Smart medical data processing method based on block chain and related equipment

Abstract

The invention discloses an intelligent medical data processing method and related equipment based on a block chain, and the method comprises the steps: obtaining the medical data information of a target user, and the medical data information of the target user comprises physical sign data information, online diagnosis and treatment data information and historical family disease record information; according to the physical sign data information of the target user, judging the physique of the target user according to a traditional Chinese medicine identification method to obtain a traditional Chinese medicine physique identification result, and predicting the illness condition of the target user according to the traditional Chinese medicine physique identification result to obtain a first characteristic illness information prediction result; judging the online diagnosis and treatment data of the target user according to the online diagnosis and treatment data information of the target user, and predicting the illness condition of the target user to obtain a second feature illness information prediction result; according to the historical family disease record information of the target user, the family member disease condition of the target user is judged, the disease condition of the target user is predicted, and a third feature disease information prediction result is obtained. The method has the effect of improving the intelligent medical data processing efficiency.

Description

Technical Field

[0001] The present application relates to the field of medical data processing technology, and in particular to a blockchain-based smart medical data processing method and related equipment. Background Art

[0002] Currently, medical data refers to data related to medicine, such as various diagnosis and treatment data, data related to technical quality, meaningful medical history information, significant technical data, data on the value of new technologies, scientific research data, and other socially relevant data. Medical data is closely related to people's daily lives, so the way it is handled is crucial.

[0003] Existing smart medical data processing methods refer to data collection and preprocessing, data storage and management, data analysis and mining, data security and privacy protection, etc. However, existing smart medical data processing methods do not consider using the target user's medical data to predict the target user's disease type and disease probability, thereby reminding the target user to prevent the disease. The processing efficiency of smart medical data is low and there is room for improvement. Summary of the Invention

[0004] In order to improve the efficiency of smart medical data processing, this application provides a blockchain-based smart medical data processing method and related equipment.

[0005] First, this application provides a blockchain-based smart medical data processing method that adopts the following technical solutions: A blockchain-based smart medical data processing method includes the following steps: Obtaining target user medical data information, including physical sign data information, online diagnosis and treatment data information, and historical family medical records information; According to the physical sign data information of the target user, the target user's constitution is judged according to the TCM identification method to obtain a TCM constitution identification result, and the target user's disease condition is predicted according to the TCM constitution identification result to obtain a first feature disease information prediction result; According to the online medical treatment data information of the target user, the target user's online medical treatment data is judged to predict the target user's disease condition to obtain a second feature disease information prediction result; According to the historical family disease records of the target user, the disease conditions of the target user's family members are judged to predict the disease conditions of the target user to obtain the third feature disease information prediction result; Creating a target user's predicted disease information dataset based on the first feature disease information prediction result, the second feature disease information prediction result, and the third feature disease information prediction result, and planning disease prevention advice information based on the target user's predicted disease information dataset; The target user's predicted disease information dataset and disease prevention advice information are sent to the user terminal and uploaded to the blockchain system.

[0006] Preferably, the target user's medical data information is acquired based on blockchain technology, and the target user's medical data information includes physical sign data information, online diagnosis and treatment data information, and historical family medical records information; The physical sign data information includes user complexion information, user tongue information, user body shape information, user voice information, and user pulse information; The online diagnosis and treatment data information includes user online consultation content information, user online consultation interval information, user online drug purchase type information and user online drug purchase frequency information; The historical family illness record information includes the historical family disease type information of the target user's family members and the historical family illness severity information when the family members were ill. The historical family illness record information also includes the target user's own historical disease type information and the historical illness severity information of the target user each time he was ill.

[0007] Preferably, based on the user's complexion information in the physical sign data information, the user's physical constitution indicated by the user's complexion is determined to obtain a result of the physical constitution indicated by the complexion; According to the user tongue image information in the physical sign data information, the user's physical constitution indicated by the user tongue image is determined to obtain the physical constitution result indicated by the tongue image; According to the user's body shape information in the physical sign data information, the user's physical constitution indicated by the body shape is determined to obtain a body shape indicated physical constitution result; According to the user voice information in the physical sign data information, determine the user's physical condition indicated by the user's voice to obtain a physical condition indicated by the voice; According to the user pulse information in the physical sign data information, the user's physical constitution shown by the user's tongue and pulse is judged to obtain the pulse-shown physical constitution result.

[0008] Preferably, a TCM constitution database is obtained, wherein the TCM constitution database includes balanced constitution, qi deficiency constitution, yang deficiency constitution, yin deficiency constitution, phlegm-damp constitution, damp-heat constitution, blood stasis constitution, qi stagnation constitution, and special constitution; According to the combination of the constitution display result of the complexion, the constitution display result of the tongue, the constitution display result of the body shape, the constitution display result of the voice and the constitution display result of the pulse, the presence ratio of each constitution of the target user in the traditional Chinese medicine constitution database is determined to obtain a traditional Chinese medicine constitution identification result; Obtaining a TCM constitution disease information database, wherein the TCM constitution disease information database includes disease types of people with different constitutions and the probability of each disease type; Matching the TCM constitution identification result with the TCM constitution disease information database to determine the disease type of the target user's constitution to obtain a first characteristic disease type prediction result and a first characteristic disease probability prediction result for each disease type; The first characteristic disease type prediction result and the first characteristic disease probability prediction result of each disease type are combined to form a first characteristic disease information prediction result.

[0009] Preferably, the user's online consultation content information in the online diagnosis and treatment data is screened and processed, and key words and phrases related to the user's physical constitution and disease are screened to obtain valid consultation content data and retained, and sentences irrelevant to the user's physical constitution and disease are screened and deleted; Based on the effective consultation content data, the disease types that the target users encounter during online diagnosis and treatment are predicted, and the online consultation disease type prediction results are obtained; Based on the online drug purchase type information of users in the online diagnosis and treatment data, the disease types that appear when the target users purchase drugs online are predicted, and the online drug purchase disease type prediction results are obtained; The online consultation disease type prediction result and the online medicine purchase disease type prediction result are combined to form a second characteristic disease type prediction result; The online consultation disease type prediction result is obtained by predicting the disease probability of each disease type in the online consultation disease type prediction result according to the time interval information of the user's online consultation; According to the user's online drug purchase frequency information, the probability of each disease type in the online drug purchase disease type prediction result is predicted to obtain the online drug purchase disease probability prediction result; The online consultation disease probability prediction result and the online medicine purchase disease probability prediction result are combined to form a second feature disease probability prediction result; The second characteristic disease type prediction result and the second characteristic disease probability prediction result are combined to form a second characteristic disease information prediction result.

[0010] Preferably, based on the historical family disease type information in the historical family disease record information, the diseased body parts of the target user's family members during the historical illness process are determined to obtain the historical family diseased body part information; Based on the historical family disease body part information, the target user's disease type affected by family genetics is predicted, and the historical family disease type prediction result is obtained; According to the target user's own historical disease type information, determine the target user's own diseased body parts during the historical illness process to obtain the historical diseased body parts information; Based on the historical diseased body part information, the target user is predicted to have the disease type affected by their own historical diseases, and the historical disease type prediction result is obtained; Obtain the blood relationship between the sick family members and the target user from the historical family illness records, and then analyze the weight ratio of the impact of the family illness; According to the historical family disease severity information when family members were ill, the degree of influence of each disease type in the historical family disease type prediction results is determined to obtain the historical family disease impact coefficient; The historical family disease probability prediction result is obtained by multiplying the historical family disease influence coefficient and the family disease influence weight ratio; Based on the target user's historical disease severity information each time they were sick, the disease probability of each disease type in the historical disease type prediction results is predicted to obtain the historical disease probability prediction results; The historical family disease type prediction result is combined with the historical self-disease type prediction result to form a third characteristic disease type prediction result, the historical family disease probability prediction result is combined with the historical self-disease probability prediction result to form a third characteristic disease probability prediction result, and the third characteristic disease type prediction result is combined with the third characteristic disease probability prediction result to form a third characteristic disease information prediction result.

[0011] Preferably, a dataset of predicted disease information of target users is created; Aggregate the first feature disease information prediction result, the second feature disease information prediction result, and the third feature disease information prediction result into a predicted disease information dataset for the target user; Disease prevention advice is planned for the target user based on the target user's predicted disease information dataset to obtain disease prevention advice information.

[0012] Preferably, a wireless communication module is obtained and a signal connection link is established between the wireless communication module and the target user; Obtain and store the target user's predicted disease information dataset and disease prevention advice information; Based on the wireless communication module, the target user's predicted disease information dataset and disease prevention recommendation information are sent to the user terminal and uploaded to the blockchain system.

[0013] Secondly, this application provides a blockchain-based smart medical data processing device, which adopts the following technical solutions: A blockchain-based smart medical data processing device, including: A user information acquisition module configured to acquire medical data information of a target user, wherein the medical data information of the target user includes physical sign data information, online diagnosis and treatment data information, and historical family medical records information; A first disease information prediction module is configured to determine the target user's constitution according to the TCM identification method based on the target user's physical sign data information to obtain a TCM constitution identification result, and predict the target user's disease condition based on the TCM constitution identification result to obtain a first characteristic disease information prediction result; The second disease type prediction module is configured to determine the target user's online diagnosis and treatment data based on the target user's online diagnosis and treatment data to predict the target user's disease condition and obtain a second characteristic disease information prediction result; A third disease type prediction module is configured to determine the disease conditions of the target user's family members based on the target user's historical family disease records and predict the target user's disease conditions to obtain a third characteristic disease information prediction result; a disease prevention suggestion planning module configured to create a target user's predicted disease information dataset based on the first feature disease information prediction result, the second feature disease information prediction result, and the third feature disease information prediction result, and to plan disease prevention suggestion information based on the target user's predicted disease information dataset; The wireless communication module is configured to send the target user's predicted disease information dataset and disease prevention recommendation information to the user terminal and upload it to the blockchain system.

[0014] In summary, this application includes at least one of the following beneficial technical effects: The target user's constitution is judged by the physical sign data information in the target user's medical data information to obtain the TCM constitution identification result, and the impact of the TCM constitution identification result on the target user's illness is judged to obtain the first feature illness information prediction result. The target user's online diagnosis and treatment data information in the target user's medical data information is judged to predict the target user's illness and obtain the second feature illness information prediction result. The target user's family members' illness is judged by the target user's historical family illness record information to predict the target user's illness and obtain the third feature illness information prediction result. According to the first feature illness information prediction result, the second feature illness information prediction result, and the third feature illness information prediction result, the target user's predicted illness information data set is obtained, and then the target user is reminded to prevent the disease according to the target user's predicted illness information data set, thereby improving the feedback utilization rate of the target user's medical data and thereby improving the efficiency of blockchain-based smart medical data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1This is a flowchart of a blockchain-based smart medical data processing method. Figure 2 This is a schematic diagram of the modules of the blockchain-based smart medical data processing equipment.

[0016] Figure numerals: 1. User information acquisition module; 2. First disease information prediction module; 3. Second disease type prediction module; 4. Third disease type prediction module; 5. Disease prevention suggestion planning module; 6. Wireless communication module. DETAILED DESCRIPTION

[0017] The present application is further described in detail below with reference to the accompanying drawings.

[0018] The embodiments of the present application disclose a blockchain-based smart medical data processing method.

[0019] A blockchain-based smart medical data processing method includes the following steps: Reference Figure 1 Step S1: Obtain target user medical data information, which includes physical sign data information, online diagnosis and treatment data information, and historical family medical records information. Step S1 specifically includes the following sub-steps: Step S11: Obtain the target user's medical data information based on blockchain technology. The target user's medical data information includes physical sign data information, online diagnosis and treatment data information, and historical family medical record information.

[0020] Step S12, the vital signs data information includes user complexion information, user tongue information, user body shape information, user voice information, and user pulse information.

[0021] Step S13, online diagnosis and treatment data information includes user online consultation content information, user online consultation interval information, user online medicine purchase type information and user online medicine purchase frequency information.

[0022] Step S14, the historical family illness record information includes the historical family disease type information of the target user's family members and the historical family illness severity information when the family members were sick. The historical family illness record information also includes the historical disease type information of the target user himself and the historical illness severity information of the target user each time he was sick.

[0023] Reference Figure 1 In step S2, the target user's physical sign data is used to determine the target user's physical condition according to the TCM identification method to obtain a TCM physical condition identification result, and the target user's disease condition is predicted based on the TCM physical condition identification result to obtain a first characteristic disease information prediction result. Step S2 specifically includes the following sub-steps: Step A1: according to the user's complexion information in the physical sign data information, determine the user's physical constitution indicated by the user's complexion to obtain a result of the physical constitution indicated by the complexion.

[0024] For example, a pale complexion in the user's complexion information indicates that the target user has Qi deficiency or Yang deficiency, and the complexion shows that the constitution is Qi deficiency or Yang deficiency. A dark complexion indicates that the target user has blood stasis, and the complexion shows that the constitution is blood stasis. A flushed complexion indicates that the target user has Yin deficiency, and the complexion shows that the constitution is Yin deficiency.

[0025] Step A2: according to the user tongue image information in the physical sign data information, determine the user's physical constitution shown by the user's tongue image to obtain the physical constitution result shown by the tongue image.

[0026] For example, if the user's tongue image information shows a red tongue with yellow fur, then the target user has damp-heat constitution, and the tongue image shows that the constitution is damp-heat constitution.

[0027] Step A3: judging the user's physique as indicated by the user's body shape according to the user's body shape information in the physical sign data information to obtain a body shape indicated physique result.

[0028] For example, if the user's body shape information shows that the target user is obese, it means that the target user has phlegm and dampness, and the body shape will display the constitution as phlegm and dampness. If the user's body shape information shows that the target user is thin, it means that the target user has qi deficiency or yin deficiency, and the body shape will display the constitution as qi deficiency or yin deficiency.

[0029] Step A4: judging the user's physical condition indicated by the user's voice according to the user's voice information in the physical sign data information, and obtaining a physical condition indicated by the voice result.

[0030] For example, if the target user's voice is low and weak in the user voice information, it means that the target user has Qi deficiency, and the voice display constitution result is Qi deficiency. If the target user's voice is loud in the user voice information, it means that the target user has phlegm and dampness, and the voice display constitution result is phlegm and dampness.

[0031] Step A5: According to the user pulse information in the physical sign data information, the user's physical constitution shown by the user's tongue and pulse is determined to obtain the pulse constitution result.

[0032] For example, if the target user's pulse is weak in the user pulse information, it indicates that the target user has Qi deficiency, and the pulse shows that the constitution is Qi deficiency. If the target user's pulse is stringy and slippery in the user pulse information, it indicates that the target user has phlegm and dampness, and the pulse shows that the constitution is phlegm and dampness.

[0033] Step S2 also includes the following sub-steps: Step B1, obtaining a TCM constitution database, which includes balanced constitution, qi deficiency constitution, yang deficiency constitution, yin deficiency constitution, phlegm-damp constitution, damp-heat constitution, blood stasis constitution, qi stagnation constitution, and special constitution.

[0034] Step B2, based on the combination of the constitution results displayed by complexion, tongue, body shape, voice and pulse, the presence ratio of each constitution in the TCM constitution database of the target user is determined to obtain the TCM constitution identification result.

[0035] Step B3: Obtain a TCM constitution disease information database, which includes disease types of people with different constitutions and the probability of each disease type.

[0036] For example, if the target user's constitution is qi deficiency, the types of diseases he or she may suffer from are respiratory diseases such as colds, allergic rhinitis, chronic bronchitis, asthma, and digestive system diseases such as chronic gastritis and chronic enteritis.

[0037] Step B4, matching the TCM constitution identification result with the TCM constitution disease information database, determining the disease type of the target user's constitution to obtain the first characteristic disease type prediction result and the first characteristic disease probability prediction result of each disease type.

[0038] Step B5: The first characteristic disease type prediction result and the first characteristic disease probability prediction result of each disease type are combined to form the first characteristic disease information prediction result.

[0039] Reference Figure 1 In step S3, the target user's online medical treatment data is used to predict the target user's disease condition based on the target user's online medical treatment data to obtain a second characteristic disease information prediction result. Step S3 specifically includes the following sub-steps: Step S31, screen and process the user's online consultation content information in the online diagnosis and treatment data, screen out key words and phrases related to the user's physical constitution and disease to obtain valid consultation content data and retain the valid consultation content data, and screen and delete sentences that are not related to the user's physical constitution and disease.

[0040] Step S32: predict the disease type that the target user may encounter during online diagnosis and treatment based on the valid consultation content data, and obtain the online consultation disease type prediction result.

[0041] Step S33: predict the disease type that the target user may encounter when purchasing medicine online based on the user's online medicine purchase type information in the online diagnosis and treatment data, and obtain the online medicine purchase disease type prediction result.

[0042] Step S34: The online consultation disease type prediction result and the online medicine purchase disease type prediction result are combined to form a second characteristic disease type prediction result.

[0043] Step S35: predict the disease probability of each disease type in the online consultation disease type prediction result based on the time interval between the user's online consultations to obtain an online consultation disease probability prediction result. The longer the time interval between the user's online consultations, the greater the online consultation disease probability prediction result.

[0044] Step S36: Predict the probability of each disease type in the online drug purchase disease type prediction result based on the user's online drug purchase frequency information to obtain an online drug purchase disease probability prediction result. The higher the user's online drug purchase frequency, the greater the online drug purchase disease probability prediction result.

[0045] Step S37: The online consultation disease probability prediction result and the online medicine purchase disease probability prediction result are combined to form a second characteristic disease probability prediction result.

[0046] Step S38: The second characteristic disease type prediction result and the second characteristic disease probability prediction result are combined to form a second characteristic disease information prediction result.

[0047] Reference Figure 1 In step S4, the target user's family members' illness conditions are determined based on the target user's historical family illness records, and the target user's illness conditions are predicted to obtain a third characteristic illness information prediction result. Step S4 specifically includes the following sub-steps: Step S41 , according to the historical family disease type information in the historical family disease record information, determine the diseased body parts of the target user's family members during the historical illness process to obtain the historical family diseased body part information.

[0048] Step S42: predict the type of disease that the target user may have due to family genetic influence based on the historical family disease body part information, and obtain the historical family disease type prediction result.

[0049] Step S43 , according to the target user's historical disease type information, the target user's historical diseased body parts are determined to obtain historical diseased body parts information.

[0050] Step S44, predicting the type of disease that the target user may have due to his or her own historical diseases based on the historical information of the affected body parts, and obtaining a historical disease type prediction result.

[0051] Step S45: Obtain the blood relationship between the target user and the sick family members in the historical family illness record information, and then analyze the family illness impact weight ratio. The closer the blood relationship between the sick family members and the target user, the greater the family illness impact weight ratio.

[0052] Step S46, based on the historical family disease severity information when family members were ill, determine the degree of influence of each disease type in the historical family disease type prediction results to obtain the historical family disease influence coefficient.

[0053] Step S47 , performing multiplication calculation based on the historical family disease impact coefficient and the family disease impact weight ratio to obtain the historical family disease probability prediction result.

[0054] Step S48 , based on the historical self-illness degree information of the target user each time he or she is ill, the probability of illness of each disease type in the historical self-illness type prediction results is predicted to obtain the historical self-illness probability prediction results.

[0055] Step S49, the historical family disease type prediction results are combined with the historical self-disease type prediction results to form a third characteristic disease type prediction result, the historical family disease probability prediction results are combined with the historical self-disease probability prediction results to form a third characteristic disease probability prediction result, and the third characteristic disease type prediction result is combined with the third characteristic disease probability prediction result to form a third characteristic disease information prediction result.

[0056] Reference Figure 1 In step S5, a target user's predicted disease information dataset is created based on the first feature disease information prediction result, the second feature disease information prediction result, and the third feature disease information prediction result, and disease prevention recommendation information is planned based on the target user's predicted disease information dataset. Step S5 specifically includes the following sub-steps: Step S51: Create a dataset of predicted disease information of the target user.

[0057] Step S52: Aggregate the first characteristic disease information prediction result, the second characteristic disease information prediction result, and the third characteristic disease information prediction result into a predicted disease information dataset of the target user.

[0058] Step S53 , planning disease prevention suggestions for the target user based on the target user's predicted disease information dataset to obtain disease prevention suggestion information.

[0059] Reference Figure 1 In step S6, the target user's predicted disease information dataset and disease prevention advice information are sent to the user terminal and uploaded to the blockchain system. Step S6 specifically includes the following sub-steps: Step S61 , obtaining the wireless communication module 6 and establishing a signal connection link between the wireless communication module 6 and the target user.

[0060] Step S62: Obtain and store the target user's predicted disease information dataset and disease prevention advice information.

[0061] In step S63, the target user's predicted disease information dataset and disease prevention advice are sent to the user terminal and uploaded to the blockchain system based on the wireless communication module 6. It should be noted that the wireless communication module 6 in the embodiment of the present application refers to a wireless communication module 6 based on wireless Bluetooth technology.

[0062] The embodiment of the present application also discloses a blockchain-based smart medical data processing-related device.

[0063] Reference Figure 2 , a blockchain-based smart medical data processing related equipment includes: The user information acquisition module 1 is configured to acquire medical data information of a target user, where the medical data information of the target user includes physical sign data information, online diagnosis and treatment data information, and historical family medical record information.

[0064] The first disease information prediction module 2 is configured to determine the target user's constitution according to the TCM identification method based on the target user's physical sign data information to obtain a TCM constitution identification result, and predict the target user's disease condition based on the TCM constitution identification result to obtain a first characteristic disease information prediction result.

[0065] The second disease type prediction module 3 is configured to determine the target user's online medical data based on the target user's online medical data information and predict the target user's disease condition to obtain a second characteristic disease information prediction result.

[0066] The third disease type prediction module 4 is configured to determine the disease conditions of the target user's family members based on the target user's historical family disease record information, and predict the target user's disease condition to obtain a third characteristic disease information prediction result.

[0067] The disease prevention suggestion planning module 5 is configured to create a predicted disease information dataset of the target user based on the first feature disease information prediction results, the second feature disease information prediction results, and the third feature disease information prediction results, and plan disease prevention suggestion information based on the predicted disease information dataset of the target user.

[0068] The wireless communication module 6 is configured to send the target user's predicted disease information dataset and disease prevention recommendation information to the user terminal and upload it to the blockchain system.

[0069] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A blockchain-based smart medical data processing method, characterized in that: The following steps are involved: Obtaining target user medical data information, including physical sign data information, online diagnosis and treatment data information, and historical family medical records information; According to the physical sign data information of the target user, the target user's constitution is judged according to the TCM identification method to obtain a TCM constitution identification result, and the target user's disease condition is predicted according to the TCM constitution identification result to obtain a first feature disease information prediction result; According to the online medical treatment data information of the target user, the target user's online medical treatment data is judged to predict the target user's disease condition to obtain a second feature disease information prediction result; According to the historical family disease records of the target user, the disease conditions of the target user's family members are judged to predict the disease conditions of the target user to obtain the third feature disease information prediction result; Creating a target user's predicted disease information dataset based on the first feature disease information prediction result, the second feature disease information prediction result, and the third feature disease information prediction result, and planning disease prevention advice information based on the target user's predicted disease information dataset; The target user's predicted disease information dataset and disease prevention advice information are sent to the user terminal and uploaded to the blockchain system.

2. The blockchain-based smart medical data processing method according to claim 1, characterized in that: The steps of obtaining target user medical data information, including physical sign data information, online diagnosis and treatment data information, and historical family medical records information, specifically include: Obtain target user medical data information based on blockchain technology, including physical sign data information, online diagnosis and treatment data information, and historical family medical records information; The physical sign data information includes user complexion information, user tongue information, user body shape information, user voice information, and user pulse information; The online diagnosis and treatment data information includes user online consultation content information, user online consultation interval information, user online drug purchase type information and user online drug purchase frequency information; The historical family illness record information includes the historical family disease type information of the target user's family members and the historical family illness severity information when the family members were ill. The historical family illness record information also includes the target user's own historical disease type information and the historical illness severity information of the target user each time he was ill.

3. The blockchain-based smart medical data processing method according to claim 2, characterized in that: The steps of determining the target user's constitution according to the TCM identification method based on the target user's physical sign data to obtain a TCM constitution identification result, and predicting the target user's illness condition based on the TCM constitution identification result to obtain a first feature illness information prediction result specifically include: According to the user's complexion information in the physical sign data information, the user's physical constitution indicated by the user's complexion is determined to obtain a physical constitution result indicated by the complexion; According to the user tongue image information in the physical sign data information, the user's physical constitution indicated by the user tongue image is determined to obtain the physical constitution result indicated by the tongue image; According to the user's body shape information in the physical sign data information, the user's physical constitution indicated by the body shape is determined to obtain a body shape indicated physical constitution result; According to the user voice information in the physical sign data information, determine the user's physical condition indicated by the user's voice to obtain a physical condition indicated by the voice; According to the user pulse information in the physical sign data information, the user's physical constitution shown by the user's tongue and pulse is judged to obtain the pulse-shown physical constitution result.

4. The blockchain-based smart medical data processing method according to claim 3 is characterized in that: The steps of determining the target user's constitution according to the TCM identification method based on the target user's physical sign data to obtain a TCM constitution identification result, and predicting the target user's illness condition based on the TCM constitution identification result to obtain a first characteristic illness information prediction result also include: Obtaining a TCM constitution database, wherein the TCM constitution database includes balanced constitution, qi deficiency constitution, yang deficiency constitution, yin deficiency constitution, phlegm-damp constitution, damp-heat constitution, blood stasis constitution, qi stagnation constitution, and special constitution; According to the combination of the constitution display result of the complexion, the constitution display result of the tongue, the constitution display result of the body shape, the constitution display result of the voice and the constitution display result of the pulse, the presence ratio of each constitution of the target user in the traditional Chinese medicine constitution database is determined to obtain a traditional Chinese medicine constitution identification result; Obtaining a TCM constitution disease information database, wherein the TCM constitution disease information database includes disease types of people with different constitutions and the probability of each disease type; Matching the TCM constitution identification result with the TCM constitution disease information database to determine the disease type of the target user's constitution to obtain a first characteristic disease type prediction result and a first characteristic disease probability prediction result for each disease type; The first characteristic disease type prediction result and the first characteristic disease probability prediction result of each disease type are combined to form a first characteristic disease information prediction result.

5. The blockchain-based smart medical data processing method according to claim 4 is characterized in that: The step of determining the target user's online medical data based on the target user's online medical data to predict the target user's disease condition and obtaining a second feature disease information prediction result specifically includes: Screening and processing the user's online consultation content information in the online diagnosis and treatment data, filtering out key words and phrases related to the user's physical constitution and disease to obtain valid consultation content data and retaining the valid consultation content data, and filtering out and deleting sentences that are not related to the user's physical constitution and disease; Based on the effective consultation content data, the disease types that the target users encounter during online diagnosis and treatment are predicted, and the online consultation disease type prediction results are obtained; Based on the online drug purchase type information of users in the online diagnosis and treatment data, the disease types that appear when the target users purchase drugs online are predicted, and the online drug purchase disease type prediction results are obtained; The online consultation disease type prediction result and the online medicine purchase disease type prediction result are combined to form a second characteristic disease type prediction result; The online consultation disease type prediction result is obtained by predicting the disease probability of each disease type in the online consultation disease type prediction result according to the time interval information of the user's online consultation; According to the user's online drug purchase frequency information, the probability of each disease type in the online drug purchase disease type prediction result is predicted to obtain the online drug purchase disease probability prediction result; The online consultation disease probability prediction result and the online medicine purchase disease probability prediction result are combined to form a second feature disease probability prediction result; The second characteristic disease type prediction result and the second characteristic disease probability prediction result are combined to form a second characteristic disease information prediction result.

6. The blockchain-based smart medical data processing method according to claim 5, characterized in that: The step of determining the illness status of the target user's family members based on the target user's historical family illness records and predicting the target user's illness status to obtain a third feature illness information prediction result specifically includes: According to the historical family disease type information in the historical family disease record information, determine the diseased body parts of the target user's family members during the historical disease process to obtain the historical family diseased body part information; Based on the historical family disease body part information, the target user's disease type affected by family genetics is predicted, and the historical family disease type prediction result is obtained; According to the target user's own historical disease type information, determine the target user's own diseased body parts during the historical illness process to obtain the historical diseased body parts information; Based on the historical diseased body part information, the target user is predicted to have the disease type affected by their own historical diseases, and the historical disease type prediction result is obtained; Obtain the blood relationship between the sick family members and the target user from the historical family illness records, and then analyze the weight ratio of the impact of the family illness; According to the historical family disease severity information when family members were ill, the degree of influence of each disease type in the historical family disease type prediction results is determined to obtain the historical family disease impact coefficient; The historical family disease probability prediction result is obtained by multiplying the historical family disease influence coefficient and the family disease influence weight ratio; Based on the target user's historical disease severity information each time they were sick, the disease probability of each disease type in the historical disease type prediction results is predicted to obtain the historical disease probability prediction results; The historical family disease type prediction result is combined with the historical self-disease type prediction result to form a third characteristic disease type prediction result, the historical family disease probability prediction result is combined with the historical self-disease probability prediction result to form a third characteristic disease probability prediction result, and the third characteristic disease type prediction result is combined with the third characteristic disease probability prediction result to form a third characteristic disease information prediction result.

7. The blockchain-based smart medical data processing method according to claim 6, characterized in that: The steps of creating a target user's predicted disease information dataset based on the first feature disease information prediction result, the second feature disease information prediction result, and the third feature disease information prediction result, and planning disease prevention recommendation information based on the target user's predicted disease information dataset specifically include: Create a dataset of predicted disease information for target users; Aggregate the first feature disease information prediction result, the second feature disease information prediction result, and the third feature disease information prediction result into a predicted disease information dataset for the target user; Disease prevention advice is planned for the target user based on the target user's predicted disease information dataset to obtain disease prevention advice information.

8. The blockchain-based smart medical data processing method according to claim 7, characterized in that: The step of sending the target user's predicted disease information dataset and disease prevention advice information to the user terminal and uploading it to the blockchain system specifically includes the following sub-steps: Acquiring a wireless communication module (6) and establishing a signal connection link between the wireless communication module (6) and a target user; Obtain and store the target user's predicted disease information dataset and disease prevention advice information; Based on the wireless communication module (6), the target user's predicted disease information data set and disease prevention recommendation information are sent to the user terminal and uploaded to the blockchain system.

9. A blockchain-based smart medical data processing device, characterized in that: The blockchain-based smart medical data processing-related device is used to implement the blockchain-based smart medical data processing method described in any one of claims 1 to 8, including: A user information acquisition module (1) is configured to acquire medical data information of a target user, wherein the medical data information of the target user includes physical sign data information, online diagnosis and treatment data information, and historical family medical record information; A first disease information prediction module (2) is configured to determine the target user's constitution according to the TCM identification method based on the target user's physical sign data information to obtain a TCM constitution identification result, and predict the target user's disease condition based on the TCM constitution identification result to obtain a first characteristic disease information prediction result; A second disease type prediction module (3) is configured to determine the target user's online diagnosis and treatment data based on the target user's online diagnosis and treatment data to predict the target user's disease condition and obtain a second characteristic disease information prediction result; A third disease type prediction module (4) is configured to determine the disease conditions of the target user's family members based on the target user's historical family disease record information and predict the target user's disease condition to obtain a third characteristic disease information prediction result; a disease prevention suggestion planning module (5), configured to create a target user's predicted disease information dataset based on the first feature disease information prediction result, the second feature disease information prediction result, and the third feature disease information prediction result, and to plan disease prevention suggestion information based on the target user's predicted disease information dataset; The wireless communication module (6) is configured to send the target user's predicted disease information data set and disease prevention recommendation information to the user terminal and upload them to the blockchain system.

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