5G intelligent medical remote diagnosis platform

Abstract

The invention discloses a 5G intelligent medical remote diagnosis platform, and the platform comprises an illness state importing module which is used for importing illness state related information of a patient, analyzing the illness state related information, and converting the illness state related information into illness state information; the doctor pushing module is used for performing doctor pushing according to the illness state information; the matching selection module is used for selecting a final diagnosis doctor; the doctor end image acquisition module is used for acquiring image information of a doctor end in the diagnosis process; the patient end image acquisition module is used for acquiring image information of a patient end in the diagnosis process; and the communication information acquisition module is used for acquiring communication related information in the diagnosis process. According to the invention, remote diagnosis with better intelligent degree and higher diagnosis accuracy can be realized.

Description

Technical Field

[0001] The present invention relates to the field of telemedicine, and in particular to a 5G smart medical remote diagnosis platform. Background Art

[0002] Medical remote diagnosis is a form of telemedicine, which uses the Internet, mobile communications and other technical means to connect patients and doctors who are separated by distance, to achieve the sharing of medical information, and remote diagnosis and treatment by doctors. The principle is that through remote video calls and medical equipment, doctors can remotely observe and analyze the patient's condition, and make diagnosis and treatment decisions;

[0003] Medical remote diagnosis is generally carried out based on a medical remote diagnosis platform.

[0004] The existing medical remote diagnosis platform has cumbersome operations during the time diagnosis process and cannot accurately match the patient with the corresponding doctor. In addition, during the diagnosis process, it is easy to have communication problems due to network fluctuations, and doctors cannot accurately understand the patient's condition, which has a certain impact on the use of the medical remote diagnosis platform. Therefore, a 5G smart medical remote diagnosis platform is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is: how to solve the problem that the existing medical remote diagnosis platform has cumbersome operations in the time diagnosis process, cannot accurately match the corresponding doctor for the patient, and is prone to communication problems due to network fluctuations during the diagnosis process, which makes it impossible for the doctor to accurately understand the patient's condition. A 5G smart medical remote diagnosis platform is provided.

[0006] The present invention solves the above technical problems through the following technical solutions, which include:

[0007] The condition import module is used for patients to import condition-related information and analyze the condition-related information and convert it into condition information;

[0008] Doctor push module, used to push doctors according to the condition information;

[0009] Matching selected modules for selecting final diagnosis doctors;

[0010] Doctor-side image acquisition module, used to collect doctor-side image information during the diagnosis process;

[0011] The patient-side image acquisition module is used to collect the patient-side image information during the diagnosis process;

[0012] A communication information collection module, used to collect communication related information during the diagnosis process;

[0013] The diagnostic platform processes the imaging information on the doctor's side and obtains prompt information on the doctor's side;

[0014] The diagnostic platform processes the patient's imaging information to obtain prompt information from the patient;

[0015] The diagnostic platform processes the communication-related information to obtain communication prompt information.

[0016] Furthermore, the method of importing the condition-related information by the condition importing module includes a text importing mode, a voice importing mode and a picture importing mode;

[0017] The specific process of analyzing and converting disease-related information into disease information is as follows:

[0018] When the disease-related information imported by the patient is text, the text is imported into a preset disease database, and the disease with the highest matching degree with the text content is extracted from the disease database as the disease information;

[0019] When the relevant information imported by the patient is voice, the voice information is processed, the modal particles in the voice information are removed, and the voice information is converted into text and then imported into a preset disease database, and the disease with the highest matching degree with the text content is extracted from the disease database as the disease information;

[0020] When the relevant information imported by the patient is picture information, content recognition is performed on the picture information to identify the text information in the picture or to identify the picture content information and convert the identified content information into text information. The text information is then imported into a preset medical condition library, and the medical condition with the highest degree of match with the text content is extracted from the medical condition library as the medical condition information.

[0021] Furthermore, the specific process of the doctor push module pushing doctors according to the condition information is as follows:

[0022] Extract the acquired medical condition information, import the medical condition information into a preset doctor allocation database, and retrieve all doctors responsible for the corresponding medical condition information from the preset doctor allocation database;

[0023] Afterwards, the doctor information of all doctors responsible for the corresponding condition information is processed to obtain the doctor score information;

[0024] The three doctors with the highest doctor rating information are selected and pushed.

[0025] Furthermore, the process of obtaining doctor rating information is as follows:

[0026] Extract all the doctor-related information of the doctors responsible for the corresponding condition information, including the doctor's praise rate, the location of the doctor's hospital, the doctor's current duty time, the doctor's communication signal strength, the image clarity of the doctor's image acquisition device, and the doctor's average treatment cycle;

[0027] Then the patient's location is extracted, and the patient's location and the doctor's hospital location are processed to obtain offline medical treatment distance information;

[0028] The doctor's favorable review rate is marked as F1, the offline treatment distance information is marked as F2, the doctor's current duty time information is marked as F3, the doctor's communication signal strength is marked as F4, the image clarity of the doctor's image acquisition device is marked as F5, and the doctor's average treatment cycle is marked as F6;

[0029] Assign a weight M1 to F1, a weight M2 to F2, a weight M3 to F3, a weight M4 to F4, a weight M5 to F5, and a weight M6 to F6, M1+M2+M3+M4+M5+M6=1, M4>M6>M5>M2>M3>M1;

[0030] The doctor's score information can be obtained through the formula F1*M1+F2*M2+F3*M3+F4*M4+F5*M5+F6*M6=Ff.

[0031] Furthermore, the specific process of selecting the final diagnosis doctor by the matching selection module is as follows: extracting three selected doctors, asking the patient whether he has offline medical treatment needs, and when there is an offline medical treatment need, selecting the doctor closest to the patient from the three selected doctors as the final diagnosis doctor;

[0032] When the patient has a history of seeing a doctor among the three selected doctors, that doctor will be selected as the final diagnosis doctor;

[0033] Patients can also manually select the final diagnosis doctor based on their actual needs.

[0034] Furthermore, the process of obtaining the prompt information on the doctor side is as follows:

[0035] Extract the image information of the doctor side, perform voice extraction on the image information of the doctor side, and obtain the voice information of the doctor side;

[0036] Then, the clarity of the image information on the doctor's side is collected to obtain the clarity information of the image information on the doctor's side;

[0037] Then the voice information from the doctor is processed to obtain the voice clarity information;

[0038] When the image clarity information of the doctor's end is less than the preset value or the voice clarity information is less than the preset value, a doctor's end prompt message is generated;

[0039] Perform audio-visual synchronization analysis on the image information and voice information on the doctor's side to obtain audio-visual synchronization rate information. When the audio-visual synchronization rate is less than a preset value, a doctor-side prompt message is generated;

[0040] Analyze and process the voice information of the doctor side to obtain the speech speed information. When the speech speed information is greater than the preset value, a prompt message for the doctor side is generated;

[0041] The image information on the doctor's side is then analyzed and processed, and the doctor's real-time dressing model is collected. The similarity between the real-time dressing model and the preset standard dressing model is then evaluated. When the similarity between the real-time dressing model and the standard dressing model is less than the preset value, a prompt message is generated on the doctor's side.

[0042] Furthermore, the specific process of obtaining the prompt information on the patient side is as follows: the prompt information on the patient side is basically the same as the prompt information on the doctor side, but the prompt information on the patient side only analyzes the image clarity, sound clarity, audio-visual synchronization rate and speech speed information.

[0043] Furthermore, the specific process of obtaining the communication prompt information is as follows:

[0044] Extract the collected communication-related information, including the 5G network information accessed by the doctor and the 5G network information accessed by the patient;

[0045] Process the 5G network information accessed by the doctor to obtain the fluctuation parameters of the doctor;

[0046] Process the 5G network information accessed by the patient to obtain the fluctuation parameters of the patient;

[0047] When the fluctuation parameters on the doctor's side are abnormal or the fluctuation parameters on the patient's side are abnormal, a communication prompt message is generated.

[0048] Furthermore, the acquisition process of the fluctuation parameter of the doctor end and the abnormality determination process are as follows: extracting and collecting the 5G network information accessed by the doctor end, the 5G network information accessed by the doctor end includes data upload rate, data download rate and signal strength information;

[0049] Continuously collect x data upload rates and x data download rates, and plot the x data upload rates and x data download rates into line graphs t1 and t2, respectively, where x≥10;

[0050] Extract the number of times that the angle of the broken line in the broken line graph t1 is greater than the preset value and the number of times that the angle of the broken line in the broken line graph t2 is greater than the preset value, and mark them as Z1 and Z2;

[0051] The number of times the signal strength information is less than the preset value within the preset time period is collected again and marked as Z3;

[0052] Z1, Z2 and Z3 constitute the doctor-side fluctuation parameters;

[0053] When any one of Z1, Z2 and Z3 is greater than the preset value, it means that the fluctuation parameter on the doctor side is abnormal;

[0054] The process of obtaining patient fluctuation parameters and determining abnormalities is the same as that on the doctor's side.

[0055] Compared with the prior art, the present invention has the following advantages: the 5G smart medical remote diagnosis platform allows patients to quickly import information related to their illness, thereby better pushing more suitable doctors to patients for remote diagnosis. Patients can select the final diagnosis doctor from the pushed doctor list according to their own needs and preferences, thereby improving the matching degree between doctors and patients and helping to improve the diagnosis effect. Image collection on the doctor and patient sides can understand the actual medical situation, detect abnormalities in time and then issue warnings to avoid misdiagnosis due to abnormal image collection, etc. The network status is monitored in real time during the medical treatment process, and timely warnings are issued when abnormalities exist, reducing medical interruptions and communication problems caused by network abnormalities, thereby achieving better medical remote diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is the overall structural block diagram of the present invention. DETAILED DESCRIPTION

[0057] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0058] like Figure 1 As shown, this embodiment provides a technical solution: a 5G smart medical remote diagnosis platform, including:

[0059] The condition import module is used for patients to import condition-related information and analyze the condition-related information and convert it into condition information;

[0060] Doctor push module, used to push doctors according to the condition information;

[0061] Matching selected modules for selecting final diagnosis doctors;

[0062] Doctor-side image acquisition module, used to collect doctor-side image information during the diagnosis process;

[0063] The patient-side image acquisition module is used to collect the patient-side image information during the diagnosis process;

[0064] A communication information collection module, used to collect communication related information during the diagnosis process;

[0065] The diagnostic platform processes the imaging information on the doctor's side and obtains prompt information on the doctor's side;

[0066] The diagnostic platform processes the patient's imaging information to obtain prompt information from the patient;

[0067] The diagnostic platform processes the communication-related information to obtain communication prompt information.

[0068] The method of importing the disease condition related information by the disease condition import module includes text import mode, voice import mode and picture import mode;

[0069] The specific process of analyzing and converting disease-related information into disease information is as follows:

[0070] When the disease-related information imported by the patient is text, the text is imported into a preset disease database, and the disease with the highest matching degree with the text content is extracted from the disease database as the disease information;

[0071] When the relevant information imported by the patient is voice, the voice information is processed, the modal particles in the voice information are removed, and the voice information is converted into text and then imported into a preset disease database, and the disease with the highest matching degree with the text content is extracted from the disease database as the disease information;

[0072] When the relevant information imported by the patient is picture information, the picture information is identified, the text information in the picture is identified, or the picture content information is identified and the identified content information is converted into text information, and then the text information is imported into a preset disease database, and the disease with the highest matching degree with the text content is extracted from the disease database as the disease information;

[0073] Through the above process, diversified import methods improve the user experience. Patients can choose the input method according to their own habits and preferences, which improves the convenience and comfort of operation. For patients who are not good at text input, especially the elderly or patients with poor eyesight, the voice import mode provides a more friendly interaction method. The diversified import methods and intelligent information analysis process enable patients to import disease-related information more conveniently and quickly, and doctors can also obtain patients' disease information more quickly and accurately, thereby improving diagnosis efficiency.

[0074] The specific process of the doctor push module pushing doctors according to the condition information is as follows:

[0075] Extract the acquired medical condition information, import the medical condition information into a preset doctor allocation database, and retrieve all doctors responsible for the corresponding medical condition information from the preset doctor allocation database;

[0076] Afterwards, the doctor information of all doctors responsible for the corresponding condition information is processed to obtain the doctor score information;

[0077] The three doctors with the highest doctor rating information are selected and pushed as selected doctors.

[0078] The process of obtaining doctor rating information is as follows:

[0079] Extract all the doctor-related information of the doctors responsible for the corresponding condition information, including the doctor's praise rate, the location of the doctor's hospital, the doctor's current duty time, the doctor's communication signal strength, the image clarity of the doctor's image acquisition device, and the doctor's average treatment cycle;

[0080] Then the patient's location is extracted, and the patient's location and the doctor's hospital location are processed to obtain offline medical treatment distance information;

[0081] The doctor's favorable review rate is marked as F1, the offline treatment distance information is marked as F2, the doctor's current duty time information is marked as F3, the doctor's communication signal strength is marked as F4, the image clarity of the doctor's image acquisition device is marked as F5, and the doctor's average treatment cycle is marked as F6;

[0082] Assign a weight M1 to F1, a weight M2 to F2, a weight M3 to F3, a weight M4 to F4, a weight M5 to F5, and a weight M6 to F6, M1+M2+M3+M4+M5+M6=1, M4>M6>M5>M2>M3>M1;

[0083] The doctor's score information can be obtained through the formula F1*M1+F2*M2+F3*M3+F4*M4+F5*M5+F6*M6=Ff;

[0084] The above process fully considers the relevant imaging factors of the assigned doctor, such as the long distance from the patient, which may affect the patient's offline medical treatment, the poor communication signal on the doctor's side, which will affect the communication of the condition, the unclear image on the doctor's side, and the patient cannot see the relevant materials displayed by the doctor, the average treatment cycle is too long, which does not meet the needs of patients, and the doctor's duty time is too long, and his lack of energy will lead to poor diagnosis effect, etc. Different weights are assigned to different parameters to ensure that the doctor's score is finally obtained, and the doctor's status can be fully understood, thereby providing a more reliable reference for patients to choose doctors;

[0085] The above process realizes intelligent matching to improve matching accuracy. The matching system of medical condition information and doctor assignment library first extracts the acquired medical condition information and imports it into the preset doctor assignment library, and retrieves all doctors responsible for the corresponding medical condition information. Through the preset doctor assignment library, the system can quickly and accurately find doctors that match the medical condition information, thereby improving the matching accuracy and efficiency.

[0086] The system processes the doctor information of all doctors responsible for the corresponding medical information, comprehensively considers factors such as the doctor's praise rate, hospital location, current duty hours, etc., and calculates the doctor's score information through a formula.

[0087] By comprehensively considering multiple factors, the system can more comprehensively evaluate the doctor's ability and service quality, and provide more accurate recommendations to patients.

[0088] Personalized recommendations improve user experience. The system extracts the doctor's praise rate as one of the important indicators for evaluating the doctor's service quality. The praise rate can reflect the doctor's reputation and recognition among patients, and help patients understand the doctor's service quality and professional level.

[0089] The system extracts the patient's location and the doctor's hospital location information, calculates the offline medical treatment distance, and uses it as one of the important factors in evaluating the doctor's recommendation priority. Considering the offline medical treatment distance helps patients choose doctors who are closer to them, reducing the difficulty and inconvenience of medical treatment and improving the user experience.

[0090] The system extracts the doctor's current duty hours information and uses it as a factor in evaluating the doctor's recommendation priority. Considering the doctor's current duty hours helps patients understand the doctor's work status and available appointment times, so that they can arrange their consultation time more reasonably.

[0091] Based on the doctor rating information, the system selects the three doctors with the highest ratings for push notifications. By quickly pushing the selected doctors, the system can reduce patients' confusion and time costs in choosing doctors, thereby improving the overall service efficiency.

[0092] Optimizing the allocation of physician resources can help improve the overall quality and efficiency of medical services, while also reducing physicians' workload and burden.

[0093] This improves the matching accuracy: through an intelligent matching process and comprehensive consideration of multiple factors, the system can provide patients with more accurate doctor recommendations.

[0094] Improve user experience: The personalized recommendation process and factors that take into account the actual needs of patients enable patients to find the most suitable doctor more conveniently and quickly.

[0095] Optimize service efficiency: Quickly push selected doctors and optimize doctor resource allocation to improve overall service efficiency and quality.

[0096] The specific process of the doctor push module to push doctors based on the condition information and the way to obtain the doctor's score information have many advantages, and can provide users with more convenient, efficient and accurate medical services.

[0097] The specific process of the matching selection module selecting the final diagnosis doctor is as follows: extract three selected doctors, ask the patient whether he needs offline medical treatment, and if there is a need for offline medical treatment, select the doctor closest to the patient from the three selected doctors as the final diagnosis doctor;

[0098] When the patient has a history of seeing a doctor among the three selected doctors, that doctor will be selected as the final diagnosis doctor;

[0099] Patients can also manually select the final diagnosis doctor based on their actual needs;

[0100] The above process improves the convenience of medical treatment. Considering the distance of offline medical treatment, when the patient has the need for offline medical treatment, the system will select the doctor closest to the patient from the three selected doctors as the final diagnosis doctor. This approach greatly reduces the distance and time cost of medical treatment for patients and improves the convenience of medical treatment;

[0101] The system allows patients to manually select the final diagnosis doctor based on their actual needs. This flexibility enables patients to choose doctors based on their preferences, trust or other factors, further improving the convenience and satisfaction of medical treatment.

[0102] The setting of giving priority to doctors with previous visits means that when there is a doctor with previous visits among the three selected doctors, the system will give priority to that doctor as the final diagnosis doctor. This practice shows respect for the patient's previous medical records, helps maintain the continuity and stability of medical services, and also enhances the patient's trust in medical services.

[0103] The process of obtaining the prompt information on the doctor side is as follows:

[0104] Extract the image information of the doctor side, perform voice extraction on the image information of the doctor side, and obtain the voice information of the doctor side;

[0105] Then, the clarity of the image information on the doctor's side is collected to obtain the clarity information of the image information on the doctor's side;

[0106] Then the voice information from the doctor is processed to obtain the voice clarity information;

[0107] When the image clarity information of the doctor's end is less than the preset value or the voice clarity information is less than the preset value, a doctor's end prompt message is generated;

[0108] Perform audio-visual synchronization analysis on the image information and voice information on the doctor's side to obtain audio-visual synchronization rate information. When the audio-visual synchronization rate is less than a preset value, a doctor-side prompt message is generated;

[0109] Analyze and process the voice information of the doctor side to obtain the speech speed information. When the speech speed information is greater than the preset value, a prompt message for the doctor side is generated;

[0110] The image information on the doctor's side is then analyzed and processed, and the doctor's real-time dressing model is collected. The similarity between the real-time dressing model and the preset standard dressing model is then evaluated. When the similarity between the real-time dressing model and the standard dressing model is less than the preset value, a prompt message is generated on the doctor's side.

[0111] The specific acquisition process of the patient-side prompt information is as follows: the patient-side prompt information is basically the same as the doctor-side prompt information, but the patient-side prompt information only analyzes the image clarity, sound clarity, audio-visual synchronization rate and speech speed information;

[0112] The above process realizes the monitoring of image and voice clarity. By detecting the clarity of images and voices on the doctor's side, it ensures that doctors can provide high-quality information during remote diagnosis and treatment. When the clarity is insufficient, the system generates a prompt message to prompt doctors or technical support personnel to make timely adjustments, thereby avoiding misdiagnosis or communication barriers caused by clarity issues.

[0113] Audio and video synchronization monitoring, audio and video synchronization is a crucial factor in remote diagnosis and treatment. The system detects the audio and video synchronization rate to ensure that the doctor's voice and image can be presented synchronously, thereby improving the realism and credibility of the diagnosis and treatment process.

[0114] Speech speed monitoring: speaking too fast may make it difficult for patients to understand what the doctor means, while speaking too slowly may prolong the diagnosis and treatment time. The system monitors the doctor's speech speed to ensure that the doctor can communicate at an appropriate speed and improve the efficiency of diagnosis and treatment.

[0115] Dress standard monitoring: The professional image and dress of doctors have an important psychological impact on patients. The system can identify whether the doctor's dress meets the standard, ensure that the doctor can appear professional during the diagnosis and treatment process, and enhance the patient's trust.

[0116] This reduces communication barriers. By ensuring the clarity of images and voices and the synchronization of audio and video, the system reduces communication barriers between doctors and patients, making the diagnosis and treatment process smoother.

[0117] Improve patient satisfaction: Patients have higher and higher expectations for the quality of medical services. By monitoring and prompting doctors' performance during the diagnosis and treatment process, the system helps improve patient satisfaction and trust.

[0118] The specific process of obtaining the communication prompt information is as follows:

[0119] Extract the collected communication-related information, including the 5G network information accessed by the doctor and the 5G network information accessed by the patient;

[0120] Process the 5G network information accessed by the doctor to obtain the fluctuation parameters of the doctor;

[0121] Process the 5G network information accessed by the patient to obtain the fluctuation parameters of the patient;

[0122] When the fluctuation parameters on the doctor's side are abnormal or the fluctuation parameters on the patient's side are abnormal, a communication prompt message is generated.

[0123] The acquisition process of the fluctuation parameters on the doctor side and the abnormality determination process are as follows: extracting and collecting the 5G network information accessed by the doctor side, the 5G network information accessed by the doctor side includes data upload rate, data download rate and signal strength information;

[0124] Continuously collect x data upload rates and x data download rates, and plot the x data upload rates and x data download rates into line graphs t1 and t2 respectively;

[0125] Extract the number of times that the angle of the broken line in the broken line graph t1 is greater than the preset value and the number of times that the angle of the broken line in the broken line graph t2 is greater than the preset value, and mark them as Z1 and Z2;

[0126] The number of times the signal strength information is less than the preset value within the preset time period is collected again and marked as Z3;

[0127] Z1, Z2 and Z3 constitute the doctor-side fluctuation parameters;

[0128] When any one of Z1, Z2 and Z3 is greater than the preset value, it means that the fluctuation parameter on the doctor side is abnormal;

[0129] The process of obtaining the patient's fluctuation parameters and determining abnormalities is the same as that on the doctor's side;

[0130] Through the above process, the communication stability can be improved. By continuously collecting the data upload rate, data download rate and signal strength information of the doctor and patient, the system can monitor the fluctuation of the 5G network in real time. This helps to find the problem of network instability in time, so as to take corresponding measures to solve it. The system determines whether the network fluctuation parameters are abnormal by drawing a line graph and counting the number of times under specific conditions. This method is both intuitive and accurate, and can effectively avoid false alarms and missed reports.

[0131] Optimize the remote diagnosis and treatment experience. A stable communication network is the basis of remote diagnosis and treatment. By monitoring and determining network fluctuation parameters, the system can promptly detect and resolve network delay issues, thereby ensuring real-time communication between doctors and patients. The stability of data upload and download rates is crucial for data transmission in remote diagnosis and treatment. By monitoring these rates, the system can ensure that doctors can clearly see the patient's imaging data, and that patients can accurately receive the doctor's diagnosis and treatment recommendations.

[0132] Enhance the reliability of medical services. By real-time monitoring and determining network fluctuation parameters, the system can issue early warnings before communication failures occur, thereby avoiding interruptions or failures caused by communication problems during diagnosis and treatment. A stable communication network helps maintain the continuity of medical services. Even in the case of network fluctuations, the system can guide doctors or patients to take appropriate measures to maintain stable communication through prompt information.

[0133] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0134] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0135] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. 5G smart medical remote diagnosis platform, characterized by: include: The condition import module is used for patients to import condition-related information and analyze the condition-related information and convert it into condition information; Doctor push module, used to push doctors according to the condition information; Matching selected modules for selecting final diagnosis doctors; Doctor-side image acquisition module, used to collect doctor-side image information during the diagnosis process; The patient-side image acquisition module is used to collect the patient-side image information during the diagnosis process; A communication information collection module, used to collect communication related information during the diagnosis process; The diagnostic platform processes the imaging information on the doctor's side and obtains prompt information on the doctor's side; The diagnostic platform processes the patient's imaging information to obtain prompt information from the patient; The diagnostic platform processes the communication-related information to obtain communication prompt information.

2. The 5G smart medical remote diagnosis platform according to claim 1 is characterized in that: The method of importing the disease condition related information by the disease condition import module includes text import mode, voice import mode and picture import mode; The specific process of analyzing and converting disease-related information into disease information is as follows: When the disease-related information imported by the patient is text, the text is imported into a preset disease database, and the disease with the highest matching degree with the text content is extracted from the disease database as the disease information; When the relevant information imported by the patient is voice, the voice information is processed, the modal particles in the voice information are removed, and the voice information is converted into text and then imported into a preset disease database, and the disease with the highest matching degree with the text content is extracted from the disease database as the disease information; When the relevant information imported by the patient is picture information, content recognition is performed on the picture information to identify the text information in the picture or to identify the picture content information and convert the identified content information into text information. The text information is then imported into a preset medical condition library, and the medical condition with the highest degree of match with the text content is extracted from the medical condition library as the medical condition information.

3. The 5G smart medical remote diagnosis platform according to claim 1 is characterized in that: The specific process of the doctor push module pushing doctors according to the condition information is as follows: Extract the acquired medical condition information, import the medical condition information into a preset doctor allocation database, and retrieve all doctors responsible for the corresponding medical condition information from the preset doctor allocation database; Afterwards, the doctor information of all doctors responsible for the corresponding condition information is processed to obtain the doctor score information; The three doctors with the highest doctor rating information are selected and pushed.

4. The 5G smart medical remote diagnosis platform according to claim 3 is characterized by: The process of obtaining doctor rating information is as follows: Extract all the doctor-related information of the doctors responsible for the corresponding condition information, including the doctor's praise rate, the location of the doctor's hospital, the doctor's current duty time, the doctor's communication signal strength, the image clarity of the doctor's image acquisition device, and the doctor's average treatment cycle; Then the patient's location is extracted, and the patient's location and the doctor's hospital location are processed to obtain offline medical treatment distance information; The doctor's favorable review rate is marked as F1, the offline treatment distance information is marked as F2, the doctor's current duty time information is marked as F3, the doctor's communication signal strength is marked as F4, the image clarity of the doctor's image acquisition device is marked as F5, and the doctor's average treatment cycle is marked as F6; Assign a weight M1 to F1, a weight M2 to F2, a weight M3 to F3, a weight M4 to F4, a weight M5 to F5, and a weight M6 to F6, M1+M2+M3+M4+M5+M6=1, M4>M6>M5>M2>M3>M1; The doctor's score information can be obtained through the formula F1*M1+F2*M2+F3*M3+F4*M4+F5*M5+F6*M6=Ff.

5. The 5G smart medical remote diagnosis platform according to claim 1 is characterized in that: The specific process of the matching selection module selecting the final diagnosis doctor is as follows: extract three selected doctors, ask the patient whether he needs offline medical treatment, and if there is a need for offline medical treatment, select the doctor closest to the patient from the three selected doctors as the final diagnosis doctor; When the patient has a history of seeing a doctor among the three selected doctors, that doctor will be selected as the final diagnosis doctor; Patients can also manually select the final diagnosis doctor based on their actual needs.

6. The 5G smart medical remote diagnosis platform according to claim 1 is characterized in that: The process of obtaining the prompt information on the doctor side is as follows: Extract the image information obtained from the doctor's side, perform voice extraction on the image information from the doctor's side, and obtain the voice information from the doctor's side; Then, the clarity of the image information on the doctor's side is collected to obtain the clarity information of the image information on the doctor's side; Then the voice information from the doctor is processed to obtain the voice clarity information; When the image clarity information of the doctor's end is less than the preset value or the voice clarity information is less than the preset value, a doctor's end prompt message is generated; Perform audio-visual synchronization analysis on the image information and voice information on the doctor's side to obtain audio-visual synchronization rate information. When the audio-visual synchronization rate is less than a preset value, a doctor-side prompt message is generated; Analyze and process the voice information of the doctor side to obtain the speech speed information. When the speech speed information is greater than the preset value, a prompt message for the doctor side is generated; The image information on the doctor's side is then analyzed and processed, and the doctor's real-time dressing model is collected. The similarity between the real-time dressing model and the preset standard dressing model is then evaluated. When the similarity between the real-time dressing model and the standard dressing model is less than the preset value, a prompt message is generated on the doctor's side.

7. The 5G smart medical remote diagnosis platform according to claim 6 is characterized in that: The specific process of obtaining the patient-side prompt information is as follows: the patient-side prompt information is basically the same as the doctor-side prompt information, but the patient-side prompt information only analyzes the image clarity, sound clarity, audio-visual synchronization rate and speech speed information.

8. The 5G smart medical remote diagnosis platform according to claim 1 is characterized in that: The specific process of obtaining the communication prompt information is as follows: Extract the collected communication-related information, including the 5G network information accessed by the doctor and the 5G network information accessed by the patient; Process the 5G network information accessed by the doctor to obtain the fluctuation parameters of the doctor; Process the 5G network information accessed by the patient to obtain the fluctuation parameters of the patient; When the fluctuation parameters on the doctor's side are abnormal or the fluctuation parameters on the patient's side are abnormal, a communication prompt message is generated.

9. The 5G smart medical remote diagnosis platform according to claim 8, characterized in that: The acquisition process of the fluctuation parameters of the doctor end and the abnormality determination process are as follows: extracting and collecting the 5G network information accessed by the doctor end, the 5G network information accessed by the doctor end includes data upload rate, data download rate and signal strength information; Continuously collect x data upload rates and x data download rates, and plot the x data upload rates and x data download rates into line graphs t1 and t2 respectively; Extract the number of times that the angle of the broken line in the broken line graph t1 is greater than the preset value and the number of times that the angle of the broken line in the broken line graph t2 is greater than the preset value, and mark them as Z1 and Z2; The number of times the signal strength information is less than the preset value within the preset time period is collected again and marked as Z3; Z1, Z2 and Z3 constitute the doctor-side fluctuation parameters; When any one of Z1, Z2 and Z3 is greater than the preset value, it means that the fluctuation parameter on the doctor side is abnormal; The process of obtaining patient fluctuation parameters and determining abnormalities is the same as that on the doctor's side.

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