An intelligent medical self-service comprehensive service system

Abstract

The present invention relates to an intelligent medical self-service integrated service system. The self-service integrated service system at least includes a cloud server, a patient terminal and a medical terminal. The patient terminal can guide the patient to log in and / or register to the self-service integrated service system to obtain patient data including the patient's medical purpose, and send the patient data to the cloud server. In response to the receipt of the patient data, the cloud server can input the patient data into a department classification model to obtain the corresponding department, and send the patient's medical purpose to the medical terminal configured for the corresponding department. Medical staff in the corresponding department establish contact with the corresponding patient through the medical terminal for initial diagnosis.

Description

Technical Field

[0001] The present invention relates to the field of medical services, and in particular to an intelligent medical self-service integrated service system. Background Art

[0002] In the current medical services, it is time-consuming and laborious to guide patients to the correct department. Professional medical staff are needed as guides to assign the corresponding department to each patient based on the description of their condition. After explaining the department, the guide is also required to explain the route to the corresponding department to the patient.

[0003] The existing technology mainly uses professional medical staff to perform manual triage and verbal guidance to guide patients to the correct department. However, due to the large number of diseases, a professional medical staff may not be able to assign a patient to a correct department. It is often the case that patients visit multiple departments before they determine the department for their own illness. Therefore, the efficiency and accuracy of arranging professional medical staff to perform manual triage and verbal guidance are low, and it consumes the energy of professional medical staff, resulting in a waste of medical resources.

[0004] In summary, the present invention provides an intelligent medical self-service integrated service system to solve the problems existing in the prior art.

[0005] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides an intelligent medical self-service integrated service system. The self-service integrated service system at least includes a cloud server, a patient terminal and a medical terminal. The patient terminal can guide the patient to log in and / or register to the self-service integrated service system to obtain patient data including the patient's medical purpose, and send the patient data to the cloud server. In response to the receipt of the patient data, the cloud server can input the patient data into a department classification model to obtain the corresponding department, and send the patient's medical purpose to the medical terminal configured for the corresponding department. The medical staff in the corresponding department establishes contact with the corresponding patient through the medical terminal for initial diagnosis. Preferably, the present invention can guide the patient to click on a preset entry through the patient terminal to obtain the patient's medical purpose that conforms to the medical terminology expression. The present invention inputs patient data including the patient's medical purpose expressed in medical terms into a department classification model trained with a large amount of historical medical data for automatic triage, thereby freeing professional medical staff from inefficient manual triage work and reducing the waste of medical resources. At the same time, it improves the accuracy of triage and can effectively avoid the situation where patients have to visit multiple departments before determining the department corresponding to their symptoms.

[0007] According to a preferred embodiment, the patient end includes at least a medical purpose inquiry module for obtaining the patient's medical purpose. The medical purpose inquiry module can divide the patient's medical purpose into a first-class purpose that requires the participation of professional medical personnel and a second-class purpose that does not require the participation of professional medical personnel according to whether the realization of the patient's medical purpose requires the participation of professional medical personnel. When the patient's medical purpose is a second-class purpose (such as the patient registers, pays, collects reports, obtains documents, etc.), the cloud server responds to the receipt of the patient data and may not input the patient data including the second-class purpose into the department classification model, but call the corresponding subsystem according to the patient's second-class purpose to achieve the patient's purpose. Preferably, the subsystem includes a registration subsystem, a payment subsystem, a reporting subsystem, etc. When and only when the patient's medical purpose is a first-class purpose that requires the participation of professional medical personnel, the cloud server will match the patient with the corresponding professional medical personnel. The cloud server performs differentiated processing on the patient's medical purpose, which can effectively reduce the energy spent by professional medical personnel on non-medical matters such as issuing numbers and printing reports while meeting the needs of patients, allowing professional medical personnel to devote more energy to the diagnosis and treatment of patients, thereby achieving full utilization of medical resources.

[0008] According to a preferred embodiment, the cloud server inputs patient data including a category of purposes into the department classification model to obtain the corresponding department. The department classification model is established in the following manner, that is, the cloud server obtains and summarizes historical medical data. The historical medical data includes historical medical descriptions and corresponding historical departments. The cloud server trains the memory network model based on the historical medical data to obtain the department classification model. Preferably, using historical medical data including historical medical descriptions and corresponding historical departments as training materials can make full use of historical medical descriptions written by professional medical personnel using professional terms to improve the professional accuracy of the training results.

[0009] According to a preferred embodiment, the cloud server is provided with a department classification model establishment module. The department classification model establishment module establishes and updates the department classification model by acquiring historical medical data to train the memory network model. The department classification model establishment module can achieve continuous learning as the historical medical data continues to increase, thereby achieving continuous expansion of case and morbidity data while establishing the department classification model with high professional accuracy by acquiring historical medical data.

[0010] According to a preferred embodiment, the department classification model establishment module specifically includes: a training execution unit, a training evaluation unit, a training adjustment unit and a model generation unit. The training execution unit is used to input the historical condition description into the memory network model to obtain the probability of each department corresponding to the historical condition description. The training evaluation unit is used to evaluate the training effect of the training execution unit and determine whether to stop training according to the evaluation result. Preferably, when the department with the highest probability obtained by the training execution unit is the same as the historical department corresponding to the historical condition description, the evaluation result is passed, and the determination result is to stop training. The evaluation result of the training evaluation unit also includes retraining. When the probability of the historical department corresponding to the historical condition description is not the highest after the execution of training, but is in the top three of the probabilities of each department in descending order, and the sum of the top three probabilities is not less than 90%, in addition, after the top three are arranged in descending order, the ratio of the probability value of the latter item to the previous item is not less than 0.8, the evaluation result is retraining, and the determination result is to continue training. When the evaluation result of the training evaluation unit is retraining, the training execution unit continues to perform training without adjusting the parameters of the memory network model until the evaluation result is passed. When the probability of the historical department corresponding to the historical condition description after training does not meet the evaluation conditions of passing or retraining, the evaluation result is invalid training, and the judgment result is retraining. The training adjustment unit is used to update the parameters of the memory network model to perform the next training when the evaluation result of the training evaluation unit is invalid training. The model generation unit is used to generate the department classification model from the current memory network model when the training evaluation unit determines that the training is stopped. Preferably, the department classification model establishment module establishes a condition description vocabulary by strictly training the historical medical data so that the historical condition description and the corresponding historical department. Preferably, the department classification model generated by the department classification model establishment module can analyze the received patient data to obtain the department that matches the patient's medical purpose. Preferably, the condition description vocabulary established by the department classification model establishment module during the training process can provide vocabulary data support for the patient's guidance to the patient, so that the preset entry selected by the patient to express his or her own medical purpose conforms to the medical terminology.

[0011] According to a preferred embodiment, the medical staff can set address data including at least one location through the medical terminal according to the initial diagnosis result and transmit it to the cloud server and / or the patient terminal to generate a patient route. Preferably, when the cloud server sends the patient's medical purpose to the medical terminal configured for the corresponding department, the medical staff in the corresponding department can establish contact with the corresponding patient through the medical terminal for remote initial diagnosis and / or consultation. Preferably, after the initial diagnosis, for patients who need face-to-face consultation, the medical staff transmits the face-to-face consultation address data to the cloud server or the patient terminal through the medical terminal to generate a patient route to guide the patient to the corresponding department for treatment.

[0012] According to a preferred embodiment, the patient end may include a self-service machine set up inside the hospital and a smart terminal carried by the patient personally. When the patient logs in and / or registers to the self-service integrated service system through the self-service machine, the self-service machine generates the patient's route and models a part of the model corresponding to the hospital for the patient's route. When the patient logs in and / or registers to the self-service integrated service system through a smart terminal carried by the patient personally, the patient's route is generated by the cloud server and a corresponding local model is modeled. When and only when the patient end is a self-service machine set up inside the hospital, the medical staff will send the face-to-face consultation address data to the patient end, i.e., the self-service machine. When the patient end is a private device such as a smart terminal carried by the patient personally, the medical staff will send the face-to-face consultation address data to the cloud server.

[0013] According to a preferred embodiment, the local model corresponding to the patient's route is sent to the smart terminal carried by the patient in the form of a video stream. The smart terminal can receive the video stream by pairing with the self-service machine. After receiving the video stream of the corresponding local model, the smart terminal carried by the patient can display the local model at any time to provide route navigation for the patient. Preferably, after the patient enters the hospital, especially when the communication signal is interfered, the patient can go to the destination by viewing the navigation video on the smart terminal.

[0014] According to a preferred embodiment, when a patient logs in and / or registers to the self-service integrated service system through the smart terminal, the cloud server can read the location information of the smart terminal. In response to the receipt of the location information, the self-service integrated service system performs differentiated operations for the corresponding patient to form a local model that matches the patient's location. Preferably, when the location information of the smart terminal shows that the smart terminal is within the hospital location range, that is, when the patient is within the hospital location range, the smart terminal prompts the patient to use the self-service machine to obtain the local model so that when the smart terminal cannot successfully receive the local model video stream generated by the cloud server due to blocked communication signals, video navigation can still be obtained.

[0015] According to a preferred embodiment, the cloud server is able to access a public map database, and the local model generated and modeled by the cloud server matches the public map. When the patient logs in and / or registers to the self-service integrated service system through the smart terminal and uses the smart terminal to navigate to the hospital entrance along the public map, the smart terminal switches from public map navigation to video navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a simplified schematic diagram of a preferred embodiment provided by the present invention;

[0017] Figure 2 It is a communication relationship diagram of a patient logging in and / or registering to a self-service integrated service system through an intelligent terminal;

[0018] Figure 3 It is a communication relationship diagram for patients to log in and / or register to the self-service integrated service system through a self-service machine.

[0019] Reference numerals list

[0020] 100: Self-service comprehensive service system; 110: Cloud server; 120: Patient end; 121: Self-service machine; 122: Smart terminal; 130: Medical end. DETAILED DESCRIPTION

[0021] The following is combined with Figures 1 to 3 Provide detailed explanation.

[0022] The intelligent medical self-service integrated service system provided by the present invention guides patients to click preset entries to obtain the patient's medical purpose that conforms to the medical terminology. The present invention inputs the patient data including the patient's medical purpose that conforms to the medical terminology into the department classification model trained with a large amount of historical medical data for automatic triage.

[0023] Example 1

[0024] The present invention provides an intelligent medical self-service integrated service system 100. Figure 1 , the self-service integrated service system 100 at least includes a cloud server 110, a patient terminal 120 and a medical terminal 130. The patient terminal 120 can guide the patient to log in and / or register to the self-service integrated service system 100 to obtain patient data including the patient's medical purpose, and send the patient data to the cloud server 110. In response to the receipt of the patient data, the cloud server 110 can input the patient data into the department classification model to obtain the corresponding department, and send the patient's medical purpose to the medical terminal 130 configured for the corresponding department. The medical staff in the corresponding department establishes contact with the corresponding patient through the medical terminal 130 for initial diagnosis. Preferably, the present invention can guide the patient to click on the preset entry through the patient terminal 120 to obtain the patient's medical purpose that conforms to the medical terminology expression. The present invention inputs patient data including the patient's medical purpose expressed in medical terms into a department classification model trained with a large amount of historical medical data for automatic triage, thereby freeing professional medical staff from inefficient manual triage work, reducing the waste of medical resources, and improving the accuracy of triage. It can effectively avoid the situation where patients have to visit multiple departments before determining the department corresponding to their symptoms.

[0025] Preferably, the patient end 120 includes at least a medical purpose inquiry module for obtaining the patient's medical purpose. The medical purpose inquiry module can divide the patient's medical purpose into a first-class purpose that requires the participation of professional medical personnel and a second-class purpose that does not require the participation of professional medical personnel according to whether the realization of the patient's medical purpose requires the participation of professional medical personnel. When the patient's medical purpose is a second-class purpose (such as the patient registers, pays, collects reports, obtains documents, etc.), the cloud server 110 responds to the receipt of the patient data and may not input the patient data including the second-class purpose into the department classification model, but call the corresponding subsystem according to the patient's second-class purpose to achieve the patient's purpose. Preferably, the subsystem includes a registration subsystem, a payment subsystem, a reporting subsystem, etc. When and only when the patient's medical purpose is a first-class purpose that requires the participation of professional medical personnel, the cloud server 110 will match the patient with the corresponding professional medical personnel. The cloud server 110 performs differentiated processing on the patient's medical purpose, which can effectively reduce the energy spent by professional medical personnel on non-medical matters such as issuing numbers and printing reports while meeting the needs of patients, allowing professional medical personnel to devote more energy to the diagnosis and treatment of patients and thus fully utilize medical resources.

[0026] Preferably, the cloud server 110 inputs patient data including a category of purposes into the department classification model to obtain the corresponding department. The department classification model is established in the following manner, that is, the cloud server 110 obtains and summarizes historical medical data. The historical medical data includes historical medical descriptions and corresponding historical departments. The cloud server 110 trains the memory network model based on the historical medical data to obtain a department classification model. Preferably, using historical medical data including historical medical descriptions and corresponding historical departments as training materials can make full use of the historical medical descriptions written by professional medical personnel using professional terms to improve the professional accuracy of the training results.

[0027] Preferably, the cloud server 110 is provided with a department classification model establishment module. The department classification model establishment module trains the memory network model by acquiring historical medical data to achieve the establishment and update of the department classification model. While the department classification model establishment module establishes a department classification model with high professional accuracy by acquiring historical medical data, it can achieve continuous learning as the historical medical data continues to increase, thereby achieving continuous expansion of case and morbidity data.

[0028] Preferably, the department classification model establishment module specifically includes: a training execution unit, a training evaluation unit, a training adjustment unit and a model generation unit. The training execution unit is used to input the historical condition description into the memory network model to obtain the probability of each department corresponding to the historical condition description. The training evaluation unit is used to evaluate the training effect of the training execution unit and determine whether to stop the training according to the evaluation result. Preferably, when the department with the highest probability obtained by the training execution unit is the same as the historical department corresponding to the historical condition description, the evaluation result is passed, and the determination result is to stop the training. The evaluation result of the training evaluation unit also includes retraining. When the probability of the historical department corresponding to the historical condition description is not the highest after the execution of the training, but is in the top three of the probability of each department in descending order, and the sum of the top three probabilities is not less than 90%, in addition, after the top three are arranged in descending order, the ratio of the probability value of the latter item to the previous item is not less than 0.8, the evaluation result is retraining, and the determination result is to continue training. When the evaluation result of the training evaluation unit is retraining, the training execution unit continues to perform training without adjusting the parameters of the memory network model until the evaluation result is passed. When the probability of the historical department corresponding to the historical condition description after training does not meet the evaluation conditions of passing or the evaluation conditions of retraining, the evaluation result is invalid training, and the judgment result is retraining. The training adjustment unit is used to update the parameters of the memory network model for the next training when the evaluation result of the training evaluation unit is invalid training. The model generation unit is used to generate a department classification model from the current memory network model when the training evaluation unit determines that the training is stopped. Preferably, the department classification model establishment module matches the historical condition description with the corresponding historical department by strictly training the historical medical data, and establishes a condition description vocabulary. Preferably, the department classification model generated by the department classification model establishment module can analyze the received patient data to obtain the department that matches the patient's medical purpose. Preferably, the condition description vocabulary established by the department classification model establishment module during the training process can provide vocabulary data support for the patient end 120 to guide the patient, so that the preset entry selected by the patient to express his or her own medical purpose conforms to the medical terminology.

[0029] Preferably, medical staff can set address data including at least one location through the medical terminal 130 according to the initial diagnosis results and transmit it to the cloud server 110 and / or the patient terminal 120 to generate the patient's travel route. Preferably, when the cloud server 110 sends the patient's medical purpose to the medical terminal 130 configured for the corresponding department, the medical staff in the corresponding department can establish contact with the corresponding patient through the medical terminal 130 for remote initial diagnosis and / or consultation. Preferably, after the initial diagnosis, for patients who need face-to-face consultation, the medical staff transmits the face-to-face consultation address data to the cloud server 110 or the patient terminal 120 through the medical terminal 130 to generate the patient's travel route so as to guide the patient to the corresponding department for treatment.

[0030] Preferably, the patient end 120 may include a self-service machine 121 set inside the hospital and a smart terminal 122 carried by the patient. Preferably, the smart terminal 122 may include devices such as smart phones, smart bracelets, and tablet computers. When the patient logs in and / or registers to the self-service integrated service system 100 through the self-service machine 121, the self-service machine 121 generates the patient's route and models a part of the model corresponding to the hospital for the patient's route. When the patient logs in and / or registers to the self-service integrated service system 100 through the smart terminal 122 carried by the patient, the patient's route is generated by the cloud server 110 and the corresponding local model is modeled. When and only when the patient end 120 is a self-service machine 121 set inside the hospital, the medical staff will send the face-to-face consultation address data to the patient end 120, that is, the self-service machine 121. When the patient end 120 is a private device such as a smart terminal 122 carried by the patient, the medical staff sends the face-to-face consultation address data to the cloud server 110.

[0031] Preferably, the local model corresponding to the patient's route is sent to the patient's personal smart terminal 122 in the form of a video stream. The smart terminal 122 can receive the video stream by pairing with the self-service machine 121. After receiving the video stream of the corresponding local model, the smart terminal 122 carried by the patient can display the local model at any time to provide route navigation for the patient. Preferably, after the patient enters the hospital, especially when the communication signal is interfered, the patient can go to the destination by viewing the navigation video on the smart terminal 122.

[0032] Preferably, when the patient logs in and / or registers to the self-service integrated service system 100 through the smart terminal 122, the cloud server 110 can read the location information of the smart terminal 122. In response to the receipt of the location information, the self-service integrated service system 100 performs differentiated operations for the corresponding patient to form a local model that matches the patient's location. Preferably, the location information of the smart terminal 122 shows that the smart terminal 122 is located within the hospital location range, that is, when the patient is within the hospital location range, the smart terminal 122 prompts the patient to go to the self-service machine 121 to obtain the local model, so that when the smart terminal 122 cannot smoothly receive the local model video stream generated by the cloud server 110 due to the obstruction of the communication signal, the patient can still obtain video navigation.

[0033] Preferably, the cloud server 110 can access a public map database. The local model generated and modeled by the cloud server 110 can be matched with the public map. When the patient logs in and / or registers to the self-service integrated service system 100 through the smart terminal 122 and uses the smart terminal 122 to navigate to the hospital entrance along the public map, the smart terminal 122 switches from public map navigation to video navigation.

[0034] Example 2

[0035] This embodiment is a further improvement of the embodiment 1, and the repeated contents are not repeated here. Preferably, the user can enter the self-service comprehensive service system through the intelligent terminal 122 to seek medical treatment. Figure 2, the user can establish data communication with the cloud server 110 through the smart terminal 122. In addition to establishing data communication with the smart terminal 122, the cloud server 110 can also establish data communication with the medical terminal 130 set in the hospital department. Preferably, when the communication of the smart terminal 122 is stable and is located outside the hospital, the patient can log in and / or register to the self-service integrated service system 100 through the smart terminal 122. Preferably, the smart terminal 122 guides the patient to operate the patient terminal 120 based on the data communication with the cloud server 110 to clarify the patient's medical purpose. When the patient's medical purpose belongs to a type of purpose that requires the participation of professional medical personnel, the cloud server 110 matches the patient with the corresponding professional medical personnel by establishing data communication with the medical terminal 130 set in the corresponding department of the hospital. Professional medical personnel are associated with the patient operating the smart terminal 122 through the medical terminal 130 to achieve remote diagnosis and treatment. Preferably, when the patient needs to go to the corresponding department of the hospital after remote diagnosis and treatment, the doctor uploads the address of the department that the patient needs to arrive at to the cloud server 110 through the medical terminal 130. In response to receiving the department address, the cloud server 110 designs a navigation route for the patient to reach the corresponding department from the patient's location based on the location information of the smart terminal 122 and the public map data. Preferably, the navigation route includes a public route from the smart terminal 122 to the hospital entrance and a personal route from the hospital entrance to the corresponding department. Preferably, the public route from the smart terminal 122 to the hospital entrance can be designed by a public navigation application software. Preferably, the personal route from the hospital entrance to the corresponding department is designed by the cloud server 110 and a local model corresponding to the hospital is generated, and the local model is sent to the smart terminal 122 in the form of a video stream. Preferably, the patient can use the smart terminal 122 to navigate to the hospital entrance along the public map. When the patient arrives at the hospital entrance, the smart terminal 122 switches from the public map navigation to the video navigation. Preferably, when performing video navigation, the patient can view the local model at any time, and can observe the model in all directions by adjusting the viewing direction of the virtual character, so as to compare the architectural features in the virtual environment with the architectural features in the actual environment to determine the route of travel.

[0036] Preferably, when the doctor diagnoses the patient's disease through remote diagnosis and treatment, the doctor issues a medical order and uploads the address of the medicine collection department to the cloud server 110. Preferably, the cloud server 110 designs a navigation route for the patient to reach the medicine collection department from his own location based on the location information of the smart terminal 122 and the public map data. Preferably, the navigation route includes a public route from the smart terminal 122 to the hospital entrance and a personal route from the hospital entrance to the medicine collection department. Preferably, the public route from the smart terminal 122 to the hospital entrance can be designed by a public navigation application software. Preferably, the personal route from the hospital entrance to the medicine collection department is designed by the cloud server 110 and a local model corresponding to the hospital is generated and then sent to the patient's smart terminal 122. Preferably, the local model generated by the cloud server 110 can be combined with a public navigation map and transmitted to the smart terminal 122. When the smart terminal 122 receives the navigation route, the patient can first move to the hospital entrance through public navigation, and then reach the medicine collection department through video navigation.

[0037] Preferably, the cloud server 110 obtains the patient's starting position and viewing direction by reading the location information of the smart terminal 122, and navigates the patient to the hospital entrance based on the patient's starting position and viewing direction in combination with public map data. Preferably, the cloud server 110 generates a corresponding local model of the route from the hospital entrance to the corresponding department. Preferably, the cloud server 110 generates the local model in the following manner. Preferably, the cloud server 110 can use the hospital entrance where the patient arrives as the patient's starting position and the direction of entering the hospital as the patient's viewing direction, and at the same time retrieve the plane data and three-dimensional data associated with the corresponding patient's target department and starting position to complete the modeling. Preferably, when the cloud server 110 is modeling a virtual model, it can generate a virtual character image in the model that is the same as the patient's starting position and viewing direction.

[0038] When the patient arrives at the hospital entrance according to the navigation route and starts video navigation, the patient's starting position and viewing direction match the corresponding local model. The patient can determine the characteristic reference objects on the navigation path in the navigation model by adjusting the viewing angle of the corresponding virtual character image based on the movement of the virtual character in the virtual model corresponding to the patient's actual position and viewing direction. When moving in the hospital, the patient can view the local model at any time, and can observe the model in all directions by adjusting the viewing direction of the virtual character to compare the architectural features in the virtual environment with the architectural features in the actual environment to determine the route of travel and realize portable navigation. In the process of the patient navigating through the smart terminal 122, the smart terminal 122 can only receive the navigation video of the virtual model generated by the cloud server 110, which can avoid the internal building structure data of the hospital from being retained on the patient's smart terminal 122.

[0039] Example 3

[0040] This embodiment is a further improvement of Embodiment 1 and Embodiment 2, and repeated contents will not be repeated. The presence of a large number of crowded people within the hospital location means that there are a large number of personal smart terminals 122, which increases the burden on local communication base stations and reduces the communication quality of smart terminals 122; in addition, when patients enter the hospital building, the smart terminals 122 carried by individuals are very likely to be interfered by the building and the communication quality is reduced. When the patient enters the hospital location, if the method of sending the local model video stream generated by the cloud server 110 to the smart terminal 122 is still used, the integrity and stability of the video stream transmission cannot be ensured. Preferably, when the patient is within the hospital location, the patient can navigate by using a self-service machine 121.

[0041] Preferably, the patient can register and / or log in to the self-service integrated service system by matching the smart terminal 122 with the self-service machine 121, and then handle the business by operating the self-service machine 121. After the medical staff uploads the address of the department that the patient needs to arrive to the cloud server 110 through the medical terminal 130, the cloud server 110 transmits the department address to the self-service machine 121 operated by the patient. In response to receiving the department address, the self-service machine 121 generates a corresponding virtual model and sends it to the smart terminal 122 worn by the patient.

[0042] Preferably, the business self-service machine 121, in response to receiving the department address, can generate a treatment path for the corresponding patient according to the corresponding patient's target location (corresponding department address), starting position and sight. The treatment path for the corresponding patient is formed in the following manner, that is, the business self-service machine 121 generates an initial treatment path according to the target location, starting position and sight of the corresponding patient in combination with the hospital structure data retrieved by the self-service machine 121 from the cloud server 110. In response to the generation of the initial treatment path, the business self-service machine 121 connects to the hospital cloud server 110 to obtain the road conditions of each node on the initial treatment path, and corrects the errors on the initial treatment path to generate the final treatment path. In the case where the hospital temporarily closes a certain area on the initial treatment path, the business self-service machine 121 will re-plan the closed path and give priority to directing the patient to the channel temporarily opened by the hospital management department.

[0043] Preferably, the self-service machine 121 can obtain its own position data through the configured positioning unit, and then obtain the starting position and viewing direction of the corresponding patient by mirroring its own position data. The starting position and viewing direction of the corresponding patient are determined in the following manner, that is, the self-service machine 121 uses the positioning unit to perform position detection to determine its own current position and orientation, and after mirroring its own current position and orientation along the display side, the viewing direction and positioning of the current patient of the self-service machine 121 are determined, and the starting position and viewing direction of the patient in the corresponding local model are determined based on the viewing direction and positioning of the current patient in combination with the hospital structure data.

[0044] Preferably, the self-service machine 121 can generate a virtual character image in the generated virtual model. Preferably, the starting position and viewing direction of the virtual character image are determined in the following manner, that is, the self-service machine 121 determines the viewing direction and positioning of the current patient of the self-service machine 121 according to its own current position and orientation, and provides the viewing direction and positioning to the business self-service machine 121, which determines the starting position and viewing direction of the corresponding virtual character image, so that the starting position and viewing direction of the corresponding virtual character image are the same as the actual position and viewing direction of the current patient of the self-service machine 121, thereby facilitating the patient to substitute the model into reality.

[0045] The business self-service machine 121 can prompt the patient who logs into the self-service comprehensive service system 100 to select his or her personalized virtual character image by providing voice prompts to the corresponding patient, and determine the patient's current position and orientation through the positioning function of the smart device and thereby determine the starting position and viewing direction of the virtual character image selected by the patient, thereby ensuring that the position and viewing direction of the virtual character image selected by the patient are consistent with the patient's actual position and viewing direction.

[0046] When the corresponding local model for determining the starting position and viewing direction of the patient is displayed to the patient in the form of a video, the patient can determine the characteristic reference objects on the navigation path in the navigation model by adjusting the viewing angle of the corresponding virtual character image based on the movement of the virtual character corresponding to the patient's actual position and viewing direction in the virtual model. When the medical treatment path generated by the patient by operating the self-service machine 121 intersects with the medical treatment path of at least another patient, the virtual character image corresponding to the current patient will appear in the navigation video generated by another patient by operating the self-service machine 121 to determine the patient's starting position and viewing direction and sent to the smart device, thereby reflecting the actual flow of people in the corresponding area in the local model.

[0047] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. Throughout the text, the features guided by "preferably" are only an optional method and should not be understood as being required. Therefore, the applicant reserves the right to abandon or delete the relevant preferred features at any time. The present invention specification contains multiple inventive concepts, such as "preferably", "according to a preferred embodiment" or "optionally", all of which indicate that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept.

Claims

1. An intelligent medical self-service integrated service system, characterized in that: At least comprising a cloud server (110), a patient terminal (120) and a medical terminal (130); The patient terminal (120) can guide the patient to log in and / or register to the self-service integrated service system to obtain patient data including the patient's medical purpose, and send the patient data to the cloud server (110); The patient end (120) at least includes a medical purpose inquiry module for obtaining the patient's medical purpose, wherein the medical purpose inquiry module can classify the patient's medical purpose into a first type of purpose requiring the participation of professional medical personnel and a second type of purpose not requiring the participation of professional medical personnel according to whether the realization of the patient's medical purpose requires the participation of professional medical personnel, and the second type of purpose includes registration, payment, report collection or receipt acquisition; The cloud server (110) can respond to the receipt of the patient data by inputting the patient data of the first category into the department classification model to obtain the corresponding department, and send the patient's medical purpose to the medical care terminal (130) configured for the corresponding department, and does not input the patient data including the second category into the department classification model, but calls the corresponding subsystem according to the patient's second category to achieve the patient's purpose, and the subsystem includes a registration subsystem, a payment subsystem and a reporting subsystem; Medical personnel in the corresponding department establish contact with the corresponding patient through the medical terminal (130) to conduct a remote initial diagnosis.

2. The intelligent medical self-service integrated service system according to claim 1, characterized in that: The department classification model is established in the following manner: the cloud server (110) obtains and summarizes historical medical data, wherein the historical medical data includes historical disease descriptions and corresponding historical departments; and the memory network model is trained based on the historical medical data to obtain the department classification model.

3. The intelligent medical self-service integrated service system according to claim 2 is characterized in that: The cloud server (110) is provided with a department classification model establishment module; the department classification model establishment module establishes and updates the department classification model by acquiring historical medical data to train a memory network model.

4. The intelligent medical self-service integrated service system according to claim 3 is characterized in that: The department classification model establishment module specifically includes: A training execution unit, used for inputting the historical condition description into the memory network model to obtain the probability that the historical condition description corresponds to each department; A training evaluation unit, used to evaluate the training effect of the training execution unit and determine whether to stop the training according to the evaluation result; A training adjustment unit, used for updating the parameters of the memory network model to perform the next training if the evaluation result of the training evaluation unit is invalid training; The model generating unit is used to generate the department classification model from the current memory network model when the training evaluation unit determines that the training is to be stopped.

5. The intelligent medical self-service integrated service system according to claim 4 is characterized in that: The medical staff can set address data including at least one location through the medical terminal (130) based on the results of the initial diagnosis and transmit it to the cloud server (110) or the patient terminal (120) to generate a patient travel route.

6. The intelligent medical self-service integrated service system according to claim 5, characterized in that: The patient terminal (120) comprises at least a self-service machine (121) arranged inside the hospital and an intelligent terminal (122) carried by the patient personally; When a patient logs in and / or registers to the self-service integrated service system through the self-service machine (121), the self-service machine (121) generates a patient's travel route and models a part of a model corresponding to the hospital for the patient's travel route; When a patient logs in and / or registers to the self-service integrated service system via a personal smart terminal (122), the patient's travel route is generated by the cloud server (110) and a corresponding local model is modeled.

7. The intelligent medical self-service integrated service system according to claim 6, characterized in that: The local model corresponding to the patient's travel route is sent in the form of a video stream to the smart terminal (122) carried by the patient, wherein the smart terminal (122) can receive the video stream by pairing with the self-service machine (121).

8. The intelligent medical self-service integrated service system according to claim 7, characterized in that: When a patient logs in and / or registers to the self-service integrated service system via the smart terminal (122), the cloud server (110) is able to read the location information of the smart terminal (122). In response to receipt of the location information, the self-service integrated service system performs differentiated operations for the corresponding patient to form a local model that matches the patient's location.

9. The intelligent medical self-service integrated service system according to claim 8, characterized in that: The cloud server (110) can access a public map database; and the local model generated and modeled by the cloud server (110) can match the public map. When the patient logs in and / or registers to the self-service integrated service system through the smart terminal (122) and uses the smart terminal (122) to navigate to the hospital entrance along the public map, the smart terminal (122) switches from public map navigation to video navigation.

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