target object's reply content to each medical case, that is, a feedback content.

[0067] S210: Detect whether the a feedback contents meet a second preset condition.

[0068] In the embodiment of the present application, the second preset condition may be preset in advance or default.

[0069] Optionally, step S210, detecting whether the a feedback contents meet a second preset condition may include the following steps: D1. Obtain a reference answer for each of the a medical cases to obtain a reference answers; D2. Determine the semantic similarity between the a feedback contents and the corresponding reference answers in the a reference answers to obtain a semantic similarities; D3, determining the semantic similarities among the a semantic similarities that are greater than a second preset similarity threshold, to obtain b semantic similarities; b is a natural number less than or equal to a; D4, determining a second accuracy rate according to the b semantic similarities and the a semantic similarities; D5. Determine a first average similarity corresponding to the b semantic similarities; D6, determining a second average similarity corresponding to ab semantic similarities among the a semantic similarities except the b semantic similarities; D7. Adjusting the second accuracy rate based on the first average similarity and the second average similarity to obtain a third accuracy rate; D8. When the third accuracy rate is greater than the second preset accuracy rate, determining that the a pieces of feedback content meet the second preset condition; D9. When the third accuracy rate is less than or equal to the second preset accuracy rate, determine that the a feedback contents do not meet the second preset condition.

[0070] In the embodiment of the present application, the second preset accuracy rate can be preset or defaulted in advance.

[0071] In a specific embodiment, a reference answer for each medical case in a medical cases can be obtained to obtain a reference answers. Specifically, a preset case library can include reference answers, and a reference answers corresponding to a medical cases are obtained from the preset case library; then, the semantic similarities between a feedback contents and the corresponding reference answers in the a reference answers can be calculated to obtain a semantic similarities. Specifically, the method for calculating a semantic similarities can be the same as the method for calculating the above n semantic similarities, which will not be repeated here; then, the semantic similarity in the a semantic similarities that is greater than the second preset similarity threshold can be found to obtain b semantic similarities; then, the second accuracy rate can be determined based on the b semantic similarities and the a semantic similarities, as follows: The second accuracy rate = b / a × 100%; According to the above formula, the second accuracy rate can be obtained, and then, the average value of the b semantic similarities, that is, the first average similarity, can be calculated; further, ab semantic similarities excluding b semantic similarities among the a semantic similarities can be determined first, and then, the average value of the ab semantic similarities, that is, the second average similarity, can be calculated; then, the second accuracy rate can be adjusted based on the first average similarity and the second average similarity to obtain a third accuracy rate; when the third accuracy rate is greater than the second preset accuracy rate, it is determined that the a feedback contents meet the second preset condition.

[0072] When the third accuracy rate is less than or equal to the second preset accuracy rate, it is determined that the a feedback contents do not meet the second preset condition.

[0073] In this way, the third accuracy rate is obtained by adjusting the second accuracy rate based on the two average similarities, thereby avoiding the limitation of simply relying on simple comparison or single dimension to judge the accuracy rate, thereby making the final third accuracy rate more real and accurate.

[0074] Optionally, step D7, adjusting the second accuracy based on the first average similarity and the second average similarity to obtain a third accuracy may include the following steps: E1. Determine a first similarity sequence according to the a semantic similarities; E2. Determine the similarity difference between every two adjacent similarities in the first similarity sequence to obtain a-1 similarity differences; E3. Determine the similarity difference values ​​greater than the first preset difference value among the a-1 similarity difference values, and obtain c similarity difference values; c is a natural number less than or equal to a-1; E4. Determine a ratio between the c similarity differences and ac-1 similarity differences among the a-1 similarity differences that are not greater than a first preset difference, to obtain a first ratio; E5. When the first ratio is less than a preset ratio, determine a ratio between the first average similarity and the second average similarity to obtain a second ratio; E6. Determine a difference between the second ratio and the first ratio to obtain a first difference; E7. Determine a target adjustment factor corresponding to the first difference; E8. Adjust the second accuracy rate according to the target adjustment factor to obtain the third accuracy rate.

[0075] In the embodiment of the present application, the preset ratio may be preset in advance or defaulted.

[0076] In a specific embodiment, the a semantic similarities mentioned above can be sorted according to the generation time of the corresponding feedback content to obtain a first similarity sequence, and the earlier the generation time, the higher the order; then, the similarity difference between every two adjacent similarities in the first similarity sequence can be calculated to obtain a-1 similarity difference values; further, the similarity difference value greater than the first preset difference value can be found out among the a-1 similarity difference values ​​to obtain c similarity difference values; then, ac-1 similarity difference values ​​not greater than the first preset difference value can be obtained among the a-1 similarity difference values, and the ratio between the c similarity difference values ​​and the ac-1 similarity difference values ​​can be calculated, and the specific calculation formula is as follows: First ratio = c / (ac-1); According to the above formula, the first ratio can be obtained; when the first ratio is less than the preset ratio, the ratio between the first average similarity and the second average similarity is determined, and the specific calculation formula is as follows: The second ratio = the first average similarity / the second average similarity; According to the above formula, the second ratio can be obtained; the first difference can be obtained by subtracting the first ratio from the second ratio; then, the target adjustment factor corresponding to the first difference can be determined. For example, a mapping relationship between a preset difference and an adjustment factor can be pre-set. Based on the mapping relationship, the target adjustment factor corresponding to the first difference can be determined. The value range of the target adjustment factor can be -0.2~0.2; finally, the second accuracy rate can be adjusted according to the target adjustment factor. The specific calculation formula is as follows: The third accuracy rate = the second accuracy rate × (1 + target adjustment factor); The third accuracy can be obtained according to the above formula.

[0077] In this way, by determining the c similarity differences that are greater than the first preset difference among the a-1 similarity differences, the key points that cause significant fluctuations in semantic similarity can be accurately screened out. These significant change points often contain important information, which may indicate that there are some issues that need special attention in the collection of feedback content, the setting of reference answers, or the understanding of corresponding medical knowledge. For example, if a large similarity difference suddenly appears in several consecutive medical cases, it may mean that the explanation of the knowledge points corresponding to these cases is not clear enough, resulting in a large deviation in the understanding of the feedback recipient, thereby causing the feedback quality to fluctuate significantly. By screening out these key differences, these "abnormal areas" that may have problems can be quickly located, so that the target objects can be re-taught in these "abnormal areas" to improve the efficiency of Chinese medicine teaching.

[0078] Optionally, when the first ratio is greater than the preset ratio, the method may include the following steps: F1. Determine a difference between the first ratio and the preset ratio to obtain a second difference; F2. When the second difference is greater than a second preset difference, determining that the a feedback contents do not meet the second preset condition; F3. When the second difference is not greater than the second preset difference, determine the feedback contents corresponding to the c similarity differences in the a feedback contents to obtain d feedback contents; d is an integer greater than c and less than or equal to a; F4. Generate new medical cases based on the target medical problem to obtain e medical cases; e equals d; F5. Obtaining feedback content of the target object for each of the e medical cases to obtain e feedback content; F6. Replace the d feedback contents in the a feedback contents with the e feedback contents to obtain updated a feedback contents; F7. For the updated a feedback contents, executing the step of detecting whether the a feedback contents meet a second preset condition.

[0079] In the embodiment of the present application, the second preset difference can be preset or defaulted in advance.

[0080] In a specific embodiment, the second difference can be obtained by subtracting the preset ratio from the first ratio; when the second difference is greater than the second preset difference, it means that the semantic similarity of the a feedback contents generated by the target object fluctuates too much, is not stable, and has an abnormality. Therefore, it can be determined that the a feedback contents do not meet the second preset condition.

[0081] When the second difference is not greater than the second preset difference, the feedback content corresponding to the a feedback content with c similarity differences can be determined to obtain d feedback content; then, a new medical case can be generated according to the target medical problem to obtain e medical cases. Specifically, the method for generating e medical cases can be the same as the method for generating a medical case mentioned above, which will not be repeated here; it needs to be explained that each medical case in the e medical cases is different from the medical case in the a medical cases.

[0082] Furthermore, the feedback content of the target object for each of the e medical cases can be obtained to obtain e feedback contents; the e feedback contents are used to replace d feedback contents in the a feedback contents to obtain updated a feedback contents; then, the step of detecting whether the a feedback contents meet the second preset condition can be executed on the updated a feedback contents.

[0083] In this way, by generating new medical cases and updating feedback content around the target medical problem, the characteristics of different medical problems and the specific situation of the target object in mastering the corresponding knowledge are fully considered. For example, for TCM disease diagnosis problems, if it is found that the similarity of the feedback content in certain disease cases fluctuates greatly, more new cases of the same disease but with different symptom details or different levels of complexity are generated in a targeted manner to guide the target object to rethink and give feedback, thereby more accurately discovering the weak links of the target object in the diagnosis knowledge of the disease, and then strengthening the explanation and training of relevant knowledge in a targeted manner, thereby realizing personalized medical knowledge transfer and ability training, and improving the overall teaching and learning effect.

[0084] S211. When the a pieces of feedback content meet the second preset condition, determine that the target subject has mastered the traditional Chinese medicine knowledge corresponding to the target medical problem.

[0085] In the embodiment of the present application, when a pieces of feedback content meet the second preset condition, it indicates that the target object has mastered the traditional Chinese medicine knowledge related to the target medical problem.

[0086] The implementation of this application has the following beneficial effects: It can be seen that the AI ​​teaching method based on TCM culture described in this application obtains the first reply content by inputting the target object's personal information together with the target medical question into the model. The addition of personal information enables the reply to fully consider the characteristics of the target object, such as age, cultural background, knowledge base and other factors, thereby making personalized replies to help the target object better understand TCM knowledge, reduce the time for understanding, and thus improve the efficiency of TCM teaching.

[0087] See also Figure 3 , Figure 3: is a functional unit composition block diagram of an AI teaching device 300 based on TCM culture provided in an embodiment of the present application. The AI ​​teaching device 300 based on TCM culture includes: an acquisition unit 301, a model building unit 302, and a teaching unit 303; wherein: The acquisition unit 301 is used to acquire a target TCM course of a target object and a target teaching plan for the target TCM course; the target TCM course includes one of the following: Zhangshu TCM course, Chinese medicine course, basic TCM theory course, acupuncture and massage course; and acquire a target TCM database corresponding to the target TCM course; The model building unit 302 is used to build a TCM AI model according to the target TCM database and the target teaching plan to obtain a target TCM AI model; The acquisition unit 301 is further used to acquire the target medical question for the target TCM course and the personal information of the target subject; The teaching unit 303 is used to input the target medical question and the personal information into the target TCM AI model to obtain a first reply content; obtain the first feedback content of the target object for the first reply content; determine whether the target object meets the first preset condition based on the first feedback content; when the target object meets the first preset condition, generate a corresponding medical case based on the target medical question to obtain a medical case; a is an integer greater than 1; obtain the feedback content of the target object for each of the a medical cases to obtain a feedback content; detect whether the a feedback content meets the second preset condition; when the a feedback content meets the second preset condition, determine that the target object has mastered the TCM knowledge corresponding to the target medical question.

[0088] Optionally, in the aspect of constructing a TCM AI model according to the target TCM database and the target teaching plan to obtain a target TCM AI model, the model construction unit 302 is specifically used for: Determining target data characteristics of the TCM data in the target TCM database; Select an AI model corresponding to the characteristics of the target data from a preset AI model library to obtain an initial AI model; Selecting a first data set from the target traditional Chinese medicine database according to the target teaching plan; Obtaining the target learning progress of the target subject for the target TCM course; The first data set is divided into a second data set and a third data set based on the target learning progress; the second data set is the TCM knowledge that the target object has learned, and the third data set is the TCM knowledge that the target object has not learned; Dividing the second data set and the third data set into a training set and a test set according to a preset ratio; Training the initial AI model according to the training set to obtain a first AI model; Testing the first AI model using the test set to obtain a test result, and determining a first accuracy rate according to the test result; When the first accuracy rate is greater than a first preset accuracy rate, the first AI model is used as the target TCM AI model.

[0089] Optionally, in the aspect of inputting the target medical question and the personal information into the target TCM AI model to obtain the first reply content, the teaching unit 303 is specifically used to: Extracting keywords from the target medical question to obtain m keywords; the m keywords are used to indicate the inquiry intention of the target medical question; m is a positive integer; Determine the synonym set corresponding to each of the m keywords in the target TCM database to obtain m synonym sets; each synonym set includes a plurality of TCM keywords; Replacing the m keywords based on the m synonym sets to obtain m TCM keywords; Determine the inquiry intention of the target medical question according to the m TCM keywords to obtain the target inquiry intention; Generate a response content corresponding to the target inquiry intention to obtain a reference first response content; The reference first reply content is adjusted according to the personal information to obtain the first reply content.

[0090] Optionally, in the aspect of replacing the m keywords based on the m synonym sets to obtain m TCM keywords, the teaching unit 303 is specifically used for: Obtain a target synonym set and a target keyword corresponding to the target synonym set; the target synonym set is any one of the m synonym sets; the target synonym set includes n traditional Chinese medicine keywords; n is a positive integer; Calculate the semantic similarity between each TCM keyword in the target synonym set and the target keyword to obtain n semantic similarities; Determine the semantic similarities greater than the first preset similarity threshold among the n semantic similarities to obtain i semantic similarities, where i is a positive integer less than or equal to n; Determine the TCM keywords corresponding to the i semantic similarities in the target synonym set to obtain i TCM keywords; Determine a TCM keyword associated with the target medical problem among the i TCM keywords to obtain at least one TCM keyword; According to the i semantic similarities, a TCM keyword with the greatest semantic similarity among the at least one TCM keyword is determined to obtain the TCM keyword corresponding to the target synonym set.

[0091] Optionally, the personal information includes: target education information and target age information. In adjusting the reference first reply content according to the personal information to obtain the first reply content, the teaching unit 303 is specifically used to: Extract the TCM keywords in the reference first reply content to obtain j TCM keywords; j is a positive integer; Determine a comprehension difficulty parameter of each of the j TCM keywords according to the personal information to obtain j comprehension difficulty parameters; Determine a first comprehension difficulty parameter according to the j comprehension difficulty parameters; Determining a second comprehension difficulty parameter acceptable to the target object according to the target educational background information; When the first comprehension difficulty parameter is greater than the second comprehension difficulty parameter, determining a difference between the first comprehension difficulty parameter and the second comprehension difficulty parameter to obtain a target comprehension difficulty difference; Selecting k comprehension difficulty parameters from the j comprehension difficulty parameters based on the target comprehension difficulty difference; the sum of the k comprehension difficulty parameters is greater than or equal to the target comprehension difficulty difference; k is a positive integer less than or equal to j; Determine the TCM keywords corresponding to the k comprehension difficulty parameters among the j TCM keywords to obtain k TCM keywords; Selecting TCM keyword annotations corresponding to the k TCM keywords from the target TCM database to obtain k TCM keyword annotations; Replacing the k TCM keywords in the referenced first reply content with the k TCM keyword annotations to obtain a second reply content; determining a target response form corresponding to the target age information; The second reply content is adjusted according to the target reply form to obtain the first reply content.

[0092] Optionally, in the aspect of detecting whether the a feedback contents meet the second preset condition, the teaching unit 303 is specifically used for: Obtain a reference answer for each of the a medical cases to obtain a reference answers; Determine semantic similarities between the a feedback contents and corresponding reference replies in the a reference replies to obtain a semantic similarities; Determine the semantic similarities greater than the second preset similarity threshold among the a semantic similarities, and obtain b semantic similarities; b is a natural number less than or equal to a; Determining a second accuracy rate according to the b semantic similarities and the a semantic similarities; Determine a first average similarity corresponding to the b semantic similarities; Determine a second average similarity corresponding to ab semantic similarities other than the b semantic similarities among the a semantic similarities; Adjusting the second accuracy rate based on the first average similarity and the second average similarity to obtain a third accuracy rate; When the third accuracy rate is greater than the second preset accuracy rate, determining that the a pieces of feedback content meet the second preset condition; When the third accuracy rate is less than or equal to the second preset accuracy rate, it is determined that the a feedback contents do not meet the second preset condition.

[0093] Optionally, in the aspect of adjusting the second accuracy rate based on the first average similarity and the second average similarity to obtain a third accuracy rate, the teaching unit 303 is specifically used to: Determine a first similarity sequence according to the a semantic similarities; Determine a similarity difference between every two adjacent similarities in the first similarity sequence to obtain a-1 similarity differences; Determine the similarity difference values ​​greater than the first preset difference value among the a-1 similarity difference values, and obtain c similarity difference values; c is a natural number less than or equal to a-1; Determine a ratio between the c similarity differences and ac-1 similarity differences among the a-1 similarity differences that are not greater than a first preset difference, to obtain a first ratio; When the first ratio is less than a preset ratio, determining a ratio between the first average similarity and the second average similarity to obtain a second ratio; determining a difference between the second ratio and the first ratio to obtain a first difference; Determining a target adjustment factor corresponding to the first difference; The second accuracy rate is adjusted according to the target adjustment factor to obtain the third accuracy rate.

[0094] Optionally, when the first ratio is greater than the preset ratio, the AI ​​teaching device 300 based on TCM culture is specifically used to: Determine a difference between the first ratio and the preset ratio to obtain a second difference; When the second difference is greater than a second preset difference, determining that the a feedback contents do not meet the second preset condition; When the second difference is not greater than the second preset difference, determining the feedback contents corresponding to the c similarity differences in the a feedback contents, to obtain d feedback contents; d is an integer greater than c and less than or equal to a; Generate new medical cases based on the target medical problem to obtain e medical cases; e equals d; Obtaining feedback content of the target object for each of the e medical cases to obtain e feedback content; Replacing the d feedback contents in the a feedback contents with the e feedback contents to obtain updated a feedback contents; For the updated a feedback contents, the step of detecting whether the a feedback contents meet a second preset condition is performed.

[0095] In a specific implementation, the AI ​​teaching device 300 based on TCM culture described in the embodiment of the present invention can also execute other implementation methods described in the AI ​​teaching method based on TCM culture provided in the above embodiment of the present invention, which will not be repeated here.

[0096] See also Figure 4 , Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application, the electronic device includes a processor, a memory, a communication interface and one or more programs, the processor, the memory and the communication interface are interconnected through a bus, the one or more programs are stored in the memory and are configured to be executed by the processor, the one or more programs include instructions for executing other implementation methods described in the AI ​​teaching method based on traditional Chinese medicine culture provided in the above embodiment of the present invention, which will not be repeated here.

[0097] An embodiment of the present invention also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes an electronic device.

[0098] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0099] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0100] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0102] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0104] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0105] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An AI teaching method based on traditional Chinese medicine culture, characterized in that: The method comprises: Obtain a target TCM course for a target object and a target teaching plan for the target TCM course; the target TCM course includes one of the following: a Zhangshu TCM course, a Chinese medicine course, a TCM basic theory course, or an acupuncture and massage course; Acquire a target TCM database corresponding to the target TCM course; Constructing a TCM AI model according to the target TCM database and the target teaching plan to obtain a target TCM AI model; Obtaining target medical issues for the target TCM course and personal information of the target subject; Input the target medical question and the personal information into the target TCM AI model to obtain a first answer content; Obtaining first feedback content from the target object in response to the first reply content; Determining whether the target object meets a first preset condition according to the first feedback content; When the target object meets the first preset condition, a corresponding medical case is generated based on the target medical problem to obtain a medical case; a is an integer greater than 1; Obtaining feedback content of the target object for each of the a medical cases to obtain a feedback content; Detecting whether the a feedback contents meet a second preset condition; When the a pieces of feedback content meet the second preset condition, it is determined that the target object has mastered the traditional Chinese medicine knowledge corresponding to the target medical problem.

2. The method according to claim 1, characterized in that The step of constructing a TCM AI model according to the target TCM database and the target teaching plan to obtain the target TCM AI model includes: Determining target data characteristics of the TCM data in the target TCM database; Select an AI model corresponding to the characteristics of the target data from a preset AI model library to obtain an initial AI model; Selecting a first data set from the target traditional Chinese medicine database according to the target teaching plan; Obtaining the target learning progress of the target subject for the target TCM course; The first data set is divided into a second data set and a third data set based on the target learning progress; the second data set is the TCM knowledge that the target object has learned, and the third data set is the TCM knowledge that the target object has not learned; Dividing the second data set and the third data set into a training set and a test set according to a preset ratio; Training the initial AI model according to the training set to obtain a first AI model; Testing the first AI model using the test set to obtain a test result, and determining a first accuracy rate according to the test result; When the first accuracy rate is greater than a first preset accuracy rate, the first AI model is used as the target TCM AI model.

3. The method according to claim 1, characterized in that The step of inputting the target medical question and the personal information into the target TCM AI model to obtain a first response content includes: Extracting keywords from the target medical question to obtain m keywords; the m keywords are used to indicate the inquiry intention of the target medical question; m is a positive integer; Determine the synonym set corresponding to each of the m keywords in the target TCM database to obtain m synonym sets; each synonym set includes a plurality of TCM keywords; Replacing the m keywords based on the m synonym sets to obtain m TCM keywords; Determine the inquiry intention of the target medical question according to the m TCM keywords to obtain the target inquiry intention; Generate a response content corresponding to the target inquiry intention to obtain a reference first response content; The reference first reply content is adjusted according to the personal information to obtain the first reply content.

4. The method according to claim 3, characterized in that The m keywords are replaced based on the m synonym sets to obtain m TCM keywords, including: Obtain a target synonym set and a target keyword corresponding to the target synonym set; the target synonym set is any one of the m synonym sets; the target synonym set includes n traditional Chinese medicine keywords; n is a positive integer; Calculate the semantic similarity between each TCM keyword in the target synonym set and the target keyword to obtain n semantic similarities; Determine the semantic similarities greater than the first preset similarity threshold among the n semantic similarities to obtain i semantic similarities, where i is a positive integer less than or equal to n; Determine the TCM keywords corresponding to the i semantic similarities in the target synonym set to obtain i TCM keywords; Determine a TCM keyword associated with the target medical problem among the i TCM keywords to obtain at least one TCM keyword; According to the i semantic similarities, a TCM keyword with the greatest semantic similarity among the at least one TCM keyword is determined to obtain the TCM keyword corresponding to the target synonym set.

5. The method according to claim 3, characterized in that The personal information includes: target education information and target age information. The first reply content is adjusted according to the personal information to obtain the first reply content, including: Extract the TCM keywords in the reference first reply content to obtain j TCM keywords; j is a positive integer; Determine a comprehension difficulty parameter of each of the j TCM keywords according to the personal information to obtain j comprehension difficulty parameters; Determine a first comprehension difficulty parameter according to the j comprehension difficulty parameters; Determining a second comprehension difficulty parameter acceptable to the target object according to the target educational background information; When the first comprehension difficulty parameter is greater than the second comprehension difficulty parameter, determining a difference between the first comprehension difficulty parameter and the second comprehension difficulty parameter to obtain a target comprehension difficulty difference; Selecting k comprehension difficulty parameters from the j comprehension difficulty parameters based on the target comprehension difficulty difference; the sum of the k comprehension difficulty parameters is greater than or equal to the target comprehension difficulty difference; k is a positive integer less than or equal to j; Determine the TCM keywords corresponding to the k comprehension difficulty parameters among the j TCM keywords to obtain k TCM keywords; Selecting TCM keyword annotations corresponding to the k TCM keywords from the target TCM database to obtain k TCM keyword annotations; Replacing the k TCM keywords in the referenced first reply content with the k TCM keyword annotations to obtain a second reply content; determining a target response form corresponding to the target age information; The second reply content is adjusted according to the target reply form to obtain the first reply content.

6. The method according to any one of claims 1 to 5, characterized in that: The detecting whether the a feedback contents meet the second preset condition includes: Obtain a reference answer for each of the a medical cases to obtain a reference answers; Determine semantic similarities between the a feedback contents and corresponding reference replies in the a reference replies to obtain a semantic similarities; Determine the semantic similarities greater than the second preset similarity threshold among the a semantic similarities, and obtain b semantic similarities; b is a natural number less than or equal to a; Determining a second accuracy rate according to the b semantic similarities and the a semantic similarities; Determine a first average similarity corresponding to the b semantic similarities; Determine a second average similarity corresponding to ab semantic similarities other than the b semantic similarities among the a semantic similarities; Adjusting the second accuracy rate based on the first average similarity and the second average similarity to obtain a third accuracy rate; When the third accuracy rate is greater than the second preset accuracy rate, determining that the a pieces of feedback content meet the second preset condition; When the third accuracy rate is less than or equal to the second preset accuracy rate, it is determined that the a feedback contents do not meet the second preset condition.

7. The method according to claim 6, characterized in that The step of adjusting the second accuracy rate based on the first average similarity and the second average similarity to obtain a third accuracy rate includes: Determine a first similarity sequence according to the a semantic similarities; Determine a similarity difference between every two adjacent similarities in the first similarity sequence to obtain a-1 similarity differences; Determine the similarity difference values ​​greater than the first preset difference value among the a-1 similarity difference values, and obtain c similarity difference values; c is a natural number less than or equal to a-1; Determine a ratio between the c similarity differences and ac-1 similarity differences among the a-1 similarity differences that are not greater than a first preset difference, to obtain a first ratio; When the first ratio is less than a preset ratio, determining a ratio between the first average similarity and the second average similarity to obtain a second ratio; determining a difference between the second ratio and the first ratio to obtain a first difference; Determining a target adjustment factor corresponding to the first difference; The second accuracy rate is adjusted according to the target adjustment factor to obtain the third accuracy rate.

8. The method according to claim 7, characterized in that When the first ratio is greater than the preset ratio, the method includes: Determine a difference between the first ratio and the preset ratio to obtain a second difference; When the second difference is greater than a second preset difference, determining that the a feedback contents do not meet the second preset condition; When the second difference is not greater than the second preset difference, determining the feedback contents corresponding to the c similarity differences in the a feedback contents, to obtain d feedback contents; d is an integer greater than c and less than or equal to a; Generate new medical cases based on the target medical problem to obtain e medical cases; e equals d; Obtaining feedback content of the target object for each of the e medical cases to obtain e feedback content; Replacing the d feedback contents in the a feedback contents with the e feedback contents to obtain updated a feedback contents; For the updated a feedback contents, the step of detecting whether the a feedback contents meet a second preset condition is performed.

9. An AI teaching device based on traditional Chinese medicine culture, characterized in that: The device comprises: an acquisition unit, a model building unit, and a teaching unit; wherein: The acquisition unit is used to acquire a target TCM course of a target object and a target teaching plan for the target TCM course; the target TCM course includes one of the following: Zhangshu TCM course, Chinese medicine course, basic TCM theory course, acupuncture and massage course; and acquire a target TCM database corresponding to the target TCM course; The model building unit is used to build a TCM AI model according to the target TCM database and the target teaching plan to obtain a target TCM AI model; The acquisition unit is further used to acquire the target medical question for the target TCM course and the personal information of the target subject; The teaching unit is used to input the target medical question and the personal information into the target TCM AI model to obtain a first reply content; obtain the first feedback content of the target object for the first reply content; determine whether the target object meets the first preset condition based on the first feedback content; when the target object meets the first preset condition, generate a corresponding medical case based on the target medical question to obtain a medical case; a is an integer greater than 1; obtain the feedback content of the target object for each of the a medical cases to obtain a feedback content; detect whether the a feedback content meets the second preset condition; when the a feedback content meets the second preset condition, determine that the target object has mastered the TCM knowledge corresponding to the target medical question.

10. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 8.

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