CN112486338A

CN112486338A - Medical information processing method and device and electronic equipment

Info

Publication number: CN112486338A
Application number: CN202011322534.8A
Authority: CN
Inventors: 卢启伟; 张淮清; 陈方圆
Original assignee: Shenzhen Eaglesoul Education Service Co Ltd
Current assignee: Shenzhen Eaglesoul Education Service Co Ltd
Priority date: 2020-11-23
Filing date: 2020-11-23
Publication date: 2021-03-12
Also published as: WO2022105003A1

Abstract

The embodiment of the disclosure provides a medical information processing method, a medical information processing device and electronic equipment, belonging to the technical field of data processing, wherein the method comprises the following steps: acquiring handwriting data formed by an intelligent pen in a preset writing area, wherein the handwriting data is used for describing pathological information; performing data processing on the handwriting received from the sensing device at a cloud service platform to form analysis content corresponding to the handwriting data; performing cluster analysis on the analyzed content to form a cluster analysis result; and determining a medical model corresponding to the pathological information and recommended information corresponding to the medical model based on the clustering analysis result. Through the processing scheme disclosed by the invention, the efficiency of medical information processing can be improved.

Description

Medical information processing method and device and electronic equipment

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a medical information processing method and apparatus, and an electronic device.

Background

Dot matrix digital intelligence pen is one kind through printing the invisible dot matrix pattern of one deck on ordinary paper, and the high-speed camera of digital pen front end catches the motion trail of nib at any time, and pressure sensor passes pressure data back data processor simultaneously, finally passes through the novel instrument of writing of the outside transmission of bluetooth or USB line with information.

Different from ordinary paper and pens, the information comprises information such as paper type, source, page number, position, handwriting coordinates, motion trail, pen point pressure, stroke sequence, pen moving time, pen moving speed and the like, and the handwriting recording process is synchronous with the writing process. When writing, the dot matrix digital pen stores the written characters or pictures on the paper in the computer in the form of bitmap to form a document, and if necessary, the characters or pictures can be synchronously displayed by projection.

How to effectively apply the handwriting content of the intelligent pen in intelligent medical treatment based on the cloud platform, the processing efficiency of medical information processing is improved, and the problem to be solved is solved.

Disclosure of Invention

In view of the above, embodiments of the present disclosure provide a medical information processing method, a medical information processing apparatus, and an electronic device, so as to at least partially solve the problems in the prior art.

In a first aspect, an embodiment of the present disclosure provides a medical information processing method, including:

acquiring handwriting data formed by an intelligent pen in a preset writing area, wherein the handwriting data is used for describing pathological information;

performing data processing on the handwriting received from the sensing device at a cloud service platform to form analysis content corresponding to the handwriting data;

performing cluster analysis on the analyzed content to form a cluster analysis result;

and determining a medical model corresponding to the pathological information and recommended information corresponding to the medical model based on the clustering analysis result.

According to a specific implementation manner of the embodiment of the present disclosure, the acquiring handwriting data formed by the smart pen in the preset writing area includes:

judging whether a pressure value generated by a pressure sensor of the intelligent pen is greater than a preset value or not;

if so, acquiring handwriting data generated by the intelligent pen in the writing area, wherein the handwriting data comprises a pressure value, a position coordinate, a time value and an acceleration value generated by the intelligent pen.

According to a specific implementation manner of the embodiment of the present disclosure, the performing, by the cloud service platform, data processing on the handwriting received from the sensing device includes:

restoring the track of the intelligent pen based on the pressure value, the position coordinate, the time value and the acceleration value contained in the handwriting data to form a graphical file;

performing character recognition on the graphical file to obtain a character set corresponding to the data;

and performing semantic analysis on the content in the character set to obtain the analysis content of the handwriting data.

According to a specific implementation manner of the embodiment of the present disclosure, performing cluster analysis on the analysis content to form a cluster analysis result includes:

inputting the analysis content into a pre-trained neural network model;

determining the cluster analysis result based on the calculation result of the neural network model.

According to a specific implementation manner of the embodiment of the present disclosure, the determining, based on the cluster analysis result, the medical model corresponding to the pathological information and the recommendation information corresponding to the medical model includes:

searching the pathological classification to which the clustering analysis result belongs;

and determining a medical model corresponding to the pathological information based on the pathological classification.

searching medicine information corresponding to the medical model;

and setting the one or more pieces of medicine information as recommendation information corresponding to the medical model.

searching selection information of the user for the one or more pieces of medicine information;

adding the selection information to the parsed content

In a second aspect, an embodiment of the present disclosure provides a medical information processing apparatus, including:

the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring handwriting data formed by the intelligent pen in a preset writing area, and the handwriting data is used for describing pathological information;

the processing module is used for carrying out data processing on the handwriting received from the sensing device on a cloud service platform to form analysis content corresponding to the handwriting data;

the clustering module is used for carrying out clustering analysis on the analyzed content to form a clustering analysis result;

and the determining module is used for determining a medical model corresponding to the pathological information and recommendation information corresponding to the medical model based on the clustering analysis result.

In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:

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 instructions executable by the at least one processor to enable the at least one processor to perform the method of processing medical information in any of the implementations of the first aspect or the first aspect.

In a fourth aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the medical information processing method in the first aspect or any implementation manner of the first aspect.

In a fifth aspect, the present disclosure also provides a computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the medical information processing method in the first aspect or any implementation manner of the first aspect.

The medical information processing scheme in the embodiment of the disclosure comprises the steps of obtaining handwriting data formed by an intelligent pen in a preset writing area, wherein the handwriting data is used for describing pathological information; performing data processing on the handwriting received from the sensing device at a cloud service platform to form analysis content corresponding to the handwriting data; performing cluster analysis on the analyzed content to form a cluster analysis result; and determining a medical model corresponding to the pathological information and recommended information corresponding to the medical model based on the clustering analysis result. Through the processing scheme disclosed by the invention, the efficiency of medical information processing is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of a medical information processing method according to an embodiment of the present disclosure;

fig. 2 is a flowchart of another medical information processing method provided by the embodiment of the present disclosure;

fig. 3 is a flowchart of another medical information processing method provided by the embodiment of the present disclosure;

fig. 4 is a flowchart of another medical information processing method provided by the embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of a medical information processing apparatus according to an embodiment of the present disclosure;

fig. 6 is a schematic diagram of an electronic device provided in an embodiment of the present disclosure.

Detailed Description

The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.

In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.

The embodiment of the disclosure provides a medical information processing method. The medical information processing method provided by the embodiment may be executed by a computing device, which may be implemented as software or as a combination of software and hardware, and may be integrally provided in a server, a client, or the like.

Referring to fig. 1, a medical information processing method in an embodiment of the present disclosure may include the following steps:

s101, acquiring handwriting data formed by the intelligent pen in a preset writing area, wherein the handwriting data is used for describing pathological information.

The intelligent pen writes in the writing area of the sensing device, handwriting data can be generated, and the content written in the writing area by the user can be described through the handwriting data.

In the process of obtaining the writing content of the user, whether a pressure sensor on the intelligent pen generates a pressure signal can be further monitored, and after the pressure value of the pressure signal is larger than a preset value, handwriting data formed by the intelligent pen on a writing area can be collected.

As an application scene, the handwriting data can be pathological information handwritten by a doctor, and the handwriting can be directly stored in an electronic mode.

And S102, performing data processing on the handwriting received from the sensing device at a cloud service platform to form analysis content corresponding to the handwriting data.

The cloud service platform is in communication connection with the intelligent pen in a communication mode, so that handwriting data written by the intelligent pen can be obtained, and data processing is performed based on the handwriting data.

Specifically, the track of the smart pen can be restored in the cloud service platform based on a pressure value, a position coordinate, a time value and an acceleration value contained in the handwriting data to form a graphical file; performing character recognition on the graphical file to obtain a character set corresponding to the data; and performing semantic analysis on the content in the character set to obtain the analysis content of the handwriting data, wherein the analysis content is the content according with the semantic grammar regulation.

S103, carrying out cluster analysis on the analyzed content to form a cluster analysis result.

A neural network model (e.g., a CNN convolutional neural network model) may be trained in advance, and the analysis content may be classified by the neural network model, so as to determine a cluster analysis result of the analysis content.

And S104, determining a medical model corresponding to the pathological information and recommendation information corresponding to the medical model based on the clustering analysis result.

Specifically, the pathological classification to which the clustering analysis result belongs can be searched; and determining a medical model corresponding to the pathological information based on the pathological classification. The medical model user describes the type of disease and corresponding treatment measures for the patient.

For this purpose, drug information corresponding to the medical model can be further searched; and setting the one or more pieces of medicine information as recommendation information corresponding to the medical model. In this way, the efficiency of writing medical records by doctors can be improved.

Furthermore, the selection information of the user for the one or more pieces of medicine information can be searched, and the selection information is added into the analysis content.

Referring to fig. 2, according to a specific implementation manner of the embodiment of the present disclosure, performing cluster analysis on the analyzed content to form a cluster analysis result includes:

s201, inputting the analysis content into a pre-trained neural network model;

s202, determining the clustering analysis result based on the calculation result of the neural network model.

Referring to fig. 3, according to a specific implementation manner of the embodiment of the present disclosure, the determining a medical model corresponding to the pathological information and recommended information corresponding to the medical model based on the cluster analysis result includes:

s301, searching the pathological classification to which the clustering analysis result belongs;

s302, determining a medical model corresponding to the pathological information based on the pathological classification.

Referring to fig. 4, according to a specific implementation manner of the embodiment of the present disclosure, the determining a medical model corresponding to the pathological information and recommended information corresponding to the medical model based on the cluster analysis result includes:

s401, searching medicine information corresponding to the medical model;

s402, setting the one or more pieces of medicine information as recommendation information corresponding to the medical model.

According to a specific implementation manner of the embodiment of the present disclosure, the determining, based on the cluster analysis result, the medical model corresponding to the pathological information and the recommendation information corresponding to the medical model includes: searching selection information of the user for the one or more pieces of medicine information; and adding the selection information into the analysis content.

As an optional mode, a graphical file corresponding to the handwriting data of the smart pen may be obtained, where the graphical file is generated by the handwriting data in a graphical mode.

After the intelligent pen finishes writing, the writing data written by the intelligent pen is uploaded to the cloud service platform, and the writing of the user is graphically restored in the cloud service platform, so that the graphical file is formed.

As an optional manner, content recognition may be performed on image characters in the graphic file, so as to obtain analysis content corresponding to the graphic file.

The graphical file comprises the content written by the user, the analysis content corresponding to the graphical file can be obtained by performing character recognition and content recognition on the graphical file, and the content contained in the graphical file can be obtained through the analysis content.

As an optional mode, the target content corresponding to the handwriting data may be searched for through a file identifier included in the graphical file.

The graphical file may contain a file identifier, which is used to indicate the content identifier in the graphical file, and the file identifier may be a test question number, for example. By identifying the file identifier, target content (for example, answers to test questions) corresponding to the file identifier can be searched in a database preset in the cloud service platform.

As an alternative, the evaluation content of the graphic file may be determined by performing similarity determination between the analysis content and the target content.

By the method for judging the similarity between the analysis content and the target content, whether the analysis content handwritten by the user through the intelligent pen is correct or not can be judged through the target content, and then corresponding evaluation is given.

According to a specific implementation manner of the embodiment of the present disclosure, the acquiring the graphical file corresponding to the smart pen handwriting data includes: after the intelligent pen completes handwriting writing, executing graphical operation on the intelligent pen handwriting in the cloud service platform; and after the imaging is finished, acquiring an imaging file corresponding to the intelligent pen handwriting data.

According to a specific implementation manner of the embodiment of the present disclosure, the content recognition of the image characters in the graphic file includes: performing character detection on the handwriting in the graphical file to obtain a character set; and performing semantic analysis on the content in the character set, and determining the analysis content corresponding to the graphical file based on the result of the semantic analysis.

According to a specific implementation manner of the embodiment of the present disclosure, searching for target content corresponding to the handwriting data through a file identifier included in the graphical file includes: inputting a file identifier contained in the graphical file in the cloud service platform; and searching target content corresponding to the handwriting data in a database preset by a cloud service platform based on the file identification.

According to a specific implementation manner of the embodiment of the present disclosure, the determining the evaluation content of the graphic file by performing similarity determination between the analysis content and the target content includes: vectorization calculation is carried out on the analysis content and the target content respectively to obtain an analysis content vector and a target content vector; carrying out similarity calculation on the analyzed content vector and the target content vector to obtain a similarity calculation result; and determining the evaluation content corresponding to the graphical file based on the similarity calculation result.

According to a specific implementation manner of the embodiment of the present disclosure, after the obtaining of the graphical file corresponding to the smart pen handwriting data, the method further includes: and carrying out target recognition on the image characters in the graphical file to obtain one or more graphical characters.

According to a specific implementation manner of the embodiment of the present disclosure, the method further includes: searching a target graph corresponding to the graphical character based on the standard character code corresponding to the graphical character; and determining evaluation information of the graphical character by judging the similarity between the graphical character and the target graph.

And carrying out target recognition on the image characters in the graphical file to obtain one or more graphical characters.

The graphic file contains one or more characters written by the user through the intelligent pen, the characters can be Chinese and English symbols, numbers or figures, and the like, and one or more graphic characters can be obtained through target recognition of the image characters, and the graphic characters describe the writing track of the user in the form of images. For example, the graphical character may be a calligraphy font that the user writes using a smart pen.

As an alternative, the target graphic corresponding to the graphical character may be searched based on a standard character code corresponding to the graphical character.

The character recognition can be carried out on the graphical character in a mode of carrying out OCR recognition on the graphical character, so that the standard character code corresponding to the graphical character is obtained. Meanwhile, a target graph is stored in the cloud service platform, the target graph is a standard style corresponding to the standard character code, and the target graph corresponding to the graphical character can be searched in the cloud service platform through the standard character code.

As an example, the graphical character may be a user-written regular-script font, the target image may be a corresponding standard regular-script font, and the standard regular-script font may be found by recognizing the user-written regular-script font.

As an alternative, the evaluation information of the graphical character may be determined by performing similarity determination between the graphical character and the target graphic.

Whether the graphical characters written by the user are standard or not can be judged through calculating the similarity between the graphical characters and the target graph, for example, when the similarity between the graphical characters and the target graph reaches over 90%, the writing handwriting of the user can be considered to reach an excellent level, and at the moment, excellent evaluation can be given in the evaluation information.

According to a specific implementation manner of the embodiment of the present disclosure, the acquiring the graphical file corresponding to the smart pen handwriting data includes:

after the intelligent pen completes handwriting writing, executing graphical operation on the intelligent pen handwriting in the cloud service platform;

and after the imaging is finished, acquiring an imaging file corresponding to the intelligent pen handwriting data.

According to a specific implementation manner of the embodiment of the present disclosure, the performing target recognition on the image characters in the graphic file to obtain one or more graphic characters includes:

performing edge detection on graphic characters in the graphic file;

based on the results of the edge detection, the one or more graphical characters are determined.

According to a specific implementation manner of the embodiment of the present disclosure, the searching for the target graphic corresponding to the graphical character based on the standard character code corresponding to the graphical character includes:

carrying out pattern recognition on the graphical characters to obtain standard character codes corresponding to the graphical characters;

and searching a target graph corresponding to the standard character code in a preset target graph database based on the standard character code.

According to a specific implementation manner of the embodiment of the present disclosure, the determining evaluation information of the graphical character by performing similarity judgment between the graphical character and the target graph includes:

carrying out similarity calculation on the graphical characters and the target graph to obtain a calculation result;

and generating evaluation information for the graphical character based on the calculation result.

According to a specific implementation manner of the embodiment of the present disclosure, after determining evaluation information of the graphical character by performing similarity determination between the graphical character and the target graph, the method further includes:

calculating a first characteristic of the handwriting data based on a handwriting matrix and a stored value corresponding to the handwriting data of the intelligent pen;

and acquiring a character set contained in a graphical file corresponding to the handwriting data so as to determine a second characteristic of the handwriting data based on the character set.

According to a specific implementation manner of the embodiment of the present disclosure, the method further includes:

performing feature extraction on semantic recognition content corresponding to the character set to obtain a third feature of the handwriting data;

and determining the writing behavior characteristic corresponding to the intelligent pen handwriting data based on the first characteristic, the second characteristic and the third characteristic.

As an alternative, the first characteristic of the handwriting data can be calculated based on a handwriting matrix and stored values corresponding to the handwriting data of the smart pen.

The handwriting matrix is a feature matrix extracted from elements representing handwriting features such as position coordinates, acceleration values and pressure values contained in the handwriting after the user writes the handwriting, and is used for identifying specific information and features of the user handwriting, and the user handwriting can be restored through the handwriting matrix.

The storage value is a characteristic value of a historical handwriting matrix of the user stored in the cloud service platform, and the characters contained in the graphical file have a one-to-one correspondence with the handwriting matrix or the storage value.

When the handwriting matrix has a corresponding storage value in the cloud service platform, the storage value can be directly adopted to replace the handwriting matrix. When the handwriting matrix does not have a corresponding storage value in the cloud service platform, calculating a characteristic value of the handwriting matrix; the feature value is formed with the storage value into a feature vector of a first feature.

As an optional manner, a character set included in a graphical file corresponding to the handwriting data may be acquired, so as to determine the second feature of the handwriting data based on the character set.

The character set is a result of character recognition of the graphical file after the handwriting of the intelligent pen is graphical, a second vector corresponding to the character set can be obtained by calculating the characteristic vector of the character set, and the second vector is used as a second characteristic of the handwriting data.

As an optional way, feature extraction may be performed on semantic recognition content corresponding to the character set to obtain a third feature of the handwriting data.

After the cloud service platform obtains the character set, semantic analysis is further performed on the content in the character set in a semantic analysis mode to obtain semantic identification content, and feature analysis is performed on the semantic identification content to further obtain a feature vector of handwriting data as a third feature.

As an alternative, the writing behavior feature corresponding to the smart pen handwriting data may be determined based on the first feature, the second feature and the third feature.

As a mode, the first feature, the second feature and the third feature may be used as feature vectors to form a writing behavior matrix; by means of calculating the characteristic value of the demonstration of the writing behavior, the characteristic value of the writing behavior matrix can be determined as the writing behavior characteristic corresponding to the intelligent pen handwriting data, and therefore the behavior characteristic corresponding to the intelligent pen handwriting data is obtained.

After the behavior characteristics are obtained, the behavior characteristics can be used as a signature value of handwriting data to realize handwriting authentication of a user, each character written by the user through the intelligent pen can identify a writer, and scenes such as file signature authenticity identification and cheating prevention in an examination can be applied.

According to a specific implementation manner of the embodiment of the disclosure, calculating a first feature of the handwriting data based on the handwriting matrix and the stored value corresponding to the handwriting data of the smart pen includes: calculating the characteristic value of the handwriting matrix; the feature value is formed with the storage value into a feature vector of a first feature.

According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a character set included in a graphical file corresponding to the handwriting data so as to determine a second feature of the handwriting data based on the character set includes: performing vector calculation on the characters in the character set to obtain a second vector; and taking the second vector as a feature vector of the second feature.

According to a specific implementation manner of the embodiment of the present disclosure, the performing feature extraction on the semantic recognition content corresponding to the character set to obtain a third feature of the handwriting data includes: performing vector calculation on the semantic identification content to obtain a third vector; and taking the third vector as a feature vector of the third feature.

According to a specific implementation manner of the embodiment of the present disclosure, the determining, based on the first feature, the second feature, and the third feature, a writing behavior feature corresponding to the smart pen handwriting data includes:

constructing a writing behavior matrix based on the first feature, the second feature and the third feature;

and determining the characteristic value of the writing behavior matrix as the writing behavior characteristic corresponding to the intelligent pen handwriting data.

According to a specific implementation manner of the embodiment of the disclosure, before calculating the first feature of the handwriting data based on the handwriting matrix and the stored value corresponding to the handwriting data of the smart pen, the method further includes:

acquiring a character set formed based on an imaging file, wherein the imaging file is formed based on an intelligent pen handwriting;

and judging whether an individualized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file.

when the personalized semantic database corresponding to the graphical file exists, calling the personalized semantic database to analyze the content in the character set to obtain an analysis result;

and re-determining the content in the character set based on the analysis result.

As an alternative, a character set formed based on a graphical file formed based on a smart pen stroke may be obtained.

The method comprises the steps that the writing handwriting of the intelligent pen is displayed in the graphical file in a graphical mode, in order to further obtain the content written by a user in the graphical file, character recognition can be conducted on the writing handwriting of the user in the graphical file, so that a character set corresponding to the graphical file is obtained, and the writing content of the intelligent pen in the graphical file can be determined through the character set.

However, in the process of converting a graphic file into characters, there are cases where recognition errors occur, and for this purpose, it is necessary to perform content analysis and recognition based on a recognized character set.

As an optional mode, whether an individualized semantic database corresponding to the graphical file exists or not can be judged in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file.

In addition, a personalized semantic database is arranged in the cloud service platform, and the personalized semantic database is generated based on a historical record for character recognition of the graphical file and can record a handwriting matrix and personalized semantic content corresponding to a stored value.

As an optional mode, when there is a personalized semantic database corresponding to the graphic file, the personalized semantic database may be called to analyze the content in the character set, so as to obtain an analysis result.

Specifically, the characters in the character set may be subjected to word segmentation processing to obtain one or more word segmentation vectors; performing vector comparison on the word segmentation vector and a semantic vector in the personalized semantic database; and determining whether the word segmentation vectors in the character set are correct or not based on the result of the vector comparison.

As an alternative, the content in the character set may be re-determined based on the parsing result.

And when the word segmentation vector is inconsistent with the semantic vector in the personalized semantic database, correcting the character vector in the character set by using the semantic vector in the personalized semantic database, and further re-determining the content in the character set.

According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a character set formed based on a graphic file includes: judging whether a new graphical file is generated in the cloud service platform; if yes, after the cloud service platform completes character recognition on the graphical file, a character set obtained by the graphical file recognition is obtained.

According to a specific implementation manner of the embodiment of the present disclosure, the determining whether there exists an individualized semantic database corresponding to the graphical file in a database preset in a cloud service platform based on the handwriting matrix and the stored value corresponding to the graphical file includes: inputting the handwriting matrix and the storage value into a database in the cloud platform; and inquiring whether a personalized semantic database corresponding to the handwriting matrix and the storage value exists.

According to a specific implementation manner of the embodiment of the present disclosure, the invoking the personalized semantic database to analyze the content in the character set includes: performing word segmentation processing on the characters in the character set to obtain one or more word segmentation vectors; performing vector comparison on the word segmentation vector and a semantic vector in the personalized semantic database; and determining whether the word segmentation vectors in the character set are correct or not based on the result of the vector comparison.

According to a specific implementation manner of the embodiment of the present disclosure, the re-determining the content in the character set based on the parsing result includes: and when the word segmentation vector in the display character set in the analysis result is inconsistent with the semantic vector in the personalized semantic database, correcting the content in the word segmentation vector based on the semantic vector in the personalized semantic database.

According to a specific implementation manner of the embodiment of the present disclosure, before the obtaining of the character set formed based on the graphic file, the method further includes: acquiring an imaging file needing character recognition, wherein the imaging file is generated by handwriting of an intelligent pen; and searching the handwriting matrix and the storage value for generating the graphical file in the cloud service platform.

According to a specific implementation manner of the embodiment of the present disclosure, the method further includes: judging whether historical characters corresponding to the handwriting matrix and the stored value exist in a preset character recognition database; and if so, directly using the historical characters as the characters of the graph to be recognized in the graphical file.

As an alternative, a graphical file that needs character recognition may be obtained, the graphical file being generated by handwriting of the smart pen.

The graphical file is formed by converting the writing track of the intelligent pen and is used for displaying the writing track of the intelligent pen in a graphical mode, and the graphical file can be various types of graphical files.

Before character recognition is carried out, a newly generated graphical file can be directly searched in a cloud service platform, and the graphical file needing to be subjected to character recognition in real time is obtained.

As an optional mode, a handwriting matrix and a storage value for generating the graphical file can be searched in a cloud service platform.

As an alternative, it may be determined whether there is a historical character corresponding to the handwriting matrix and the stored value in a preset character recognition database.

The character recognition database stores the characters which are recognized before, and simultaneously stores the one-to-one correspondence between the recognized characters and the handwriting matrix or the stored value.

As an optional mode, if yes, the historical characters are directly used as characters of a graph to be recognized in the graphical file.

By the method, the characters of the graph to be recognized can be directly recognized based on the historical recognition records, and character recognition is not needed to be carried out on each graph in each graphical file, so that the character recognition efficiency is greatly improved.

According to a specific implementation manner of the embodiment of the present disclosure, after determining whether there is a historical character corresponding to the handwriting matrix and the stored value in the preset character recognition database, the method further includes:

when the historical characters corresponding to the handwriting matrix and the stored values do not exist in a preset character recognition database, directly recognizing the characters on the graphical file;

and storing the recognized characters and handwriting matrixes or stored values corresponding to the recognized characters into the character recognition database.

According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a graphical file that needs character recognition includes: searching a newly generated graphical file in the cloud service platform;

and taking the newly generated graphical file as a graphical file needing character recognition.

According to a specific implementation manner of the embodiment of the present disclosure, the searching and generating the handwriting matrix and the stored value of the graphical file in the cloud service platform includes: and inquiring the handwriting matrix and the storage value of the graphical file in a data acquisition module of the cloud service platform.

According to a specific implementation manner of the embodiment of the present disclosure, the determining whether there is a historical character corresponding to the handwriting matrix and the stored value in the preset character recognition database includes:

inputting the handwriting matrix and the storage value into the character recognition database to execute query operation;

and judging whether historical characters corresponding to the handwriting matrix and the stored value exist or not based on the result of the query operation.

According to a specific implementation manner of the embodiment of the present disclosure, the directly using the historical characters as characters of a graph to be recognized in a graphical file includes:

acquiring the position coordinates of the graph to be identified in the graphical file;

and setting the historical characters at the position coordinates of the graph to be recognized in the graphical file to obtain a character recognition result.

According to a specific implementation manner of the embodiment of the present disclosure, before the obtaining of the graphic file that needs character recognition, the method further includes:

acquiring handwriting data needing to be imaged, wherein the handwriting data comprises paper information, a handwriting matrix and a stored value when an intelligent pen writes, the handwriting matrix is generated by an intelligent pen client based on the intelligent pen handwriting, and the stored value is generated by a cloud service platform based on historical handwriting data of a user;

searching a handwriting matrix and a stored value corresponding to the current paper information in a graphical module of the cloud service platform;

sequencing the handwriting matrix and the stored value based on the generation time corresponding to the handwriting matrix and the stored value to form a graphical sequencing result;

and forming a graphical file of the handwriting data on the current page according to the handwriting matrix and the graphical style corresponding to the storage value on the current page in sequence based on the graphical sorting result.

As an optional mode, handwriting data needing to be imaged can be obtained, the handwriting data comprise paper information, a handwriting matrix and a stored value when the intelligent pen writes, the handwriting matrix is generated by the intelligent pen client based on the intelligent pen handwriting, and the stored value is generated by the cloud service platform based on historical handwriting data of a user.

After the handwriting data is generated at the intelligent pen end, in order to improve the recognition efficiency of the handwriting of the intelligent pen, the handwriting data of the intelligent pen can be uploaded to the cloud service platform, the handwriting data is processed through the cloud service platform, as a way of the handwriting data, the handwriting data is converted into a graphic file, and the real shape of the handwriting is displayed through the graphic file.

Therefore, paper information, a handwriting matrix and a storage value formed by the intelligent pen during writing can be acquired from the handwriting data.

The paper information is used to describe on which paper the smart pen has performed handwriting writing, for example, the user writes 10 pages of content through the smart pen, and at this time, the content written by the user can be searched through 1-10 pages.

The stored value is a characteristic value of the historical handwriting matrix of the user stored in the cloud service platform, and when the handwriting matrix generated during writing of the intelligent pen is stored in the historical handwriting matrix stored in the cloud service platform, the stored value of the historical handwriting matrix is used for replacing the newly generated handwriting matrix, so that the data processing process is saved, and the system resources are reduced.

As an optional mode, a handwriting matrix and a stored value corresponding to the current paper information can be searched in a graphical module of the cloud service platform.

The cloud service platform can comprise a graphical module, and handwriting matrixes and stored values corresponding to current paper information stored in the database can be inquired through the graphical module, so that the handwriting of a user can be restored based on the inquired handwriting matrixes and stored values.

As an optional manner, the handwriting matrix and the stored value may be sorted based on the generation time corresponding to the handwriting matrix and the stored value, so as to form a graphical sorting result.

Specifically, the handwriting matrix and the handwriting corresponding to the stored value may be sorted in an ascending manner or a descending manner, so that the handwriting of the current page may be sorted according to the actual generation sequence or the reverse sequence of the handwriting.

As an optional manner, based on the graphical sorting result, a graphical file of the handwriting data on the current page may be formed on the current page in sequence according to the handwriting matrix and the graphical style corresponding to the storage value.

After the current page is sequentially sorted according to the handwriting matrixes and the graphic styles corresponding to the storage values, a pressure value or a position coordinate corresponding to each handwriting matrix or the storage values can be further obtained, the thickness characteristics of the handwriting are determined through the pressure values, the position coordinate of the handwriting on the current page is determined through the position coordinate, and finally a graphical handwriting file is formed.

Through the content of the embodiment, the handwriting can be rapidly subjected to graphical operation, and the medical information processing efficiency is improved.

According to a specific implementation manner of the embodiment of the present disclosure, the acquiring handwriting data that needs to be graphed includes: inquiring newly generated handwriting data in the cloud service platform; and considering the newly generated handwriting data as the handwriting data needing to be imaged.

According to a specific implementation manner of the embodiment of the present disclosure, in the graphical module of the cloud service platform, searching the handwriting matrix and the stored value corresponding to the current paper information includes: executing query operation in a database of the cloud service platform based on the acquired identification of the smart pen; and obtaining a handwriting matrix and a storage value corresponding to the current paper information based on the query result.

According to a specific implementation manner of the embodiment of the present disclosure, the sorting the handwriting matrix and the stored value based on the generation time corresponding to the handwriting matrix and the stored value includes: the handwriting matrixes and the generation time of the storage values are arranged in an ascending order; and determining the arrangement sequence of the handwriting matrix and the storage values based on the ascending arrangement result.

According to a specific implementation manner of the embodiment of the present disclosure, the forming, on the basis of the graphical sorting result, a graphical file of the handwriting data on the current page according to the handwriting matrix and the graphical style corresponding to the storage value in sequence includes: searching the handwriting position coordinates and the pressure values corresponding to the handwriting matrixes or the storage values according to the time sequence; and generating the graphical handwriting corresponding to the handwriting matrix or the storage value based on the handwriting position coordinates and the pressure value.

According to a specific implementation manner of the embodiment of the present disclosure, before acquiring handwriting data that needs to be patterned, the method further includes: and dividing the acquired handwriting data based on the pressure value and the acceleration value to form a plurality of handwriting data sections.

According to a specific implementation manner of the embodiment of the disclosure, after the obtained handwriting data is divided based on the pressure value and the acceleration value to form a plurality of handwriting data segments, the method further includes: packaging the time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence corresponding to the handwriting data segment to form a handwriting matrix corresponding to the handwriting data segment; sending the characteristic value corresponding to the handwriting matrix to a data acquisition module in a cloud service platform so that the data acquisition module can inquire whether stored values similar to the characteristic value exist in handwriting data stored in the cloud service platform or not; when the storage values similar to the characteristic values already exist in the cloud service platform, directly calling the storage matrix corresponding to the storage values as the characteristic matrix corresponding to the characteristic values.

The intelligent pen is at the in-process of writing, can generate the writing orbit of intelligent pen through the mode of dot matrix, writes the multiple data that the orbit can include the intelligent pen, for example, the generation time of handwriting, the pressure value of nib, the position coordinate of writing the pen on writing paper, the acceleration value when writing etc.. The data are sampled and arranged according to time training, so that a time sequence, a pressure value sequence, a position coordinate sequence and an acceleration value sequence can be formed, and the time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence can be used for describing and restoring handwriting of a user.

As an alternative, the handwriting data may be divided based on pressure values and acceleration values to form a plurality of handwriting data segments.

If the handwriting of the intelligent pen is directly uploaded to a server side for data processing, the processing speed of the data is low due to the fact that the data volume is too large, and therefore the handwriting data of the intelligent pen needs to be processed.

As one approach, the first pressure value threshold and the second acceleration threshold may be set first. The pressure value sequence is divided based on the first pressure value threshold value to form a plurality of pressure value sequences, for example, a pressure value sequence part larger than the first pressure value threshold value can be divided to form one or more pressure value sequences to represent one or more handwriting strokes actually written by the user.

After the pressure value sequences are determined, the acceleration value sequences corresponding to each pressure value sequence can be further searched, and based on a second acceleration value threshold, the acceleration value sequences are cut to form a plurality of acceleration value sequences. Through the second acceleration threshold, handwriting data of the user in a pause state can be filtered, and therefore segmented handwriting data are further simplified. And finally, dividing the handwriting data based on the time sequence corresponding to the acceleration value sequence.

As an optional manner, the time sequence, the pressure value sequence, the position coordinate sequence, and the acceleration value sequence corresponding to the handwriting data segment may be encapsulated to form a handwriting matrix corresponding to the handwriting data segment.

The time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence can be respectively used as row vectors or column vectors, and then one or more handwriting matrixes corresponding to the handwriting data segments are formed.

As an optional mode, the eigenvalue corresponding to the handwriting matrix may be sent to a data acquisition module in the cloud service platform, so that the data acquisition module can query whether stored values similar to the eigenvalue exist in the handwriting data already stored in the cloud service platform; when the stored values similar to the characteristic values exist in the cloud service platform, a storage matrix corresponding to the stored values is directly called as the characteristic matrix corresponding to the characteristic values, and when the stored values similar to the characteristic values do not exist in the cloud service platform, an intelligent pen client generating the characteristic data is informed to upload the handwriting matrix to the data acquisition module.

The storage value is a writing characteristic value formed based on previous writing of a user, whether a storage matrix stored in the cloud service platform is called or not can be determined by comparing whether the characteristic value is similar to the storage value or not, and data in the writing matrix is directly replaced by numerical values in the storage matrix, so that data transmission and calculation amount are further reduced, and the writing processing efficiency is improved.

By uploading the characteristic values, the calculation amount of the data can be further reduced, and the calculation process of the data is simplified.

According to a specific implementation manner of the embodiment of the present disclosure, the acquiring handwriting data of the smart pen includes: monitoring whether pressure data generation exists in the smart pen; and if so, acquiring handwriting data generated by the intelligent pen.

According to a specific implementation manner of the embodiment of the present disclosure, the dividing the handwriting data according to the pressure value and the acceleration value includes: dividing the pressure value sequence based on a first pressure value threshold value to form a plurality of pressure value sequences. The pressure value sequence is divided based on the first pressure value threshold value to form a plurality of pressure value sequences, for example, a pressure value sequence part larger than the first pressure value threshold value can be divided to form one or more pressure value sequences to represent one or more handwriting strokes actually written by the user.

Searching an acceleration value sequence corresponding to each pressure value sequence; and based on the second acceleration value threshold value, performing cutting operation on the acceleration value sequence to form a plurality of acceleration value sequences. And based on the second acceleration value threshold value, performing cutting operation on the acceleration value sequence to form a plurality of acceleration value sequences. Through the second acceleration threshold, handwriting data of the user in a pause state can be filtered, and therefore segmented handwriting data are further simplified. And dividing the handwriting data based on the time sequence corresponding to the acceleration value sequence. With the above embodiment, the amount of calculation of data can be further reduced by setting the threshold value.

According to a specific implementation manner of the embodiment of the present disclosure, the encapsulating the time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence corresponding to the handwriting data segment includes:

and taking the time sequence, the pressure value sequence, the position coordinate sequence and the acceleration value sequence as row vectors of the matrix, and forming a handwriting matrix corresponding to the handwriting data section in a time sequence pair mode.

According to a specific implementation manner of the embodiment of the present disclosure, before the eigenvalue corresponding to the handwriting matrix is sent to a data acquisition module in a cloud service platform, the method further includes:

and respectively calculating the characteristic values of the divided handwriting data to form a characteristic value sequence of the handwriting data.

and performing graphical processing on the handwriting data obtained by the data acquisition module by using a graphical module in the cloud service platform to obtain handwriting image data of the intelligent pen.

According to a specific implementation manner of the embodiment of the present disclosure, the method further includes: aiming at the handwriting image data, performing character recognition by using a character recognition module in a cloud service platform to obtain character data corresponding to the handwriting image data; and performing content analysis service on the character data through a content analysis module in the cloud service platform to form writing content data corresponding to the handwriting data.

The intelligent pen is used as an electronic equipment terminal, writing data of a user can be collected in the modes of pressure, acceleration value and the like under the use of the user, so that writing data are formed, and the writing data are used as handwriting data of the user and are transmitted to the cloud service platform in a wireless or wired mode.

The cloud service platform is connected with the intelligent pen terminal in a wired or wireless mode, a plurality of data processing modules can be arranged in the cloud service platform, and writing data generated by the intelligent pen are processed and analyzed through the processing modules, so that the handwriting recognition and identification of a user become more accurate and efficient.

As a mode, a data acquisition module is arranged in the cloud service platform, and handwriting data written by a user can be acquired and stored through the data acquisition module.

The data acquisition module can be set to have extremely high flexibility and expandability, and can adjust the resource configuration in time according to the data acquisition requirement, ensure the quick response of the system and avoid the data blockage caused by the quick expansion of the traffic.

The data acquisition module is provided with a data storage service unit and is used for supporting high-concurrency data storage service and providing support for distributed computation by adopting distributed data storage service in a big data architecture.

The handwriting of the user collected by the data collection module is usually stored in the modes of time, position coordinates, pressure values, acceleration values and the like, and therefore, the collected handwriting data needs to be subjected to imaging processing to be restored into the real handwriting of the user.

Therefore, various data such as time, configuration, movement, pressure and the like of the acquired original handwriting data can be subjected to structural processing, the original handwriting data can be calculated into image and video data through a graphical calculation module, and finally the image and video data are output in various output formats such as bitmap, vector diagram and dynamic video, and the handwriting of the user is fixed in the form of the image.

After the handwriting image data corresponding to the writing is obtained, the graphical characters can be identified by utilizing a character identification module arranged in the cloud service platform, so that the character data corresponding to the handwriting image is obtained.

The character recognition function of handwriting can be set in the character recognition module, and data written by a user can be quickly converted into standard characters which can be recognized by a computer, for example, recognition characters of contents such as Chinese characters, letters, symbols and formulas can be set.

As an optional mode, a semantic understanding function based on a natural language processing technology is added in a handwriting recognition module, the probability of character content can be calculated according to text content of context, and the accuracy of character recognition is improved.

After the handwriting of the user is recognized as the standard character, the content can be analyzed by utilizing a content analysis module arranged on a cloud service platform to execute artificial intelligent technologies such as natural language processing, machine learning and deep learning, and the services comprise entity recognition, relation extraction, semantic understanding, abstract extraction, keyword extraction, knowledge map construction and the like of the character content.

By analyzing the content, the total judgment and analysis can be carried out on the writing content of the user by integrating all the context content of the handwriting of the user, and the accuracy of the writing content data is further improved.

Through the content and the scheme of the embodiment, the handwriting of the user can be processed at the cloud end, so that the processing efficiency and the accuracy of the handwriting of the intelligent pen are improved.

According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: and performing characteristic analysis on the writing behavior of the user based on the content data to form a writing characteristic font library corresponding to the user. For example, the writing behaviors of the user including the writing characteristics of a single character, the specific drawing writing characteristics, the overall writing habit, the writing speed and other writing characteristics can be extracted and analyzed, a unique character characteristic library of the specific user can be generated, handwriting authentication of the user is realized, each character written by the user through the intelligent pen can identify a writer, and scenes such as file signature authenticity identification and cheating prevention in an examination can be applied.

According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: and comparing and analyzing the handwriting image data with preset target handwriting data according to target characteristics, and determining an analysis result of the handwriting image data based on the comparison and analysis result. For example, the system can receive preset written/drawn target characters/graphics, collect the written content of the user, and calculate the similarity between the target and the written result by using the methods of graph hash value comparison, cosine similarity comparison, mutual information comparison, and the like, so as to judge the similarity between the written content of the user and the target, and the system can be applied to scenes such as calligraphy learning, drawing learning, and the like.

According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes:

firstly, comparing the content data with preset target data to form a content comparison result.

As an application scenario, the content data may be answer data written during a process of a user performing an examination or the like, the target data may be answer data corresponding to the examination content, and the comparison result may be formed by comparing the content data with the target data.

Secondly, based on the content comparison result, determining a similarity value between the content data and the target data.

Through the comparison result formed in the steps, the similarity value between the content data and the target data can be determined, and therefore the accuracy of the handwriting data solved by the user can be further determined. By the content of the embodiment, whether the content written by the user is correct can be further judged based on the writing data of the user.

According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: and simultaneously sending the handwriting image data and the content data to a client so as to facilitate the client to display the handwriting image data or the content data.

According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: identifying the content data, and judging whether table content data exists in the content data or not; and if so, displaying the table content data in a table form.

By the method, the data which needs to be displayed in the form mode can be identified, and the part of content is displayed in the form mode, so that the processing function of the intelligent pen data is improved.

According to a specific implementation manner of the embodiment of the present disclosure, after the writing content data corresponding to the handwriting data is formed, the method further includes: and performing semantic analysis on the content data, and judging whether recommended data responding to the content data exists or not. The recommended data may be data corresponding to content data, and as an example, the content data is pathological data of a user written by a doctor by handwriting or the like, and prescription data (recommended data) corresponding to the pathological data may be recommended by analyzing the pathological data, thereby facilitating the doctor to select part of the recommended data according to actual needs. And if so, generating recommendation data corresponding to the content data. With this embodiment, the writing efficiency of the written content data can be further improved.

Corresponding to the above embodiment, referring to fig. 5, the present embodiment also discloses a medical information processing apparatus 50, including:

the acquiring module 501 is configured to acquire handwriting data formed by the intelligent pen in a preset writing area, where the handwriting data is used to describe pathological information;

the processing module 502 is configured to perform data processing on the handwriting received from the sensing device at a cloud service platform to form analysis content corresponding to the handwriting data;

a clustering module 503, configured to perform clustering analysis on the analyzed content to form a clustering analysis result;

a determining module 504, configured to determine, based on the cluster analysis result, a medical model corresponding to the pathological information and recommendation information corresponding to the medical model.

For parts not described in detail in this embodiment, reference is made to the contents described in the above method embodiments, which are not described again here.

Referring to fig. 6, an embodiment of the present disclosure also provides an electronic device 60, including:

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

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the medical information processing method of the foregoing method embodiments.

The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the medical information processing method in the aforementioned method embodiments.

Referring now to FIG. 6, a schematic diagram of an electronic device 60 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 6, the electronic device 60 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 60 are also stored. The processing device 601, the ROM602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. While the figures illustrate an electronic device 60 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.

Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A medical information processing method characterized by comprising:

2. The method according to claim 1, wherein the acquiring handwriting data formed by the smart pen in a preset writing area comprises:

3. The method according to claim 2, wherein the data processing of the handwriting received from the sensing device at a cloud service platform comprises:

4. The method of claim 1, wherein performing cluster analysis on the parsed content to form a cluster analysis result comprises:

inputting the analysis content into a pre-trained neural network model;

5. The method according to claim 1, wherein the determining the medical model corresponding to the pathological information and the recommended information corresponding to the medical model based on the cluster analysis result includes:

6. The method according to claim 5, wherein the determining the medical model corresponding to the pathological information and the recommended information corresponding to the medical model based on the cluster analysis result comprises:

searching medicine information corresponding to the medical model;

7. The method according to claim 6, wherein the determining the medical model corresponding to the pathological information and the recommended information corresponding to the medical model based on the cluster analysis result comprises:

and adding the selection information into the analysis content.

8. A medical information processing apparatus characterized by 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 instructions executable by the at least one processor to enable the at least one processor to perform the medical information processing method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of the preceding claims 1-7.