CN113706724A

CN113706724A - Referral supervision method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN113706724A
Application number: CN202111007725.XA
Authority: CN
Inventors: 李术扬
Original assignee: Ping An International Smart City Technology Co Ltd
Current assignee: Shenzhen Ping An Smart Healthcare Technology Co ltd
Priority date: 2021-08-30
Filing date: 2021-08-30
Publication date: 2021-11-26

Abstract

The application relates to the technical field of artificial intelligence and digital medical treatment, and provides a referral supervision method based on artificial intelligence, which comprises the following steps: analyzing the received referral application; determining a supervision area according to the GPS coordinates transferred out of the hospital and the GPS coordinates transferred into the hospital; recognizing vehicle information of the referral vehicle picture, and rendering a 3D virtual vehicle model on a 3D electronic map according to the vehicle information; acquiring the GPS coordinate of the referral vehicle, and determining whether a first referral receiving instruction is generated or not according to the GPS coordinate of the referral vehicle and the GPS coordinate of the referral vehicle; and when a first referral receiving instruction is determined and generated according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer hospital and a second referral receiving instruction confirmed by a supervisor according to a 3D virtual vehicle model rendered on the 3D electronic map is received, calling a disease grade identification model to identify the disease grade of the electronic patient case. The application can rapidly provide the patient with serious illness for treatment service, and improve the treatment experience of the patient.

Description

Referral supervision method, device, equipment and storage medium based on artificial intelligence

Technical Field

The application belongs to the technical field of artificial intelligence, and particularly relates to a referral supervision method, device, equipment and storage medium based on artificial intelligence.

Background

The patient referral and transfer is a behavior which often occurs in the hospitalizing process, and is a link of medical disputes and medical accidents. The supervision of referral becomes an important task for medical supervision authorities. For example, in a new coronary epidemic, transfer data from a febrile patient may adversely affect the control of the epidemic if the report is delayed or missed.

The inventor finds that the existing monitoring system can only count the stock data reported by the hospital, but the probability of misrepresentation exists in the report of the hospital, so that the accuracy of the monitoring result counted by the monitoring system is poor. In addition, the existing monitoring system performs statistics in a form of fetching data at regular time, and the statistical result is displayed in a text form, so that the monitoring result cannot be updated in real time, the monitoring efficiency is low, and the monitoring display mode is not friendly.

Disclosure of Invention

In view of the above, the present invention provides a referral supervision method, apparatus, device and storage medium based on artificial intelligence, and aims to solve the technical problems in the prior art that the supervision result cannot be updated in real time and the supervision efficiency is low.

In order to achieve the above object, the present invention provides a referral supervision method based on artificial intelligence, which comprises:

analyzing the received referral application to obtain a vehicle picture and a patient electronic case for transfer out of the hospital, transfer into the hospital and referral;

determining a supervision area according to the GPS coordinates of the transferred-out hospital and the GPS coordinates of the transferred-in hospital, and loading and displaying a 3D electronic map corresponding to the supervision area on a display screen;

recognizing vehicle information of the referral vehicle picture, and rendering a 3D virtual vehicle model on the 3D electronic map according to the vehicle information;

acquiring the GPS coordinate of the referral vehicle, and determining whether a first referral receiving instruction is generated or not according to the GPS coordinate of the referral vehicle and the GPS coordinate of the referral hospital;

when a first referral receiving instruction is determined and generated according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer-in hospital and a second referral receiving instruction confirmed by a supervisor according to a 3D virtual vehicle model rendered on the 3D electronic map is received, a disease grade identification model is called to identify the disease grade of the patient electronic case;

and generating a referral list according to the disease grade and sending the referral list to the terminal transferred to the hospital.

Preferably, before the parsing of the received referral application, the method further comprises:

receiving a referral application;

saving the referral application to a WebSocket message queue;

and pushing the WebSocket message to the front end in real time through the WebSocket message queue.

Preferably, the recognizing the vehicle information of the referral vehicle picture and rendering a 3D virtual vehicle model on the 3D electronic map according to the vehicle information includes:

identifying the vehicle type, the color and the license plate number of the referral vehicle in the referral vehicle picture;

acquiring a target 3D virtual vehicle model corresponding to the referral vehicle model;

loading the color of the referral vehicle and the license plate number of the referral vehicle to the target 3D virtual vehicle model;

and generating a prompt box at a preset distance position from the target 3D virtual vehicle model, and typing referral information into the prompt box.

Preferably, the determining whether to generate a first referral receiving instruction according to the GPS coordinates of the referral vehicle and the GPS coordinates of the referral hospital includes:

calculating a first distance between the GPS coordinates of the referral vehicle and the GPS coordinates of the referral hospital;

calculating a second distance between the GPS coordinates of the roll-in vehicle and the GPS coordinates of the roll-out hospital;

calculating the ratio of the first distance to the second distance and judging whether the ratio is greater than a preset ratio threshold value;

when the ratio is determined to be larger than the preset ratio threshold, determining to generate a first referral receiving instruction;

determining not to generate a first referral reception instruction when it is determined that the ratio is less than or equal to the preset ratio threshold.

Preferably, the invoking the disease level identification model identifies a disease level of the electronic case of the patient, including:

identifying a plurality of entity types in the patient electronic case, and entity names and entity attributes corresponding to each entity type;

constructing an entity attribute vector according to the types, the entity names and the entity attributes corresponding to the entity types;

and inputting the entity attribute vector into a disease grade identification model to identify the disease grade.

Preferably, before the receiving a second referral receiving instruction confirmed by a supervisor according to a 3D virtual vehicle model rendered on the 3D electronic map, the method further comprises:

controlling the 3D virtual vehicle model to move on the 3D electronic map according to the GPS coordinates of the referral vehicle;

calling an icon style to create a directed moving path pointing to the 3D virtual vehicle model from the first 3D virtual hospital model and displaying the directed moving path;

identifying whether the directed movement path is a path between the first 3D virtual hospital model and the second 3D virtual hospital model;

popping up a referral text box when the directed movement path is identified as a path between the first 3D virtual hospital model and the second 3D virtual hospital model;

and receiving a second referral receiving instruction input by the user through the referral text box.

Preferably, after the generating of the referral list according to the disease grade and the sending of the referral list to the terminal of the transfer-in hospital, the method further comprises:

receiving the information of receiving the referrals fed back by the terminal of the transfer hospital according to the referral list;

and adding the information of receiving a doctor into the historical information of receiving a doctor for real-time statistical analysis and generating a supervision report.

In order to achieve the above object, the present invention further provides a referral supervision apparatus based on artificial intelligence, the apparatus comprising:

the receiving module is used for analyzing the received referral application to obtain a vehicle picture and a patient electronic case for transfer out of the hospital, transfer into the hospital and referral;

the determining module is used for determining a supervision area according to the GPS coordinates of the transferred-out hospital and the GPS coordinates of the transferred-in hospital, and loading and displaying a 3D electronic map corresponding to the supervision area on a display screen;

the rendering module is used for identifying the vehicle information of the referral vehicle picture and rendering a 3D virtual vehicle model on the 3D electronic map according to the vehicle information;

the generation module is used for acquiring the GPS coordinates of the referral vehicle and determining whether a first referral receiving instruction is generated or not according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer hospital;

the calling module is used for calling a disease grade identification model to identify the disease grade of the electronic patient case when a first referral receiving instruction is determined to be generated according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer hospital and a second referral receiving instruction confirmed by a supervisor according to a 3D virtual vehicle model rendered on the 3D electronic map is received;

and the sending module is used for generating a referral list according to the disease grade and sending the referral list to the terminal transferred to the hospital.

In order to achieve the above object, the present invention also provides an electronic device, including:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a program executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based referral supervision method.

In order to achieve the above object, the present invention further provides a computer readable storage medium, wherein the computer readable storage medium stores an artificial intelligence based referral monitoring program, and when the artificial intelligence based referral monitoring program is executed by a processor, the steps of the artificial intelligence based referral monitoring method are implemented.

The method comprises the steps of obtaining a GPS coordinate of a referral vehicle through pictures of a referral vehicle, a transfer-in hospital and a referral vehicle obtained through analysis, determining whether a first referral receiving instruction is generated or not according to the GPS coordinate of the referral vehicle and the GPS coordinate of the transfer-in hospital, determining a supervision area according to the GPS coordinate of the referral vehicle and the GPS coordinate of the transfer-in hospital when the first referral receiving instruction is determined to be generated according to the GPS coordinate of the referral vehicle and the GPS coordinate of the transfer-in hospital, and loading and displaying a 3D electronic map corresponding to the supervision area on a display screen; then, vehicle information of the referral vehicle picture is identified, a 3D virtual vehicle model is rendered on the 3D electronic map according to the vehicle information, the referral process of the referral vehicle can be intuitively and vividly simulated, and a supervisor can intuitively determine whether a second referral receiving instruction is generated or not; the system can dynamically track the referral process in real time, avoids the situations of misrepresentation and missed presentation, can generate the referral list in time when the referral vehicle reaches the destination, has high supervision efficiency, can generate the referral list only under the conditions of determining to generate a first referral receiving instruction and receiving a second referral receiving instruction, can effectively ensure the supervision result, and has high supervision result accuracy. And finally, calling a disease grade identification model to identify the disease grade of the electronic patient case of the patient, generating a referral list according to the disease grade and sending the referral list to the terminal of the hospital to enable medical workers of the hospital to schedule and optimize medical resources of the hospital in advance, rapidly providing a treatment service for the patient with serious illness and improving the treatment experience of the patient.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a diagram of an electronic device according to a preferred embodiment of the present invention;

FIG. 2 is a block diagram of a preferred embodiment of the artificial intelligence based referral monitoring apparatus of FIG. 1;

FIG. 3 is a flow chart of a referral supervision method based on artificial intelligence according to a preferred embodiment of the invention;

the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Fig. 1 is a schematic diagram of an electronic device 1 according to a preferred embodiment of the invention.

The electronic device 1 includes but is not limited to: memory 11, processor 12, display 13, and network interface 14. The electronic device 1 is connected to a network through a network interface 14 to obtain raw data. The network may be a wireless or wired network such as an Intranet (Internet), the Internet (Internet), a Global System for mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), or a Wi-Fi communication network.

The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 11 may be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like equipped with the electronic device 1. Of course, the memory 11 may also comprise both an internal memory unit and an external memory device of the electronic device 1. In this embodiment, the memory 11 is generally used for storing an operating system and various application software installed in the electronic device 1, such as program codes of the referral supervisor 10 based on artificial intelligence. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.

Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is typically used for controlling the overall operation of the electronic device 1, such as performing data interaction or communication related control and processing. In this embodiment, the processor 12 is configured to run the program code stored in the memory 11 or process data, for example, run the program code of the artificial intelligence based referral supervisor 10.

The display 13 may be referred to as a display screen or display unit. In some embodiments, the display 13 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-emitting diode (OLED) touch screen, or the like. The display 13 is used for displaying information processed in the electronic device 1 and for displaying a visual work interface, e.g. displaying the results of data statistics.

The network interface 14 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), the network interface 14 typically being used for establishing a communication connection between the electronic device 1 and other electronic devices.

Fig. 1 shows only an electronic device 1 with components 11-14 and an artificial intelligence based referral supervisor 10, but it will be understood that not all of the shown components are required and that more or fewer components may alternatively be implemented.

Optionally, the electronic device 1 may further comprise a target user interface, the target user interface may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional target user interface may further comprise a standard wired interface and a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch screen, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized target user interface, among other things.

The electronic device 1 may further include a Radio Frequency (RF) circuit, a sensor, an audio circuit, and the like, which are not described in detail herein.

In the above embodiment, the processor 12, when executing the artificial intelligence based referral supervisor 10 stored in the memory 11, may implement the following steps:

For the detailed description of the above steps, please refer to the following description of fig. 2 regarding a functional block diagram of an embodiment of the artificial intelligence based referral supervision apparatus 100 and fig. 3 regarding a flowchart of an embodiment of an artificial intelligence based referral supervision method.

Referring to fig. 2, a functional block diagram of the referral supervision device 100 based on artificial intelligence is shown.

The referral supervision device 100 based on artificial intelligence can be installed in electronic equipment. According to the implemented functions, the artificial intelligence based referral supervision apparatus 100 may comprise a receiving module 110, a determining module 120, a rendering module 130, a generating module 140, a calling module 150 and a sending module 160. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.

In the present embodiment, the functions regarding the respective modules/units are as follows:

and the receiving module 110 is used for analyzing the received referral application to obtain a vehicle picture and a patient electronic case for transfer out of the hospital, transfer into the hospital and referral.

In this embodiment, a medical co-body platform is installed in the computer device, and the medical co-body platform is used for monitoring referral information of each medical institution. The medical co-body platform can provide an intelligent large screen to visually display statistical data. Hospital Information Systems (HIS) are installed in terminals corresponding to each medical institution. Doctors in each medical institution can issue referral application forms through the HIS system, and the HIS system sends referral applications to the medical co-body platform through the HTTP-SERVER.

And after the medical co-body platform receives the referral application, analyzing the referral application to obtain an analysis result. The parsing result may include, but is not limited to: transfer out of hospital, transfer into hospital, transfer vehicle pictures and patient electronic cases. The referral vehicle picture comprises a referral vehicle picture, and the patient electronic case is that the patient is transferred to a hospital and original information of the patient in the treatment process is recorded in an electronic mode.

In one embodiment, before said parsing the received referral application, further comprises:

receiving a referral application;

saving the referral application to a WebSocket message queue;

In this embodiment, after receiving a referral application, the hospital platform first stores the referral application in a WebSocket message queue, where WebSocket is a push service based on long connection, and is used to actively push a message to a terminal user without initiating an interface request by the terminal user.

In the optional embodiment, the referral application can be quickly acquired and analyzed in real time through a message pushing mechanism of the WebSocket message queue.

And the determining module 120 is configured to determine a monitoring area according to the GPS coordinate of the transfer-out hospital and the GPS coordinate of the transfer-in hospital, and load and display a 3D electronic map corresponding to the monitoring area on a display screen.

In this embodiment, the computer device may determine a rectangular area by using the GPS coordinate of the transfer-out hospital and the GPS coordinate of the transfer-in hospital as two symmetric points of the rectangle, and determine the rectangular area as the supervision area. The computer device only needs to load the 3D electronic map corresponding to the supervised area.

In the embodiment, the 3D electronic map can be directionally and quickly loaded by determining the supervision area and loading the 3D electronic map of the supervision area instead of loading all the 3D electronic maps, so that the display effect of the 3D electronic map is quickly realized.

And the rendering module 130 is configured to identify vehicle information of the referral vehicle picture, and render a 3D virtual vehicle model on the 3D electronic map according to the vehicle information.

In this embodiment, a three-dimensional engine ceium may be used to render a 3D model on a display interface of a medical co-body platform, the bottom layer of the ceium is rendered using a Web graphics library, the Web graphics library implements creation of a Web interactive three-dimensional animation through an HTML script itself, and the rendering is implemented using a graphics hardware acceleration function of the bottom layer. The Cesium is an open source framework based on JavaScript, can be used for drawing a 3D earth in a browser and drawing a map on the earth (supporting tile services of multiple formats), does not need any plug-in support, but the browser must support WebGL, supports multiple data visualization modes, and can draw various geometric figures, imported pictures and even 3D models.

The computer equipment stores the corresponding relation between the GPS range and the 3D virtual hospital model in advance, the corresponding first 3D virtual hospital model can be determined by matching the GPS range where the GPS coordinate of the transferred-out hospital is located, and the corresponding second 3D virtual hospital model can be determined by matching the GPS range where the GPS coordinate of the transferred-in hospital is located. The computer equipment determines a first positioning point on the 3D electronic map according to the GPS coordinate of the transfer-out hospital, and renders a first 3D virtual hospital model corresponding to the GPS coordinate of the transfer-out hospital by taking the first positioning point as a center; and determining a second positioning point on the 3D electronic map according to the GPS coordinate of the transferred-to hospital, and rendering a second 3D virtual hospital model corresponding to the GPS coordinate of the transferred-to hospital by taking the second positioning point as a center.

The hospital-integrated platform renders a first 3D virtual hospital model and a second 3D virtual hospital model on the 3D electronic map according to GPS coordinates of a hospital so as to simulate a referral scene more truly, 3D display is more hierarchical, and a supervisor can conveniently and visually know the whole referral process.

In one embodiment, the identifying the vehicle information of the referral vehicle picture and rendering a 3D virtual vehicle model on the 3D electronic map according to the vehicle information comprises:

In this embodiment, in this optional embodiment, the vehicle type of the referral vehicle may be obtained by inputting the referral vehicle picture into a pre-trained vehicle type recognition model for recognition; inputting the referral vehicle picture into a pre-trained color recognition model for recognition to obtain the color of the referral vehicle; and recognizing the referral vehicle picture by adopting a license plate number recognition algorithm to obtain the referral vehicle license plate number.

The referral information may include, but is not limited to, basic information of the patient (e.g., name, age, sex, etc.) and medical information (e.g., doctor visit, department visit, diagnosis results).

And typing referral information into the prompt box and synchronously moving on the 3D electronic map along with the 3D virtual vehicle model.

The generating module 140 is configured to acquire the GPS coordinate of the referral vehicle, and determine whether to generate a first referral receiving instruction according to the GPS coordinate of the referral vehicle and the GPS coordinate of the transfer hospital.

In this embodiment, the computer device may send a GPS coordinate acquisition request to the referral vehicle in real time or periodically, determine the GPS coordinate of the referral vehicle according to the GPS coordinate fed back by the referral vehicle, or actively report the GPS coordinate to the computer device in real time or periodically, so that the computer device receives the GPS coordinate of the referral vehicle in real time or periodically.

In one embodiment, the determining whether to generate a first referral receiving instruction according to the GPS coordinates of the referral vehicle and the GPS coordinates of the referral hospital comprises:

In this optional embodiment, if the ratio of the first distance to the second distance is greater than the preset ratio threshold, it indicates that the referral vehicle is closer to the transfer hospital, and it is determined that a first referral instruction is generated. And if the ratio of the first distance to the second distance is not greater than the preset ratio threshold, the referral vehicle is far away from the transfer hospital, and a first referral instruction is determined not to be generated.

The calling module 150 is configured to call a disease level identification model to identify a disease level of the patient electronic case when a first referral receiving instruction is determined to be generated according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer hospital and a second referral receiving instruction confirmed by a supervisor according to a 3D virtual vehicle model rendered on the 3D electronic map is received.

In this embodiment, the computer device calls the disease level identification model to identify the disease level of the electronic patient case of the patient only when determining that the first referral receiving instruction is generated and when receiving the second referral receiving instruction confirmed by the supervisor, so as to facilitate the subsequent generation of referral lists of different levels according to the disease level. The computer device does not invoke the disease level identification model to identify the disease level of the patient electronic case upon determining that the first referral receipt instruction is not generated and/or upon not receiving a second referral receipt instruction confirmed by a supervisor.

When the computer equipment determines to generate a first referral receiving instruction and receives a second referral receiving instruction confirmed by a supervisor, the referral vehicle in a real referral scene is indicated to be fast driven to a transfer hospital, a target referral receiving instruction is generated to generate a referral supervision event and store the referral supervision event, and subsequent tracking, tracing and statistical analysis are facilitated.

In one embodiment, the invoking the disease level identification model identifies a disease level of the electronic case of the patient, comprising:

In this embodiment, the entity types refer to medical terms such as diseases, symptoms, diagnosis classifications, treatments, examination tests, human tissues, examination items, the entity names refer to entity items included in each entity type, and the entity attributes refer to degrees of the entity items. For example, the entity type is disease, the entity item is cancer, and the entity attribute is stage 3.

The computer equipment is pre-stored with a medical knowledge map constructed by a professional medical entity marker, identifies a plurality of entity types in the patient electronic case through the medical knowledge map, and analyzes entity attributes of each entity item in the patient electronic case according to a context semantic analysis algorithm.

The computer equipment can divide the table according to the disease degrees of the world health organization, takes 4 disease degrees which respectively correspond to mild, moderate, severe and high-risk, and establishes an electronic case set according to the disease degrees. According to the following steps of 6: 2: 2, dividing a training set, a testing set and a verifying set in sequence according to the proportion, and identifying a plurality of entity types of electronic cases in the training set, the testing set and the verifying set and entity names and entity attributes corresponding to each entity type through medical knowledge maps, thereby respectively constructing a training entity attribute vector set, a testing entity attribute vector set and a verifying entity attribute vector set. And training a neural network model for analyzing the disease grade based on the training entity attribute vector set, the testing entity attribute vector set and the verifying entity attribute vector set to obtain a disease grade identification model.

In one embodiment, before the receiving a second referral receiving instruction confirmed by a supervisor according to a 3D virtual vehicle model rendered on the 3D electronic map, the method further comprises:

In this embodiment, the icon style may be bmap. symbol.

And the computer equipment can call a road book function to control the 3D virtual vehicle model to move on the electronic map according to the GPS coordinates of the referral vehicle.

The computer device may determine that the directional movement path is a path between the first 3D virtual hospital model and the second 3D virtual hospital model when the directional movement path is within the prison area by identifying whether the directional movement path is within the prison area; when the directional movement path is not in the supervision area, determining that the directional movement path is not a path between the first 3D virtual hospital model and the second 3D virtual hospital model.

In an alternative embodiment, the computer device may also display a display color associated with the distance on the directional movement path. Different gradient color values can be displayed according to users or actual requirements, and due to gradient, the display color can be set to start displaying and finished displaying, and a plurality of display colors can be added in the middle. For example, when a gradation color value transitioning from red to blue is to be displayed, the shift amount of the gradation start point may be set to 0, the corresponding color is red, the shift amount of the gradation end point is set to 1, and the corresponding color is blue.

And the sending module 160 is configured to generate a referral list according to the disease level and send the referral list to the terminal of the transfer-in hospital.

In this embodiment, when the identified disease grade is higher, it indicates that the patient has a serious disease condition, and needs to make an immediate visit, a first-level referral sheet is generated for the patient; when the identified disease level is lower, indicating that the patient is milder and does not require an immediate visit, a second level of referral is generated for the patient.

Different levels of referral lists are generated by identifying disease grades and are sent to a terminal transferred to a hospital, so that medical workers transferred to the hospital can schedule medical resources of the hospital in advance, such as various medical devices, the manpower of doctors and the like, the medical resources can be optimized, the treatment service can be provided for patients with serious illness rapidly, and the treatment experience of the patients is improved.

In an optional embodiment, after the generating and sending the referral list according to the disease level to the terminal transferred to the hospital, the method further comprises:

In this embodiment, the supervision report can be displayed on the smart large screen of the medical co-physical platform in real time.

In the embodiment, the GPS coordinates of the referral vehicle are obtained through analysis of the pictures of the referral vehicle, the transfer-in hospital and the referral vehicle, whether a first referral receiving instruction is generated or not is determined according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer-in hospital, when the first referral receiving instruction is determined to be generated according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer-in hospital, a supervision area is determined according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer-in hospital, and a 3D electronic map corresponding to the supervision area is loaded and displayed on a display screen; then, vehicle information of the referral vehicle picture is identified, a 3D virtual vehicle model is rendered on the 3D electronic map according to the vehicle information, the referral process of the referral vehicle can be intuitively and vividly simulated, and a supervisor can intuitively determine whether a second referral receiving instruction is generated or not; the system can dynamically track the referral process in real time, avoids the situations of misrepresentation and missed presentation, can generate the referral list in time when the referral vehicle reaches the destination, has high supervision efficiency, can generate the referral list only under the conditions of determining to generate a first referral receiving instruction and receiving a second referral receiving instruction, can effectively ensure the supervision result, and has high supervision result accuracy. And finally, calling a disease grade identification model to identify the disease grade of the electronic patient case of the patient, generating a referral list according to the disease grade and sending the referral list to the terminal of the hospital to enable medical workers of the hospital to schedule and optimize medical resources of the hospital in advance, rapidly providing a treatment service for the patient with serious illness and improving the treatment experience of the patient.

In addition, the invention also provides a referral supervision method based on artificial intelligence. Fig. 3 is a schematic method flow diagram of an embodiment of the referral supervision method based on artificial intelligence according to the invention. When the processor 12 of the electronic device 1 executes the artificial intelligence based referral supervision program 10 stored in the memory 11, the artificial intelligence based referral supervision method is realized, which comprises steps S101-S106. The respective steps will be specifically described below.

S101: and analyzing the received referral application to obtain the images of the vehicle and the patient electronic case transferred out of the hospital, transferred into the hospital and referred to.

receiving a referral application;

saving the referral application to a WebSocket message queue;

S102: and determining a supervision area according to the GPS coordinates of the transferred-out hospital and the GPS coordinates of the transferred-in hospital, and loading and displaying a 3D electronic map corresponding to the supervision area on a display screen.

S103: and identifying vehicle information of the referral vehicle picture, and rendering a 3D virtual vehicle model on the 3D electronic map according to the vehicle information.

S104: and acquiring the GPS coordinate of the referral vehicle, and determining whether a first referral receiving instruction is generated or not according to the GPS coordinate of the referral vehicle and the GPS coordinate of the transfer hospital.

S105: and when a first referral receiving instruction is determined and generated according to the GPS coordinates of the referral vehicle and the GPS coordinates of the transfer hospital and a second referral receiving instruction confirmed by a supervisor according to a 3D virtual vehicle model rendered on the 3D electronic map is received, calling a disease grade identification model to identify the disease grade of the patient electronic case.

In this embodiment, the icon style may be bmap. symbol.

S106: and generating a referral list according to the disease grade and sending the referral list to the terminal transferred to the hospital.

Furthermore, the embodiment of the present invention also provides a computer-readable storage medium, which may be any one or any combination of a hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, and the like. The computer readable storage medium comprises a storage data area and a storage program area, the storage data area stores data created according to the use of the blockchain node, the storage program area stores the artificial intelligence based referral supervisor 10, and when the artificial intelligence based referral supervisor 10 is executed by a processor, the following operations are realized:

In another embodiment, in order to further ensure the privacy and security of all the data, all the data may be stored in a node of a block chain. Such as a two-dimensional code, an identification code, etc., which may be stored in the block link points.

It should be noted that the blockchain in the present invention is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The specific implementation of the computer-readable storage medium of the present invention is substantially the same as the specific implementation of the referral supervision method based on artificial intelligence, and will not be described herein again.

It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.

It should be noted that, the above embodiments of the present invention may acquire and process related data based on an artificial intelligence technique. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above, and includes instructions for enabling an electronic device (such as a mobile phone, a computer, an electronic apparatus, or a network device) to execute the method according to the embodiments of the present invention.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims

1. A referral supervision method based on artificial intelligence is characterized by comprising the following steps:

2. The artificial intelligence based referral supervision method of claim 1, wherein prior to said parsing of the received referral application, the method further comprises:

receiving a referral application;

saving the referral application to a WebSocket message queue;

3. The artificial intelligence based referral supervision method according to claim 1, wherein the identifying vehicle information of the referral vehicle picture and rendering a 3D virtual vehicle model on the 3D electronic map according to the vehicle information comprises:

4. The artificial intelligence based referral supervision method according to claim 1 wherein said determining whether to generate a first referral receiving instruction according to the GPS coordinates of the referral vehicle and the GPS coordinates of the referral hospital comprises:

5. The artificial intelligence based referral supervision method of claim 1 wherein said invoking a disease level identification model identifies a disease level of the patient electronic case comprises:

6. The artificial intelligence based referral supervision method according to claim 5, further comprising, before the receiving a second referral receiving instruction confirmed by a supervisor according to a 3D virtual vehicle model rendered on the 3D electronic map:

7. The artificial intelligence based referral supervision method according to claim 1, further comprising, after the generating of the referral sheet according to the disease level and sending to the terminal of the referral to the hospital:

8. An artificial intelligence based referral supervision apparatus, comprising:

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and the number of the first and second groups,

the memory stores a program executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based referral supervision method of any one of claims 1-7.

10. A computer readable storage medium, characterized in that the computer readable storage medium stores an artificial intelligence based referral supervisor which, when executed by a processor, implements the steps of the artificial intelligence based referral supervision method according to any of claims 1-7.