CN113628735A - Online appointment registration method and device based on neural network - Google Patents
Online appointment registration method and device based on neural network Download PDFInfo
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Abstract
The application relates to the field of artificial intelligence, and discloses an online appointment registration method based on a neural network, which comprises the following steps: receiving an online registration request input by a user, and acquiring registration information of the online registration request, wherein the registration information comprises user information, illness state information and registration time; performing information authentication on the user information; when the information authentication is successful, acquiring a face image of a user, and performing face authentication on the face image; when the face authentication is successful, carrying out feature extraction on the disease condition information to obtain feature disease condition information; and matching the association degree of the characteristic illness state information and the medical staff in the medical library according to the registration time, and selecting the medical staff with the association degree larger than a preset threshold value as a target registration object of the online registration request. In addition, the application also provides an online appointment register device, electronic equipment and a storage medium based on the neural network. The method and the device can ensure the consistency of the information of the on-line appointment register, improve the reliability of the on-line appointment register and avoid the phenomenon of medical resource waste.
Description
Technical Field
The present application relates to the field of artificial intelligence, and in particular, to an online appointment registration method and apparatus based on a neural network, an electronic device, and a computer-readable storage medium.
Background
With the continuous development and perfection of artificial intelligence technology, people's daily life is greatly enriched and facilitated, in the medical field, many hospitals are provided with platforms for on-line appointment registration, the subjective motility of the inquiry personnel can be brought into full play based on the on-line appointment registration, the optimal matching of the inquiry personnel and the inquiry time is realized, and the inquiry service of the inquiry personnel is greatly improved.
At present, on-line appointment registration is usually based on registration information filled by a user to match corresponding registered doctors, false information and imposition information exist in the registration information filled by the user, and thus the situation of inconsistent information during on-line appointment registration is easily caused, and the phenomenon of medical resource waste is caused.
Disclosure of Invention
In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides an online appointment registration method, apparatus, electronic device and computer readable storage medium based on a neural network, which can ensure consistency of information of online appointment registration, improve reliability of online appointment registration and avoid waste of medical resources.
In a first aspect, the present application provides an online appointment registration method based on a neural network, including:
receiving an online registration request input by a user, and collecting registration information of the online registration request, wherein the registration information comprises user information, illness state information and registration time;
performing information authentication on the user information;
when the information authentication is successful, acquiring a face image of the user, and performing face authentication on the face image;
when the face authentication is successful, carrying out feature extraction on the disease condition information to obtain feature disease condition information;
and matching the association degree of the characteristic illness state information and medical staff in a medical library according to the registration time, and selecting the medical staff with the association degree larger than a preset threshold value as a target registration object of the online registration request.
It can be seen that, in the embodiment of the application, registration information of an online registration request input by a user is collected, wherein the registration information comprises user information, illness state information and registration time, so that the premise of online appointment registration can be guaranteed, the user information is authenticated, the accuracy of the user information in the online registration request can be guaranteed, and the reliability of online registration is guaranteed; secondly, when the information authentication is successful, the face image of the user is collected, and the face image is subjected to face authentication, so that the consistency of the information of the user who makes an on-line appointment for registration can be guaranteed, the reliability of the on-line appointment for registration is further improved, and the waste phenomenon of medical resources is avoided; furthermore, when the face authentication is successful, the embodiment of the application extracts the characteristics of the illness state information to obtain the characteristic illness state information, can reduce the subsequent online registration information matching calculation amount, improves the online appointment registration speed, and matches the association degree of the characteristic illness state information and the medical staff in the medical library according to the registration time so as to realize the online appointment registration. Therefore, the online appointment registration method provided by the embodiment of the application can ensure the consistency of the online appointment registration information, improve the reliability of online appointment registration and avoid the phenomenon of medical resource waste.
In a possible implementation manner of the first aspect, the collecting registration information of the online registration request includes:
acquiring a request field of the online registration request, and inquiring a data table of the request field from a background database;
and inquiring registration information of the online registration request according to the data table.
In one possible implementation manner of the first aspect, the performing information authentication on the user information includes:
acquiring an information identifier of the user information, and inquiring whether a server side information identifier corresponding to the information identifier exists in a server corresponding to the information identifier;
if the server-side information identifier of the information identifier does not exist, the information authentication fails;
and if the server-side information identifier of the information identifier exists, the information authentication is successful.
In a possible implementation manner of the first aspect, the performing face authentication on the face image includes:
carrying out face frequency domain conversion on the face image to obtain a target face image;
recognizing the living body probability of the target face image by using a face living body recognition model;
identifying whether the target face image is a living body image or not according to the living body probability;
if the target face image is a living body image, judging that the face authentication is successful;
and if the target face image is not the living body image, judging that the face authentication fails.
In a possible implementation manner of the first aspect, the identifying the living body probability of the target face image by using the living body face recognition model includes:
performing feature extraction on the target face image by using a convolution module in the face living body recognition model to obtain a feature face image;
and calculating the living body probability of the characteristic face image by using an attention module in the face living body recognition model.
In a possible implementation manner of the first aspect, the performing feature extraction on the condition information to obtain feature condition information includes:
deleting stop words in the illness state information, and segmenting the deleted illness state information to obtain illness state words;
converting the illness state words into illness state word vectors, and calculating the weight of the illness state word vectors;
and selecting the disease condition word vector with the weight larger than the preset weight, and generating characteristic disease condition information according to the selected disease condition word vector.
In one possible implementation manner of the first aspect, the matching, according to the registration time, the association degree of the characteristic illness state information with the medical staff in the medical library includes:
inquiring the medical field of medical personnel in the medical library, and calculating the matching degree of the characteristic illness state information and the medical field;
selecting the medical personnel with the matching degree larger than the preset matching degree as an initial registration object, and inquiring the medical time of the initial registration object;
and calculating the association degree of the registration time and the medical time to obtain the association degree of the characteristic disease information and the medical staff in the medical library.
In a second aspect, the present application provides an online appointment registration apparatus based on a neural network, the apparatus comprising:
the system comprises an information acquisition module, a registration module and a registration module, wherein the information acquisition module is used for receiving an online registration request input by a user and acquiring registration information of the online registration request, and the registration information comprises user information, illness state information and registration time;
the information authentication module is used for carrying out information authentication on the user information;
the face authentication module is used for extracting the features of the illness state information to obtain feature illness state information when the face authentication is successful;
the information extraction module is used for extracting the characteristics of the illness state information to obtain characteristic illness state information when the face authentication is successful;
and the appointment registration module is used for matching the association degree of the characteristic illness state information and medical staff in a medical library according to the registration time, and selecting the medical staff with the association degree larger than a preset threshold value as a target registration object of the online registration request.
In a third aspect, the present application provides an electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the neural network-based on-line reservation registration method as defined in any one of the first aspects above.
In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the neural network-based online booking registration method according to any one of the first aspects.
It is understood that the beneficial effects of the second to fourth aspects can be seen from the description of the first aspect, and are not described herein again.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a detailed flowchart of an online appointment registration method based on a neural network according to an embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating a step of the neural network-based online appointment registration method shown in fig. 1 according to an embodiment of the present invention.
Fig. 3 is a flowchart illustrating another step of the neural network-based online appointment registration method shown in fig. 1 according to an embodiment of the present invention.
Fig. 4 is a flowchart illustrating another step of the neural network-based online appointment registration method shown in fig. 1 according to an embodiment of the present invention.
Fig. 5 is a block diagram of an online appointment registration apparatus based on a neural network according to an embodiment of the present disclosure.
Fig. 6 is a schematic internal structural diagram of an electronic device implementing an online subscription registration method based on a neural network according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. 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 application.
An online subscription registration method based on a neural network provided by an embodiment of the present application is described with reference to a flowchart shown in fig. 1. The neural network-based online booking registration method described in fig. 1 includes:
s1, receiving an online registration request input by a user, and collecting registration information of the online registration request, wherein the registration information comprises user information, illness state information and registration time.
In the embodiment of the present application, the online registration request may be understood as an appointment inquiry request input by a user in an intelligent registration platform, and the intelligent registration platform may be an app, an applet, a public number, and the like. It should be appreciated that, after an online registration request is input by a user, the intelligent registration platform obtains registration information of the online registration request, wherein the registration information includes: the system comprises user information, illness state information and registration time, wherein the user information refers to personal basic data of a user, such as name, age, gender, contact way, address, identity card and the like, the illness state information refers to body state description data of the user, such as dizziness, heaviness in head and feet, cold, chest distress and the like, and the registration time refers to the time that the user expects to visit a doctor and is generated based on different user requirements.
As an embodiment of the present application, the collecting registration information of the online registration request includes: and acquiring a request field of the online registration request, inquiring a data table of the request field from a background database, and inquiring registration information of the online registration request according to the data table.
The request field may be understood as an identity of the online registration request, and is used to represent the registration identity of the online registration request, and the background database is used to store page data generated by the intelligent registration platform, and may be a relational database, such as a MySQL database.
Further, in an optional embodiment of the present application, the query of the registration information may be performed through an upper SQL statement, such as a select () query statement.
And S2, performing information authentication on the user information.
It should be understood that, in an actual registration scene, a phenomenon of false information filling occurs to increase a success rate of reservation registration, so that, in the embodiment of the present application, the accuracy of the user information in the online registration request is ensured by performing information authentication on the user information, thereby ensuring the reliability of subsequent online registration.
As an embodiment of the present application, the performing information authentication on the user information includes: acquiring an information identifier of the user information; inquiring whether a server side information identifier corresponding to the information identifier exists in the server corresponding to the information identifier; if the server-side information identifier of the information identifier does not exist, the information authentication fails; and if the server-side information identifier of the information identifier exists, the information authentication is successful.
Wherein the information identifier is used for characterizing the uniqueness of the user information, such as: user gestures, user passwords, user fingerprints and the like, wherein the server-side information identification refers to an identity identification which is registered in the server in advance by the user.
Further, in another embodiment of the present application, when the information authentication fails, the step of performing S1 is returned again to implement the re-acquisition of the user information.
And S3, when the information authentication is successful, acquiring the face image of the user, and performing face authentication on the face image.
It should be understood that when the information authentication is successful, the authenticity of the user of the online registration request can be represented, but in an actual medical registration scene, registration information falsely replacing information of other people exists, for example, the user a takes the personal information of the user B, so that the user a takes the information of the user B to perform online reservation registration, therefore, the online reservation registration reliability is further improved and the waste of medical resources is avoided by acquiring the face image of the user and authenticating to ensure the consistency of the user information of the online reservation registration. Further, in an optional embodiment of the present application, the collecting of the facial image of the user may be implemented by an image collecting device, such as a camera.
As an embodiment of the present application, referring to fig. 2, the performing face authentication on the face image includes:
s201, performing face frequency domain conversion on the face image to obtain a target face image;
s202, recognizing the living body probability of the target face image by using a face living body recognition model;
s203, identifying whether the target face image is a living body image or not according to the living body probability;
s204, if the target face image is a living body image, judging that the face authentication is successful;
s205, if the target face image is not a living body image, judging that the face authentication fails.
The face frequency domain conversion is used for separating ambient light of a face image, improving anti-interference performance of the face image and greatly improving accuracy of face image living body identification, and the face living body model is used for detecting whether the face image is a forged face image (such as an image shot in advance) or not so as to guarantee accuracy of face authentication and can be constructed through a Densenet201 network.
In an optional embodiment, the S201 includes: carrying out Fourier transform on the face image to obtain a frequency domain face image; filtering ambient light in the frequency domain face image by using a low-pass filter to obtain a standard frequency domain face image; carrying out inverse Fourier transform on the standard frequency domain face image to obtain a space frequency domain face image; and performing channel superposition on the standard frequency domain face image and the spatial frequency domain face image to obtain a target face image. The Fourier transform is used for converting the face image from a spatial domain into a frequency domain so as to analyze an ambient light frequency band of the face image, the inverse Fourier transform is used for converting the face image from the frequency domain into the spatial domain, and the channel superposition is used for extracting the frequency domain characteristic of the face image, improving the ambient light interference resistance of the face image and ensuring the identification accuracy of the face living body.
In an alternative embodiment, the S202 includes: performing feature extraction on the target face image by using a convolution module in the face living body recognition model to obtain a feature face image; and calculating the living body probability of the characteristic face image by using an attention module in the face living body recognition model. The Convolution module is a Convolution module consisting of a channel-by-channel Convolution (Depthwise Convolution) and a point-by-point Convolution (Pointwise Convolution), and is used for reducing the parameter quantity and the operation quantity of the model while keeping the accuracy of the model and realizing the feature point extraction of the face image; the attention module is used for setting different weight parameters for each channel c (channel) in the feature graph (h x w c) obtained by convolution of the convolution module, and living body probability calculation of the feature face image is achieved.
In an alternative embodiment, the S203 includes: and if the living body probability is greater than the preset probability, the target face image is a living body image, and if the living body probability is not greater than the preset probability, the target face image is a non-living body image. The preset probability may be set to 0.65, or may be set according to an actual service scenario.
Further, in another embodiment of the present application, when the information authentication fails, the step of S2 is executed again to realize the re-acquisition of the face image.
And S4, when the face authentication is successful, performing feature extraction on the disease condition information to obtain feature disease condition information.
It should be understood that, when the face authentication is successful, the user information and identity representing the online registration request are both verified, and the user is a user who really needs to register, so that the method and the device reduce the subsequent online registration information matching calculation amount and improve the online appointment registration speed by performing feature extraction on the illness state information in the online registration request.
As an embodiment of the present application, referring to fig. 3, the performing feature extraction on the disease condition information to obtain feature disease condition information includes:
s301, deleting stop words in the disease condition information, and segmenting the deleted disease condition information to obtain disease condition words;
s302, converting the illness state words into illness state word vectors, and calculating the weight of the illness state word vectors;
s303, selecting the disease condition word vector with the weight larger than the preset weight, and generating characteristic disease condition information according to the selected disease condition word vector.
The stop word refers to a word that has no meaning in the condition information, such as a mood assist word, an adverb, a preposition, a conjunction, and the like, and the weight can be understood as the importance of the condition word vector in the condition information.
In an optional embodiment, the deletion of the stop word can be realized by matching with the stop word in the stop word list; the word segmentation can be realized by a word segmentation algorithm, such as a Chinese character segmentation algorithm, a dictionary word segmentation algorithm, a Markov word segmentation algorithm and the like; the conversion of the disease condition Word vector can be realized by a Word vector conversion algorithm, such as a Word2vec algorithm; the weight of the disease word vector can be realized by an information concentration algorithm, such as a factor analysis algorithm, a principal component analysis algorithm and the like.
In an optional embodiment, the preset weight may be set to 0.6, or may be set according to an actual service scenario.
And S5, matching the association degree of the characteristic illness state information and medical staff in a medical library according to the registration time, and selecting the medical staff with the association degree larger than a preset threshold value as a registration object of the online registration request.
In the embodiment of the application, the medical library is constructed based on medical staff information and medical department information of different hospitals.
As an embodiment of the present application, referring to fig. 4, matching the correlation between the characteristic disease information and the medical staff in the medical library according to the registration time includes:
s401, inquiring the medical field of medical staff in the medical library, and calculating the matching degree of the characteristic illness state information and the medical field;
s402, selecting the medical personnel with the matching degree larger than the preset matching degree as an initial registration object, and inquiring the medical time of the initial registration object;
and S403, calculating the association degree of the registration time and the medical time to obtain the association degree of the characteristic disease information and medical staff in the medical library.
The medical field refers to the medical excellence range of the medical staff, namely the types of the medical staff treating diseases, such as neurology medical staff, brain medical staff and medical staff. The medical treatment time refers to the clinic visit time when the medical staff is not scheduled.
In an alternative embodiment, the matching degree of the characteristic illness state information and the medical field is calculated by the following formula:
wherein R represents a degree of matching, AiIn the information of representing characteristic disease conditionThe ith disease field, BiThe ith medical field representing the medical domain. Further, the preset matching degree is set to 0.88.
In an optional embodiment, a calculation method of the association degree between the registration time and the medical time is the same as the calculation method of the matching degree, and further description is omitted here.
Further, in the embodiment of the present application, the medical staff with the association degree greater than the preset threshold is selected as the target registration object of the online registration request, and the target registration object is returned to the user, so as to realize the online registration request of the user. The preset threshold may be set to 0.9, or may be set according to an actual service scenario.
According to the embodiment of the application, registration information of an online registration request input by a user is collected, wherein the registration information comprises user information, illness state information and registration time, the premise of online appointment registration can be guaranteed, information authentication is carried out on the user information, the accuracy of the user information in the online registration request can be guaranteed, and therefore the reliability of online registration is guaranteed; secondly, when the information authentication is successful, the face image of the user is collected, and the face image is subjected to face authentication, so that the consistency of the information of the user who makes an on-line appointment for registration can be guaranteed, the reliability of the on-line appointment for registration is further improved, and the waste phenomenon of medical resources is avoided; furthermore, when the face authentication is successful, the embodiment of the application extracts the characteristics of the illness state information to obtain the characteristic illness state information, can reduce the subsequent online registration information matching calculation amount, improves the online appointment registration speed, and matches the association degree of the characteristic illness state information and the medical staff in the medical library according to the registration time so as to realize the online appointment registration. Therefore, the online appointment registration method based on the neural network can ensure the information consistency of online appointment registration, improve the reliability of online appointment registration and avoid the phenomenon of medical resource waste.
Fig. 5 is a functional block diagram of an online subscription registration apparatus based on a neural network according to the present invention.
The neural network-based online booking registration apparatus 500 may be installed in an electronic device. According to the realized function, the online appointment register device based on the neural network can comprise an information acquisition module 501, an information authentication module 502, a face authentication module 503, an information extraction module 504 and an appointment register 505. A module according to 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:
the information acquisition module 501 is configured to receive an online registration request input by a user, and acquire registration information of the online registration request, where the registration information includes user information, illness state information, and registration time;
the information authentication module 502 is configured to perform information authentication on the user information;
the face authentication module 503 is configured to perform feature extraction on the illness state information when the face authentication is successful, so as to obtain feature illness state information;
the information extraction module 504 is configured to perform feature extraction on the illness state information when the face authentication is successful, so as to obtain feature illness state information;
and the appointment registration module 505 is configured to match the association degree between the characteristic disease condition information and medical staff in a medical library according to the registration time, and select the medical staff with the association degree greater than a preset threshold value as a target registration object of the online registration request.
In detail, in the embodiment of the present application, when the modules in the neural network-based online appointment registration apparatus 500 are used, the same technical means as the neural network-based online appointment registration method described in fig. 1 and fig. 4 are adopted, and the same technical effects can be produced, and details are not repeated here.
Fig. 6 is a schematic structural diagram of an electronic device implementing the neural network-based online subscription registration method according to the present application.
The electronic device may include a processor 60, a memory 61, a communication bus 62, and a communication interface 63, and may further include a computer program, such as a neural network-based on-line reservation registration program, stored in the memory 61 and executable on the processor 60.
In some embodiments, the processor 60 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 60 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules stored in the memory 61 (for example, executing an on-line reservation registration program based on a neural network, etc.), and calling data stored in the memory 61.
The memory 61 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 61 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 61 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 61 may also include both an internal storage unit and an external storage device of the electronic device. The memory 61 may be used not only to store application software installed in the electronic device and various types of data, such as codes of an online reservation registration program based on a neural network, etc., but also to temporarily store data that has been output or will be output.
The communication bus 62 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 61 and at least one processor 60 or the like.
The communication interface 63 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, 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 OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 6 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 6 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 60 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The neural network based on-line subscription registration program stored in the memory 61 of the electronic device 6 is a combination of a plurality of computer programs, and when running in the processor 60, can realize:
receiving an online registration request input by a user, and collecting registration information of the online registration request, wherein the registration information comprises user information, illness state information and registration time;
performing information authentication on the user information;
when the information authentication is successful, acquiring a face image of the user, and performing face authentication on the face image;
when the face authentication is successful, carrying out feature extraction on the disease condition information to obtain feature disease condition information;
and matching the association degree of the characteristic illness state information and medical staff in a medical library according to the registration time, and selecting the medical staff with the association degree larger than a preset threshold value as a target registration object of the online registration request.
Specifically, the processor 60 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a non-volatile computer-readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present application also provides a computer-readable storage medium, storing a computer program that, when executed by a processor of an electronic device, may implement:
receiving an online registration request input by a user, and collecting registration information of the online registration request, wherein the registration information comprises user information, illness state information and registration time;
performing information authentication on the user information;
when the information authentication is successful, acquiring a face image of the user, and performing face authentication on the face image;
when the face authentication is successful, carrying out feature extraction on the disease condition information to obtain feature disease condition information;
and matching the association degree of the characteristic illness state information and medical staff in a medical library according to the registration time, and selecting the medical staff with the association degree larger than a preset threshold value as a target registration object of the online registration request.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An online appointment registration method based on a neural network, the method comprising:
receiving an online registration request input by a user, and collecting registration information of the online registration request, wherein the registration information comprises user information, illness state information and registration time;
performing information authentication on the user information;
when the information authentication is successful, acquiring a face image of the user, and performing face authentication on the face image;
when the face authentication is successful, carrying out feature extraction on the disease condition information to obtain feature disease condition information;
and matching the association degree of the characteristic illness state information and medical staff in a medical library according to the registration time, and selecting the medical staff with the association degree larger than a preset threshold value as a target registration object of the online registration request.
2. The neural network-based online appointment registration method of claim 1, wherein the collecting registration information of the online registration request comprises:
acquiring a request field of the online registration request, and inquiring a data table of the request field from a background database;
and inquiring registration information of the online registration request according to the data table.
3. The neural network-based online subscription registration method of claim 1, wherein the performing information authentication on the user information comprises:
acquiring an information identifier of the user information, and inquiring whether a server side information identifier corresponding to the information identifier exists in a server corresponding to the information identifier;
if the server-side information identifier of the information identifier does not exist, the information authentication fails;
and if the server-side information identifier of the information identifier exists, the information authentication is successful.
4. The neural network-based online booking registration method of claim 1, wherein the performing face authentication on the face image comprises:
carrying out face frequency domain conversion on the face image to obtain a target face image;
recognizing the living body probability of the target face image by using a face living body recognition model;
identifying whether the target face image is a living body image or not according to the living body probability;
if the target face image is a living body image, judging that the face authentication is successful;
and if the target face image is not the living body image, judging that the face authentication fails.
5. The neural network-based online booking registration method of claim 4, wherein the identifying the live body probability of the target face image by using the face live body recognition model comprises:
performing feature extraction on the target face image by using a convolution module in the face living body recognition model to obtain a feature face image;
and calculating the living body probability of the characteristic face image by using an attention module in the face living body recognition model.
6. The neural network-based online appointment registration method of claim 1, wherein the performing feature extraction on the disease condition information to obtain feature disease condition information comprises:
deleting stop words in the illness state information, and segmenting the deleted illness state information to obtain illness state words;
converting the illness state words into illness state word vectors, and calculating the weight of the illness state word vectors;
and selecting the disease condition word vector with the weight larger than the preset weight, and generating characteristic disease condition information according to the selected disease condition word vector.
7. The neural network-based on-line appointment registration method according to any one of claims 1 to 6, wherein the matching of the degree of association of the characteristic disease information with medical staff in a medical library according to the registration time comprises:
inquiring the medical field of medical personnel in the medical library, and calculating the matching degree of the characteristic illness state information and the medical field;
selecting the medical personnel with the matching degree larger than the preset matching degree as an initial registration object, and inquiring the medical time of the initial registration object;
and calculating the association degree of the registration time and the medical time to obtain the association degree of the characteristic disease information and the medical staff in the medical library.
8. An online appointment registration device based on a neural network, the device comprising:
the system comprises an information acquisition module, a registration module and a registration module, wherein the information acquisition module is used for receiving an online registration request input by a user and acquiring registration information of the online registration request, and the registration information comprises user information, illness state information and registration time;
the information authentication module is used for carrying out information authentication on the user information;
the face authentication module is used for extracting the features of the illness state information to obtain feature illness state information when the face authentication is successful;
the information extraction module is used for extracting the characteristics of the illness state information to obtain characteristic illness state information when the face authentication is successful;
and the appointment registration module is used for matching the association degree of the characteristic illness state information and medical staff in a medical library according to the registration time, and selecting the medical staff with the association degree larger than a preset threshold value as a target registration object of the online registration request.
9. An electronic device, characterized in that the electronic device comprises:
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 memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the neural network-based on-line reservation registration method of any one of claims 1 to 7.
10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the neural network-based on-line reservation registration method of any one of claims 1 to 7.
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