Medical data supervision method based on big data
By setting a minimum collection scope and conversation encryption channel in Internet hospitals, combined with identification models and access mechanisms, the data security and privacy protection issues of Internet hospitals are solved, and the security and reasonable sharing of data transmission are achieved.
Patent Information
- Application Number
- CN202510800444.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-16
- Publication Date
- 2025-09-19
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Internet hospitals have insufficient investment in network security, which makes medical data vulnerable to attacks, leaks of patients' privacy information, and poses risks of illegal acquisition and excessive use during data sharing.
By setting the minimum collection scope, collecting basic patient information and matching the doctor, using the dialogue encryption channel and identification model to transmit information, judging abnormal operations, generating early warning signals, and setting up access mechanisms to control data sharing permissions, a historical medical information database is generated and abnormal sharing operations are intercepted.
Effectively protect patient privacy information, prevent illegal use of data, ensure data transmission security, and avoid data leakage and illegal sharing.
Smart Images
Figure CN120674025A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of big data technology, and in particular to a medical data supervision method based on big data. Background Art
[0002] With the development of information technology and the wave of digitalization, Internet hospitals, as an emerging medical service model, are gradually becoming an important part of the medical industry. Public hospitals across the country have launched Internet medical services, effectively improving patients' medical experience. Patients can seek medical treatment online, doctors can see and prescribe medicine online, pay online, and have medicines delivered to their homes. This provides patients with one-stop medical services, including online consultations, follow-up visits, prescriptions, appointment registration, health consultations, and electronic medical record management. However, while Internet hospitals bring convenience, they still have the following shortcomings in the process of medical data supervision: 1. Medical data is highly sensitive and valuable, making it a key target for hacker attacks. However, internet hospitals have relatively weak investment and protection capabilities in network security, making them vulnerable to attacks. Data leaks have resulted in the disclosure of a large amount of patients' personal information and medical data, infringing on their privacy rights and potentially exposing them to risks such as identity theft and fraud. Therefore, online medical data must be secure and patient privacy must be protected. 2. In existing technologies, a large number of internet hospitals share online medical data. However, during this data sharing process, many internet hospitals fail to implement strict sharing mechanisms, which may result in patient data being illegally obtained or misused by third parties. Furthermore, some internet hospitals are unclear about the scope and purpose of their use of patient medical data, potentially leading to the use of patient data beyond necessary limits, posing a threat to patient privacy. Therefore, in order to solve the above problems, the present invention provides a medical data supervision method based on big data. Summary of the Invention
[0003] In order to solve the above technical problems, the present invention provides a medical data supervision method based on big data; The purpose of the present invention can be achieved by the following technical solution: a method for monitoring medical data based on big data, the method comprising the following steps: Step S1: Set a minimum collection scope in the Internet hospital, collect basic information of the patient through the minimum collection scope, match the doctor according to the patient's basic information, and collect the basic information of the doctor; Step S2: obtaining a patient consultation dialogue transmission unit based on the patient's basic information and the doctor's basic information, and obtaining patient conversation information based on the patient consultation dialogue transmission unit; the patient consultation dialogue transmission unit is provided with a conversation encryption channel and an authentication model; Step S3: The patient conversation information is transmitted to the patient consultation conversation transmission unit corresponding to the doctor through the conversation encryption channel, and abnormal transmission is obtained; whether it is an abnormal operation feature is determined according to the identification model; if not, the complete patient consultation information is collected; if so, an early warning signal is generated; Step S4: Integrate several complete patient medical information data to generate a historical patient medical information database; set up an access mechanism, obtain sharing permissions according to the access mechanism, perform sharing operations on the historical patient medical information database according to the sharing permissions, and obtain abnormal sharing operations, thereby generating an early warning signal; Step S5: intercepting the early warning signal.
[0004] Furthermore, the process of setting the minimum acquisition range includes: Based on big data, symptom characteristics and multi-source data are collected to generate a symptom feature library, and a personal information encryption mechanism is set up. The personal information encryption mechanism is provided with anonymous access rights and partial encryption rights; the anonymous access rights are used to anonymize the patient's name and ID number and automatically generate a code corresponding to the patient, marked as the first letter of the name + the sum of every three digits of the ID number; the partial encryption rights are used to manually encrypt the personal information involuntarily exposed by the patient; and then the symptom feature library and the personal information encryption mechanism are connected to generate a minimum collection range.
[0005] Furthermore, the process of collecting the basic information of the attending doctor includes: The Internet hospital is connected to a patient terminal and a doctor terminal; a symptom input terminal is set on the patient terminal corresponding to the Internet hospital, and the patient inputs symptom information through the symptom input terminal, and the feature information is sent to the symptom feature library according to the minimum collection range, and all symptom features in the symptom information are extracted according to the symptom feature library; a number of doctors corresponding to the Internet hospital are dispatched according to the symptom features, and the patient actively selects the corresponding doctor, and the basic doctor information of the corresponding doctor is collected; then a corresponding patient consultation dialogue transmission unit is generated, and the generation time of the patient consultation dialogue transmission unit is collected; the generation time is associated with the patient's corresponding code to generate a temporary code corresponding to the patient; Integrate the patient's corresponding temporary code and symptom characteristics to generate the patient's basic information.
[0006] Furthermore, the process of setting up the encrypted conversation channel includes: The information generated by the patient terminal and the doctor terminal in the patient consultation dialogue transmission unit is marked as patient dialogue information and doctor consultation dialogue information respectively; and a dialogue information encryption mechanism corresponding to the patient dialogue information is set in the dialogue encryption channel.
[0007] Furthermore, the process of setting up the conversation information encryption mechanism includes: Set up a privacy key feature word library and a privacy key image library; collect patient conversation information and obtain information features corresponding to the patient conversation information, wherein the information features include text, numbers and images; if it is text and numbers, dispatch the corresponding privacy key feature words in the patient conversation information according to the privacy key feature word library, encrypt the privacy key feature words to generate encrypted patient conversation information; if it is an image, extract the image feature data corresponding to the image, encrypt the basic patient information corresponding to the image feature data, and send the image to the privacy key image library for matching. If the match is successful, further encryption is performed to generate encrypted patient conversation information; otherwise, no further encryption is performed to generate encrypted patient conversation information; Obtain the basic information of the doctor, dispatch the doctor's working hospital, name, age, years of service and number, and obtain the corresponding Chinese pinyin and numbers; then arrange the working hospital, name, age, years of service and number in the order of the corresponding Chinese pinyin and numbers to set the conversation information public key corresponding to the patient's conversation information, and obtain the patient's corresponding temporary code as the private key of the conversation information public key; The patient conversation information is used to generate encrypted patient conversation information and a conversation information public key, thereby establishing a conversation information encryption mechanism.
[0008] Furthermore, the setting process of the identification model includes: An abnormal behavior feature database is set up, and abnormal behavior nodes are generated from the abnormal behavior features corresponding to the abnormal behavior feature database to collect the number of abnormal behaviors; then, an identification model of the patient consultation dialogue transmission unit corresponding to the doctor's terminal is established based on the abnormal behavior feature database.
[0009] Furthermore, the acquisition process of the abnormal transmission includes: The patient conversation information is used to generate corresponding encrypted patient conversation information and corresponding conversation information public key through the conversation encryption channel, and is transmitted to the patient consultation conversation transmission unit of the doctor's terminal for display; the doctor's terminal receives the encrypted patient conversation information at the patient consultation conversation transmission unit; the doctor's basic information is dispatched and compared with the conversation information public key. If a match is successful, the doctor inputs the corresponding temporary code for the conversation information public key. If the input is successful, the encrypted patient conversation information is decrypted to obtain the patient conversation information and generate a normal transmission; otherwise, an abnormal transmission is generated, and a warning signal is generated; if the match is unsuccessful, an abnormal transmission is generated, and a warning signal is generated; Set a verification time period and perform private key verification during normal transmission according to the verification time period. If the verification fails, an abnormal transmission is generated and an early warning signal is generated; otherwise, no processing is performed.
[0010] Furthermore, the process of generating the warning signal by the identification model includes: The doctor's terminal operates on the patient's conversation information in the patient's consultation conversation transmission unit, collects the operation features, and sends the features to the abnormal behavior feature database in the identification model for identification. If the identification is successful, the corresponding operation feature is marked as an abnormal operation feature and an early warning signal is generated; otherwise, the corresponding patient conversation information is collected until the end of the consultation to obtain the consultation time period, and all patient conversation information is collected to generate complete patient consultation information; Based on the abnormal behavior node in the identification model, the number of abnormal behaviors corresponding to the operation characteristics of the consultation time period is collected, and a threshold of abnormal behavior number is set. It is compared with the number of abnormal behaviors. If the number of abnormal behaviors is greater than the threshold of abnormal behavior number, an early warning signal is generated; otherwise, no action is taken.
[0011] Furthermore, the process of obtaining the sharing permission includes: The patient's basic information, the doctor's basic information and the complete patient's medical information are associated to generate historical patient medical information; and the ID number is used as the unique identifier to integrate several historical patient medical information to generate a historical patient medical information database for storage; Set up access roles, and set up access scopes and access quantities corresponding to the access roles. Map the access roles with the corresponding access scopes and access data to generate a role access mechanism corresponding to the access roles. Obtain sharing permissions to the historical patient medical information database based on the role access mechanism, and then perform sharing operations on the historical patient medical information database.
[0012] Furthermore, the acquisition process of the abnormal sharing operation includes: The sharing operation is sent to the identification model to obtain the number of sharing operations for comparison, abnormal sharing operations are obtained, and an early warning signal is generated.
[0013] Compared with the prior art, the present invention has the following beneficial effects: the present invention sets a minimum collection range, collects the patient's basic information through the minimum collection range, matches the doctor according to the patient's basic information, and collects the doctor's basic information; obtains the patient's medical conversation transmission unit according to the patient's basic information and the doctor's basic information, and obtains the patient's conversation information according to the patient's medical conversation transmission unit; the patient's medical conversation transmission unit is provided with a conversation encryption channel and an identification model; transmits the patient's conversation information to the patient's medical conversation transmission unit corresponding to the doctor through the conversation encryption channel, and obtains abnormal transmission; determines whether it is an abnormal operation feature according to the identification model, and if not, collects the complete patient's medical information; if so, generates an early warning signal; effectively protects the patient's privacy information during medical treatment; The present invention integrates several complete patient medical information data to generate a historical patient medical information database; sets up an access mechanism, obtains sharing permissions according to the access mechanism, performs sharing operations on the historical patient medical information database according to the sharing permissions, obtains abnormal sharing operations, and then generates an early warning signal; intercepts the early warning signal; and effectively avoids the illegal use of data by a third party during the sharing process. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.
[0015] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION
[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention. Example
[0017] like Figure 1 As shown, a method for supervising medical data based on big data includes the following steps: Step S1: Set a minimum collection scope in the Internet hospital, collect basic information of the patient through the minimum collection scope, match the doctor according to the patient's basic information, and collect the basic information of the doctor; Step S2: obtaining a patient consultation dialogue transmission unit based on the patient's basic information and the doctor's basic information, and obtaining patient conversation information based on the patient consultation dialogue transmission unit; the patient consultation dialogue transmission unit is provided with a conversation encryption channel and an authentication model; Step S3: The patient conversation information is transmitted to the patient consultation conversation transmission unit corresponding to the doctor through the conversation encryption channel, and abnormal transmission is obtained; whether it is an abnormal operation feature is determined according to the identification model; if not, the complete patient consultation information is collected; if so, an early warning signal is generated; Step S4: Integrate several complete patient medical information data to generate a historical patient medical information database; set up an access mechanism, obtain sharing permissions according to the access mechanism, perform sharing operations on the historical patient medical information database according to the sharing permissions, and obtain abnormal sharing operations, thereby generating an early warning signal; Step S5: intercepting the early warning signal. Example
[0018] This embodiment further limits the embodiment 1, and the step S1 is implemented by the following process: The process of collecting basic information of the visiting doctor includes: Based on big data, symptom feature multi-source data is collected to generate a symptom feature library, and a personal information encryption mechanism is set up. The personal information encryption mechanism is provided with anonymous access rights and partial encryption rights; the anonymous access rights are used to anonymize the patient's name and ID number and automatically generate a corresponding code for the patient, marked as the initials of the name + the sum of every three digits of the ID number; the partial encryption rights are used to manually encrypt the patient's involuntarily exposed personal information; and then the symptom feature library and the personal information encryption mechanism are connected to generate a minimum collection scope; In the above embodiment, it should be further explained that the symptom characteristics include but are not limited to all keywords corresponding to the disease, and the corresponding keywords are marked as disease characteristics; the Internet hospital needs to verify the patient's name and ID number when registering, obtain personal information and the opening time of the Internet hospital, but the personal information is stored by the platform and should not be extracted by a third party; therefore, the patient's name and ID number need to be anonymized, and then a temporary code is generated for consultation; the initials of the name include uppercase and lowercase letters; the sum of every three digits of the ID number is used to represent the sum of any three ID numbers; The Internet hospital is connected to a patient terminal and a doctor terminal; a symptom input terminal is set on the patient terminal corresponding to the Internet hospital, and the patient inputs symptom information through the symptom input terminal, and the feature information is sent to the symptom feature library according to the minimum collection range, and all symptom features in the symptom information are extracted according to the symptom feature library; a number of doctors corresponding to the Internet hospital are dispatched according to the symptom features, and the patient actively selects the corresponding doctor, and the basic doctor information of the corresponding doctor is collected; then a corresponding patient consultation dialogue transmission unit is generated, and the generation time of the patient consultation dialogue transmission unit is collected; the generation time is associated with the patient's corresponding code to generate a temporary code corresponding to the patient; Integrate the patient's corresponding temporary code and symptom characteristics to generate the patient's basic information. Example
[0019] This embodiment further limits the embodiment 1, and the step S2 is implemented by the following process: The process of setting up the encrypted conversation channel includes: Mark the information generated by the patient terminal and the doctor terminal in the patient consultation dialogue transmission unit as patient dialogue information and doctor consultation dialogue information respectively; set a dialogue information encryption mechanism corresponding to the patient dialogue information in the dialogue encryption channel; The process of setting up the conversation information encryption mechanism includes: A privacy key feature word library and a privacy key image library are set up; patient conversation information is collected and information features corresponding to the patient conversation information are obtained, wherein the information features include text, numbers and images; if it is text and numbers, the corresponding privacy key feature words in the patient conversation information are dispatched according to the privacy key feature word library, and the privacy key feature words are encrypted to generate encrypted patient conversation information; if it is an image, the image feature data corresponding to the image is extracted, the basic information of the patient corresponding to the image feature data is encrypted, and the image is sent to the privacy key image library for matching. If the match is successful, further encryption is performed to generate encrypted patient conversation information; otherwise, no further encryption is performed to generate encrypted patient conversation information.
[0020] It should be further explained that, in the specific implementation process, the privacy-critical feature word library includes but is not limited to name, age, symptoms, drug name, hospital name, code, etc.; the privacy-critical image library includes but is not limited to X-rays, medical records, and medicine orders, etc.; Obtain the basic information of the doctor, dispatch the doctor's working hospital, name, age, years of service and number, and obtain the corresponding Chinese pinyin and numbers; then arrange the working hospital, name, age, years of service and number in the order of the corresponding Chinese pinyin and numbers to set the conversation information public key corresponding to the patient's conversation information, and obtain the patient's corresponding temporary code as the private key of the conversation information public key; It should be further explained that, in the specific embodiment, the number is the corresponding number of the doctor in the Internet hospital; for example, if the hospital where the doctor works is the First People's Hospital, the corresponding Chinese pinyin is diyirenminyiyuan; if the age is 38, the corresponding number is 38; if the years of service are 10, the corresponding number is 10; the number is 002; and the public key of the conversation information obtained is diyirenminyiyuanzhangwei3810002; The patient conversation information is used to generate encrypted patient conversation information and a conversation information public key, thereby establishing a conversation information encryption mechanism.
[0021] The setting process of the identification model includes: An abnormal behavior feature database is set up, and abnormal behavior nodes are generated from the abnormal behavior features corresponding to the abnormal behavior feature database to collect the number of abnormal behaviors; then, an identification model of the patient consultation dialogue transmission unit corresponding to the doctor's terminal is established based on the abnormal behavior feature database.
[0022] It should be further explained that, in the specific implementation process, the abnormal behavior characteristics include but are not limited to screenshots, copying, forwarding, downloading, etc. Example
[0023] This embodiment further limits the embodiment 1, and the step S3 is implemented by the following process: The process of obtaining abnormal transmission includes: The patient conversation information is used to generate corresponding encrypted patient conversation information and corresponding conversation information public key through the conversation encryption channel, and is transmitted to the patient consultation conversation transmission unit of the doctor's terminal for display; the doctor's terminal receives the encrypted patient conversation information at the patient consultation conversation transmission unit; the doctor's basic information is dispatched and compared with the conversation information public key. If a match is successful, the doctor inputs the corresponding temporary code for the conversation information public key. If the input is successful, the encrypted patient conversation information is decrypted to obtain the patient conversation information and generate a normal transmission; otherwise, an abnormal transmission is generated, and a warning signal is generated; if the match is unsuccessful, an abnormal transmission is generated, and a warning signal is generated; Set a verification time period and perform private key verification during normal transmission according to the verification time period. If the verification fails, an abnormal transmission is generated and an early warning signal is generated; otherwise, no processing is performed.
[0024] The process of generating the early warning signal by the identification model includes: The doctor's terminal operates on the patient's conversation information in the patient's consultation conversation transmission unit, collects the operation features, and sends the features to the abnormal behavior feature database in the identification model for identification. If the identification is successful, the corresponding operation feature is marked as an abnormal operation feature and an early warning signal is generated; otherwise, the corresponding patient conversation information is collected until the end of the consultation to obtain the consultation time period, and all patient conversation information is collected to generate complete patient consultation information; Based on the abnormal behavior node in the identification model, the number of abnormal behaviors corresponding to the operation characteristics of the consultation time period is collected, and a threshold of abnormal behavior number is set. It is compared with the number of abnormal behaviors. If the number of abnormal behaviors is greater than the threshold of abnormal behavior number, an early warning signal is generated; otherwise, no action is taken. Example
[0025] This embodiment further limits the embodiment 1, and the step S4 is implemented by the following process: The process of obtaining the sharing permission includes: The patient's basic information, the doctor's basic information and the complete patient's medical information are associated to generate historical patient medical information; and the ID number is used as the unique identifier to integrate several historical patient medical information to generate a historical patient medical information database for storage; Set up access roles, and set the access scope and access quantity corresponding to the access roles. Map the access roles with the corresponding access scope and access data to generate the role access mechanism corresponding to the access roles. Obtain sharing permissions to the historical patient medical information database based on the role access mechanism, and then perform sharing operations on the historical patient medical information database. It should be further explained that, in the specific implementation process, the sharing operations include but are not limited to screenshots, copying, forwarding, downloading, etc.; The acquisition process of the abnormal sharing operation includes: The sharing operation is sent to the identification model to obtain the number of sharing operations for comparison, abnormal sharing operations are obtained, and an early warning signal is generated.
[0026] The features and exemplary embodiments of various aspects of the present application are described in detail above. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The above description of the embodiments is merely to provide a better understanding of the present application by showing examples of the present application.
[0027] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.
Claims
1. A method for monitoring medical data based on big data, characterized in that: The method comprises the following steps: Step S1: Set a minimum collection scope in the Internet hospital, collect basic patient information within the minimum collection scope, match the doctor according to the patient's basic information, and collect the doctor's basic information; Step S2: obtaining a patient consultation dialogue transmission unit based on the patient's basic information and the doctor's basic information, and obtaining patient conversation information based on the patient consultation dialogue transmission unit; the patient consultation dialogue transmission unit is provided with a conversation encryption channel and an authentication model; Step S3: The patient conversation information is transmitted to the patient consultation conversation transmission unit corresponding to the doctor through the conversation encryption channel, and abnormal transmission is obtained; whether it is an abnormal operation feature is determined according to the identification model; if not, the complete patient consultation information is collected; if so, an early warning signal is generated; Step S4: Integrate several complete patient medical information data to generate a historical patient medical information database; set up an access mechanism, obtain sharing permissions according to the access mechanism, perform sharing operations on the historical patient medical information database according to the sharing permissions, and obtain abnormal sharing operations, thereby generating an early warning signal; Step S5: intercepting the early warning signal.
2. A method for monitoring medical data based on big data according to claim 1, characterized in that: The process of setting the minimum acquisition range includes: Based on big data, symptom characteristics and multi-source data are collected to generate a symptom feature library, and a personal information encryption mechanism is set up. The personal information encryption mechanism is provided with anonymous access rights and partial encryption rights; the anonymous access rights are used to anonymize the patient's name and ID number and automatically generate a code corresponding to the patient, marked as the first letter of the name + the sum of every three digits of the ID number; the partial encryption rights are used to manually encrypt the personal information involuntarily exposed by the patient; and then the symptom feature library and the personal information encryption mechanism are connected to generate a minimum collection range.
3. A method for monitoring medical data based on big data according to claim 2, characterized in that: The process of collecting the basic information of the visiting doctor includes: The Internet hospital is connected to a patient terminal and a doctor terminal; a symptom input terminal is set on the patient terminal corresponding to the Internet hospital, and the patient inputs symptom information through the symptom input terminal, and the feature information is sent to the symptom feature library according to the minimum collection range, and all symptom features in the symptom information are extracted according to the symptom feature library; a number of doctors corresponding to the Internet hospital are dispatched according to the symptom features, and the patient actively selects the corresponding doctor, and the basic doctor information of the corresponding doctor is collected; then a corresponding patient consultation dialogue transmission unit is generated, and the generation time of the patient consultation dialogue transmission unit is collected; the generation time is associated with the patient's corresponding code to generate a temporary code corresponding to the patient; Integrate the patient's corresponding temporary code and symptom characteristics to generate the patient's basic information.
4. A method for monitoring medical data based on big data according to claim 3, characterized in that: The process of setting up the encrypted conversation channel includes: The information generated by the patient terminal and the doctor terminal in the patient consultation dialogue transmission unit is marked as patient dialogue information and doctor consultation dialogue information respectively; and a dialogue information encryption mechanism corresponding to the patient dialogue information is set in the dialogue encryption channel.
5. A method for monitoring medical data based on big data according to claim 4, characterized in that: The process of setting up the conversation information encryption mechanism includes: Set up a privacy key feature word library and a privacy key image library; collect patient conversation information and obtain information features corresponding to the patient conversation information, wherein the information features include text, numbers and images; if it is text and numbers, dispatch the corresponding privacy key feature words in the patient conversation information according to the privacy key feature word library, encrypt the privacy key feature words to generate encrypted patient conversation information; if it is an image, extract the image feature data corresponding to the image, encrypt the basic patient information corresponding to the image feature data, and send the image to the privacy key image library for matching. If the match is successful, further encryption is performed to generate encrypted patient conversation information; otherwise, no further encryption is performed to generate encrypted patient conversation information; Obtain the basic information of the doctor, dispatch the doctor's working hospital, name, age, years of service and number, and obtain the corresponding Chinese pinyin and numbers; then arrange the working hospital, name, age, years of service and number in the order of the corresponding Chinese pinyin and numbers to set the conversation information public key corresponding to the patient's conversation information, and obtain the patient's corresponding temporary code as the private key of the conversation information public key; The patient conversation information is used to generate encrypted patient conversation information and a conversation information public key, thereby establishing a conversation information encryption mechanism.
6. A method for monitoring medical data based on big data according to claim 5, characterized in that: The setting process of the identification model includes: An abnormal behavior feature database is set up, and abnormal behavior nodes are generated from the abnormal behavior features corresponding to the abnormal behavior feature database to collect the number of abnormal behaviors; then, an identification model of the patient consultation dialogue transmission unit corresponding to the doctor's terminal is established based on the abnormal behavior feature database.
7. A method for monitoring medical data based on big data according to claim 6, characterized in that: The acquisition process of the abnormal transmission includes: The patient conversation information is used to generate corresponding encrypted patient conversation information and corresponding conversation information public key through the conversation encryption channel, and is transmitted to the patient consultation conversation transmission unit of the doctor's terminal for display; the doctor's terminal receives the encrypted patient conversation information at the patient consultation conversation transmission unit; the doctor's basic information is dispatched and compared with the conversation information public key. If a match is successful, the doctor inputs the corresponding temporary code for the conversation information public key. If the input is successful, the encrypted patient conversation information is decrypted to obtain the patient conversation information and generate a normal transmission; otherwise, an abnormal transmission is generated, and a warning signal is generated; if the match is unsuccessful, an abnormal transmission is generated, and a warning signal is generated; Set a verification time period and perform private key verification during normal transmission according to the verification time period. If the verification fails, an abnormal transmission is generated and an early warning signal is generated; otherwise, no processing is performed.
8. A method for monitoring medical data based on big data according to claim 7, characterized in that: The process of generating the early warning signal by the identification model includes: The doctor's terminal operates on the patient's conversation information in the patient's consultation conversation transmission unit, collects the operation features, and sends the features to the abnormal behavior feature database in the identification model for identification. If the identification is successful, the corresponding operation feature is marked as an abnormal operation feature and an early warning signal is generated; otherwise, the corresponding patient conversation information is collected until the end of the consultation to obtain the consultation time period, and all patient conversation information is collected to generate complete patient consultation information; Based on the abnormal behavior node in the identification model, the number of abnormal behaviors corresponding to the operation characteristics of the consultation time period is collected, and a threshold of abnormal behavior number is set. It is compared with the number of abnormal behaviors. If the number of abnormal behaviors is greater than the threshold of abnormal behavior number, an early warning signal is generated; otherwise, no action is taken.
9. A method for monitoring medical data based on big data according to claim 8, characterized in that: The process of obtaining the sharing permission includes: The patient's basic information, the doctor's basic information and the complete patient's medical information are associated to generate historical patient medical information; and the ID number is used as the unique identifier to integrate several historical patient medical information to generate a historical patient medical information database for storage; Set up access roles, and set up access scopes and access quantities corresponding to the access roles. Map the access roles with the corresponding access scopes and access data to generate a role access mechanism corresponding to the access roles. Obtain sharing permissions to the historical patient medical information database based on the role access mechanism, and then perform sharing operations on the historical patient medical information database.
10. A method for supervising medical data based on big data according to claim 9, characterized in that: The acquisition process of the abnormal sharing operation includes: The sharing operation is sent to the identification model to obtain the number of sharing operations for comparison, abnormal sharing operations are obtained, and an early warning signal is generated.