Detailed Description
Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present disclosure will become readily apparent to those skilled in the art from the following disclosure, which describes embodiments of the present disclosure by way of specific examples. It will be apparent that the described embodiments are merely some, but not all embodiments of the present disclosure. The disclosure may be embodied or practiced in other different specific embodiments, and details within the subject specification may be modified or changed from various points of view and applications without departing from the spirit of the disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following 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 present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, 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. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the disclosure by way of illustration, and only the components related to the disclosure are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided in order to provide 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 in the present embodiment may be executed by a computing device, which may be implemented as software, or as a combination of software and hardware, and which may be integrally provided in a server, a client, or the like.
Referring to fig. 1, the medical information processing method in the embodiment of the present disclosure may include the steps of:
s101, handwriting data formed by the intelligent pen in a preset writing area are obtained, and the handwriting data are used for describing pathological information.
The intelligent pen can generate handwriting data by performing writing operation in a writing area of the sensing device, and the content written in the writing area by a user can be described through the handwriting data.
In the process of acquiring the writing content of the user, whether the pressure sensor on the intelligent pen generates a pressure signal can be further monitored, and handwriting data formed on the writing area by the intelligent pen can be acquired after the pressure value of the pressure signal is larger than a preset value.
As an application scenario, handwriting data can be pathological information handwritten by a doctor, and in this way, the handwriting can be directly stored in an electronic way.
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, handwriting data written by the intelligent pen can be obtained, and data processing is performed based on the handwriting data.
Specifically, the cloud service platform can restore 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 pen data; and carrying out semantic analysis on the content in the character set to obtain analysis content of the handwriting data, wherein the analysis content is content conforming to the specification of semantic grammar.
S103, carrying out cluster analysis on the analysis 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 parsed contents are classified through the neural network model, thereby determining a cluster analysis result of the parsed contents.
S104, determining a medical model corresponding to the pathological information and recommended information corresponding to the medical model based on the clustering analysis result.
Specifically, the pathological classification to which the cluster analysis result belongs may 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 corresponding to the patient and the corresponding treatment measures.
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 recommended information corresponding to the medical model. In this way, the efficiency of the doctor in writing medical records can be improved.
Furthermore, the selection information of the user on the one or more pieces of medicine information can be searched, and the selection information is added into the analysis content.
According to a specific implementation manner of the embodiment of the present disclosure, the obtaining handwriting data formed by the smart pen in a preset writing area includes:
Judging whether a pressure value generated by a pressure sensor of the intelligent pen is larger than a preset value or not;
if yes, handwriting data generated by the intelligent pen are collected in the writing area, wherein the handwriting data comprise pressure values, position coordinates, time values and acceleration values generated by the intelligent pen.
According to a specific implementation manner of the embodiment of the present disclosure, the processing, at the cloud service platform, the 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 pen data;
And carrying out semantic analysis on the content in the character set to obtain the analysis content of the handwriting data.
Referring to fig. 2, according to a specific implementation manner of the embodiment of the disclosure, the performing cluster analysis on the parsed 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 disclosure, the determining, based on the result of the cluster analysis, the medical model corresponding to the pathological information and the recommended information corresponding to the medical model includes:
s301, searching for 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 disclosure, the determining, based on the result of the cluster analysis, the medical model corresponding to the pathological information and the recommended information corresponding to the medical model includes:
s401, searching medicine information corresponding to the medical model;
s402, setting the one or more pieces of medicine information as recommended information corresponding to the medical model.
According to a specific implementation manner of the embodiment of the present disclosure, the determining, based on the result of the cluster analysis, the medical model corresponding to the pathological information and the recommended information corresponding to the medical model includes: searching selection information of a user for the one or more drug information; and adding the selection information into the analysis content.
As an alternative way, a graphic file corresponding to the handwriting data of the intelligent pen can be obtained, and the graphic file is generated by the handwriting data in a graphic way.
After the intelligent pen is written, writing handwriting data of the intelligent pen is uploaded to a cloud service platform, and the writing handwriting of a user is graphically restored in the cloud service platform, so that the graphical file is formed.
As an alternative way, content recognition may be performed on the image characters in the graphic file, so as to obtain the resolved content corresponding to the graphic file.
The graphic file contains the content written by the user, the analysis content corresponding to the graphic file can be obtained by carrying out character recognition and content recognition on the graphic file, the content contained in the graphic file can be obtained through the analysis content, and as a mode, the graphic file can be a test answer handwritten by the user, and the analysis content is character content which can be recognized by a standard computer and is used for analyzing the test answer handwritten by the user.
As an alternative, the target content corresponding to the handwriting data may be searched for by a file identifier included in the graphic file.
The graphical file may include a file identifier, where the file identifier is used to indicate a 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.
Alternatively, the evaluation content of the graphic file may be determined by performing similarity judgment between the parsed 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 obtaining a graphic file corresponding to handwriting data of an intelligent pen includes: after the intelligent pen finishes handwriting writing, performing graphical operation on the handwriting of the intelligent pen in a cloud service platform; and after the patterning is completed, a patterning file corresponding to the handwriting data of the intelligent pen is obtained.
According to a specific implementation manner of the embodiment of the present disclosure, the content recognition for the image characters in the graphic file includes: performing character detection on the handwriting in the graphical file to obtain a character set; and carrying out 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, the searching, through a file identifier included in the graphic file, for target content corresponding to the handwriting data 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 the cloud service platform based on the file identification.
According to a specific implementation manner of the embodiment of the present disclosure, the determining, by performing similarity judgment between the parsed content and the target content, the evaluation content of the graphic file includes: vectorizing calculation is carried out on the analysis content and the target content respectively, so that analysis content vectors and target content vectors are obtained; performing similarity calculation on the analysis 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 graphical file corresponding to the handwriting data of the smart pen is obtained, 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 of an embodiment of the disclosure, the method further includes: searching a target graph corresponding to the graphical character based on a 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.
As an alternative way, a graphic file corresponding to the handwriting data of the intelligent pen can be obtained, and the graphic file is generated by the handwriting data in a graphic way.
After the intelligent pen is written, writing handwriting data of the intelligent pen is uploaded to a cloud service platform, and the writing handwriting of a user is graphically restored in the cloud service platform, so that the graphical file is formed.
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 a user through the intelligent pen, the characters can be Chinese, english symbols, numbers or figures, and the like, and one or more graphic characters can be obtained by carrying out target recognition on 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 handwriting font written by the user using a smart pen.
As an alternative, the target graphic corresponding to the graphic character may be searched based on the standard character code corresponding to the graphic character.
The graphic character can be subjected to character recognition in an OCR recognition mode, so that the standard character code corresponding to the graphic character is obtained. Meanwhile, the cloud service platform stores target graphics, the target graphics are standard patterns corresponding to standard character codes, and the target graphics corresponding to the graphical characters can be searched in the cloud service platform through the standard character codes.
As an example, the graphic characters may be regular characters written by the user, the target image is a corresponding standard regular character, and the standard regular character can be found by recognizing the regular characters written by the user.
Alternatively, the evaluation information of the graphic character may be determined by performing similarity judgment between the graphic character and the target graphic.
Whether the graphical character written by the user is standard or not can be judged by calculating the similarity between the graphical character and the target graph and by the similarity, for example, when the similarity between the graphical character and the target graph reaches more than 90%, the user writing handwriting can be judged to reach an excellent level, and excellent evaluation can be given in the evaluation information.
According to a specific implementation manner of the embodiment of the present disclosure, the obtaining a graphic file corresponding to handwriting data of an intelligent pen includes:
After the intelligent pen finishes handwriting writing, performing graphical operation on the handwriting of the intelligent pen in a cloud service platform;
and after the patterning is completed, a patterning file corresponding to the handwriting data of the intelligent pen is obtained.
According to a specific implementation manner of the embodiment of the present disclosure, the performing object 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 graphic character based on the standard character code corresponding to the graphic character includes:
performing pattern recognition on the graphical character to obtain a standard character code corresponding to the graphical character;
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, by performing similarity judgment between the graphic character and the target graphic, evaluation information of the graphic character includes:
performing similarity calculation on the graphical character 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 the evaluation information of the graphic character by performing similarity judgment between the graphic character and the target graphic, the method further includes:
calculating a first characteristic of 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 of an embodiment of the disclosure, the method further includes:
extracting features of semantic recognition content corresponding to the character set to obtain third features of the handwriting data;
and determining writing behavior characteristics corresponding to the intelligent pen handwriting data based on the first characteristics, the second characteristics and the third characteristics.
As an alternative, the first feature of the handwriting data may be calculated based on a handwriting matrix and a stored value corresponding to the handwriting data of the smart pen.
The handwriting matrix is a characteristic matrix extracted from the elements representing the handwriting characteristics, such as position coordinates, acceleration values, pressure values and the like, contained in the handwriting after the user writes the handwriting, and is used for identifying specific information and characteristics of the handwriting of the user, and the handwriting of the user can be restored through the handwriting matrix.
The stored value is a characteristic value of a historical handwriting matrix of a user stored in the cloud service platform, characters contained in the graphical file have a one-to-one correspondence with the handwriting matrix or the stored value, and therefore the handwriting matrix and the stored value for generating the graphical file can be searched directly based on the correspondence.
When the handwriting matrix has a corresponding stored value in the cloud service platform, the stored 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 values together with the stored values form a feature vector of the first feature.
Alternatively, a set of characters included in a graphic file corresponding to the handwriting data may be acquired so as to determine the second feature of the handwriting data based on the set of characters.
The character set is a result of character recognition on the graphical file after the writing handwriting of the intelligent pen is graphical, and a second vector corresponding to the character set can be obtained by carrying out feature vector calculation on the character set, and the second vector is used as a second feature of the handwriting data.
As an alternative way, feature extraction may be performed on the semantic recognition content corresponding to the character set, so as to obtain a third feature of the handwriting data.
After the character set is obtained, the cloud service platform also needs to perform semantic analysis on the content in the character set in a semantic analysis mode to obtain semantic identification content, and can further obtain a feature vector of handwriting data as a third feature by performing feature analysis on the semantic identification content.
Alternatively, writing behavior features corresponding to the smart pen handwriting data may be determined based on the first feature, the second feature, and the third feature.
As one way, the first feature, the second feature, and the third feature may be used as feature vectors to form a writing behavior matrix; by calculating the characteristic value of the writing behavior evidence, the characteristic value of the writing behavior matrix can be determined as the writing behavior characteristic corresponding to the intelligent pen handwriting data, so that 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 signature values of handwriting data to realize handwriting identification of a user, each character written by the user through the intelligent pen can be used for identifying a writer, and the method can be applied to scenes such as file signature authenticity identification, examination cheating prevention and the like.
According to a specific implementation manner of the embodiment of the disclosure, the 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 includes: calculating a characteristic value of the handwriting matrix; the feature values together with the stored values form a feature vector of the first feature.
According to a specific implementation manner of an embodiment of the present disclosure, the obtaining a character set included in a graphic 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; the second vector is taken as a feature vector of the second feature.
According to a specific implementation manner of the embodiment of the present disclosure, the feature extraction of the semantic recognition content corresponding to the character set to obtain a third feature of the handwriting data includes: vector calculation is carried out 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 handwriting data of the intelligent pen.
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 a graphical file, wherein the graphical file is formed based on intelligent pen handwriting;
Based on the handwriting matrix and the storage value corresponding to the graphical file, judging whether a personalized semantic database corresponding to the graphical file exists in a database preset in a cloud service platform.
According to a specific implementation of an embodiment of the disclosure, the method further includes:
When a personalized semantic database corresponding to the graphical file exists, calling the personalized semantic database to analyze the content in the character set, and obtaining an analysis result;
And re-determining the content in the character set based on the analysis result.
As an alternative, a set of characters formed based on a graphical file formed based on smart pen writing may be obtained.
The graphical file displays the writing handwriting of the intelligent pen in a graphical mode, character recognition can be carried out on the writing handwriting of the user in the graphical file in order to further acquire the writing content 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 character-forming a graphic file, there is a case where recognition errors occur, and for this purpose, content analysis and recognition are required here based on the recognized character set.
As an alternative way, based on the handwriting matrix and the stored value corresponding to the graphical file, whether a personalized semantic database corresponding to the graphical file exists or not may be judged in a database preset in the cloud service platform.
The handwriting matrix is a characteristic matrix extracted from the elements representing the handwriting characteristics, such as position coordinates, acceleration values, pressure values and the like, contained in the handwriting after the user writes the handwriting, and is used for identifying specific information and characteristics of the handwriting of the user, and the handwriting of the user can be restored through the handwriting matrix.
The stored value is a characteristic value of a historical handwriting matrix of a user stored in the cloud service platform, characters contained in the graphical file have a one-to-one correspondence with the handwriting matrix or the stored value, and therefore the handwriting matrix and the stored value for generating the graphical file can be searched directly based on the correspondence.
In addition, the cloud service platform is further provided with a personalized semantic database, the personalized semantic database is generated based on a history record aiming at character recognition of the graphical file, and personalized semantic contents corresponding to the handwriting matrix and the storage value can be recorded.
As an alternative way, when there is a personalized semantic database corresponding to the graphic file, the personalized semantic database may be called to parse the content in the character set, so as to obtain a parsing result.
Specifically, word segmentation processing can be performed on characters in the character set to obtain one or more word segmentation vectors; vector comparison is carried out on the word segmentation vector and semantic vectors in the personalized semantic database; based on the result of the vector comparison, it is determined whether the word segmentation vector in the character set is correct.
As an alternative, the content in the character set may be redetermined based on the parsing result.
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 determining the content in the character set again.
According to a specific implementation manner of an embodiment of the present disclosure, the obtaining 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 finishes 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, based on the handwriting matrix and the stored value corresponding to the graphic file, whether there is a personalized semantic database corresponding to the graphic file in a database preset in a cloud service platform includes: inputting the handwriting matrix and the stored value into a database in the cloud platform; and inquiring whether a personalized semantic database corresponding to the handwriting matrix and the stored value exists.
According to a specific implementation manner of the embodiment of the present disclosure, the invoking the personalized semantic database to parse the content in the character set includes: performing word segmentation on characters in the character set to obtain one or more word segmentation vectors; vector comparison is carried out on the word segmentation vector and semantic vectors in the personalized semantic database; based on the result of the vector comparison, it is determined whether the word segmentation vector in the character set is correct.
According to a specific implementation manner of the embodiment of the present disclosure, the redefining, based on the parsing result, contents in the character set includes: 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 the character set formed based on the graphic file, the method further includes: acquiring a graphical file required to be subjected to character recognition, wherein the graphical file is generated by handwriting of an intelligent pen; and searching a handwriting matrix and a stored value for generating the graphical file in a cloud service platform.
According to a specific implementation of an embodiment of the disclosure, the method further includes: judging whether a history character corresponding to the handwriting matrix and a stored value exists in a preset character recognition database; if yes, the history characters are directly used as characters of the graph to be identified in the graphical file.
As an alternative, a graphical file may be obtained that requires character recognition, the graphical file being generated from the handwriting of the smart pen.
The graphic 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 graphic mode, and the graphic file can be various graphic files.
Before character recognition, the newly generated graphical file can be directly searched in the cloud service platform, and the graphical file which needs to be subjected to character recognition in real time is obtained.
As an alternative way, the handwriting matrix and the stored value for generating the graphical file can be searched in the cloud service platform.
The handwriting matrix is a characteristic matrix extracted from the elements representing the handwriting characteristics, such as position coordinates, acceleration values, pressure values and the like, contained in the handwriting after the user writes the handwriting, and is used for identifying specific information and characteristics of the handwriting of the user, and the handwriting of the user can be restored through the handwriting matrix.
The stored value is a characteristic value of a historical handwriting matrix of a user stored in the cloud service platform, characters contained in the graphical file have a one-to-one correspondence with the handwriting matrix or the stored value, and therefore the handwriting matrix and the stored value for generating the graphical file can be searched directly based on the correspondence.
As an alternative, it may be determined in a preset character recognition database whether there is a history character corresponding to the handwriting matrix and the stored value.
The character recognition database stores the previously recognized characters, and stores the one-to-one correspondence between the recognized characters and the handwriting matrix or the stored values, so that whether the history characters corresponding to the handwriting matrix and the stored values exist can be directly inquired in the character recognition database.
As an alternative way, if yes, the history character is directly used as the character of the graph to be identified in the graphic file.
By the method, the characters of the graphics to be recognized can be directly recognized based on the history recognition record, and each graphic in each graphic file is not required to be recognized, so that the character recognition efficiency is greatly improved.
According to a specific implementation manner of the embodiment of the present disclosure, after determining, in a preset character recognition database, whether there is a history character corresponding to the handwriting matrix and the stored value, the method further includes:
When a preset character recognition database does not contain the history characters corresponding to the handwriting matrix and the stored values, directly recognizing the characters on the graphical file;
And storing the recognized characters and the corresponding handwriting matrixes or storage values thereof into the character recognition database.
According to a specific implementation manner of the embodiment of the present disclosure, the obtaining a graphic file that needs to be subjected to character recognition includes: searching a newly generated graphical file in the cloud service platform;
and taking the newly generated graphical file as the graphical file needing character recognition.
According to a specific implementation manner of the embodiment of the present disclosure, searching the handwriting matrix and the stored value for generating the graphic file in the cloud service platform includes: and inquiring a handwriting matrix and a stored 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, in a preset character recognition database, determining whether there is a history character corresponding to the handwriting matrix and a stored value includes:
inputting the handwriting matrix and the stored value into the character recognition database to execute query operation;
Based on the result of the query operation, whether the history characters corresponding to the handwriting matrix and the stored value exist or not is judged.
According to a specific implementation manner of the embodiment of the present disclosure, the directly using the history character as a character of a graphic to be identified in a graphic 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 the graphic file needing to be subjected to character recognition, the method further includes:
The method comprises the steps of obtaining handwriting data to be patterned, wherein the handwriting data comprise paper surface information, a handwriting matrix and a stored value when an intelligent pen writes, the handwriting matrix is generated by an intelligent pen client terminal based on 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 storage value corresponding to the current paper surface information in a graphical module of the cloud service platform;
ordering 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 ordering result;
And based on the graphical ordering result, sequentially forming a graphical file of the handwriting data on the current page according to the graphical style corresponding to the handwriting matrix and the stored value on the current page.
As an alternative way, handwriting data to be patterned may be obtained, where the handwriting data includes paper information when the smart pen writes, a handwriting matrix, where the handwriting matrix is generated by the smart pen client based on the smart pen handwriting, and a stored value, where the stored value is generated by the cloud service platform based on the historical handwriting data of the 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 a cloud service platform, the handwriting data is processed through the cloud service platform, and the handwriting data is converted into a graphic file as one mode of the handwriting data, so that the real shape of the handwriting is displayed through the graphic file.
Therefore, the paper surface information, the handwriting matrix and the stored value formed by the intelligent pen during writing can be obtained from the handwriting data.
The paper information is used to describe on which paper the smart pen is writing, for example, 10 pages of content are written by the user through the smart pen, at which time the content written by the user can be found by 1-10 pages each.
The handwriting matrix is a characteristic matrix extracted from the elements representing the handwriting characteristics, such as position coordinates, acceleration values, pressure values and the like, contained in the handwriting after the user writes the handwriting, and is used for identifying specific information and characteristics of the handwriting of the user, and the handwriting of the user can be restored through the handwriting matrix.
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 at the moment, so that the data processing process is saved, and the system resource is reduced.
As an alternative way, the handwriting matrix and the stored value corresponding to the current paper surface information can be searched in the graphical module of the cloud service platform.
The cloud service platform can comprise a graphical module, and the graphical module can query a handwriting matrix and a stored value corresponding to the current paper information stored in the database, so that previous handwriting of the user can be restored based on the queried handwriting matrix and the stored value.
As an alternative, the handwriting matrix and the stored value may be ordered based on the generation time corresponding to the handwriting matrix and the stored value, so as to form a graphical ordering result.
Specifically, the handwriting matrix and the handwriting corresponding to the stored value may be ordered in an ascending or descending manner, so that the handwriting of the current page may be ordered according to the actual generation order or the reverse order of the handwriting.
As an alternative way, based on the graphical ordering result, a graphical file of the handwriting data on the current page can be formed according to the graphical style corresponding to the handwriting matrix and the stored value on the current page in sequence.
After the current page is sequenced according to the handwriting matrix and the graph style corresponding to the stored value, a pressure value or a position coordinate corresponding to each handwriting matrix or the stored value can be further obtained, the thickness characteristic of the handwriting is determined through the pressure value, the position coordinate of the handwriting on the current page is determined through the position coordinate, and finally the graphical handwriting file is formed.
Through the content of the embodiment, the graphical operation can be rapidly executed on the handwriting, and the efficiency of medical information processing is improved.
According to a specific implementation manner of the embodiment of the present disclosure, the obtaining handwriting data to be patterned includes: inquiring newly generated handwriting data in the cloud service platform; and recognizing the newly generated handwriting data as the handwriting data needing to be patterned.
According to a specific implementation manner of the embodiment of the present disclosure, searching, in a graphics module of a cloud service platform, a handwriting matrix and a stored value corresponding to current paper surface information includes: based on the obtained identification of the intelligent pen, query operation is executed in a database of the cloud service platform; based on the query result, a handwriting matrix and a storage value corresponding to the current paper surface information are obtained.
According to a specific implementation manner of the embodiment of the present disclosure, the ordering the handwriting matrix and the stored value based on the generation time corresponding to the handwriting matrix and the stored value includes: ascending arrangement is carried out on the handwriting matrix and the generation time of the stored value; and determining the arrangement sequence of the handwriting matrix and the storage value based on the result of the ascending arrangement.
According to a specific implementation manner of the embodiment of the present disclosure, based on the graphical ordering result, a graphical file of the handwriting data on a current page is formed sequentially according to a graphical style corresponding to the handwriting matrix and the stored value on the current page, including: searching handwriting position coordinates and pressure values corresponding to the handwriting matrix or the stored values according to time sequence; and generating the graphical handwriting corresponding to the handwriting matrix or the stored 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 the handwriting data to be patterned is obtained, 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 segments.
According to a specific implementation manner of an embodiment of the present disclosure, the method further includes, after dividing the obtained handwriting data based on the pressure value and the acceleration value to form a plurality of handwriting data segments: packaging a time sequence, a pressure value sequence, a position coordinate sequence and an acceleration value sequence corresponding to the handwriting data segment to form a handwriting matrix corresponding to the handwriting data segment; the characteristic values corresponding to the handwriting matrix are sent 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 values exist in handwriting data stored in the cloud service platform; when the storage value similar to the characteristic value exists in the cloud service platform, directly calling a storage matrix corresponding to the storage value as a characteristic matrix corresponding to the characteristic value.
The intelligent pen can generate a writing track of the intelligent pen in a writing process in a dot matrix mode, and the writing track can comprise various data of the intelligent pen, such as the generation time of handwriting, the pressure value of a pen point during writing, the position coordinate of the writing pen on writing paper, the acceleration value during writing and the like. By arranging the data according to time training, a time sequence, a pressure value sequence, a position coordinate sequence and an acceleration value sequence can be formed, and can be used for describing and restoring the handwriting of a user.
As an alternative, the handwriting data may be divided based on the pressure value and the acceleration value, forming a plurality of handwriting data segments.
If the writing handwriting of the intelligent pen is directly uploaded to the server for data processing, the processing speed of the data is slower due to the overlarge data quantity, so that the writing handwriting data of the intelligent pen needs to be processed.
As one way, the first pressure value threshold and the second acceleration threshold may be set first. Based on the first pressure value threshold, the pressure value sequence is divided to form a plurality of pressure value sequences, for example, a pressure value sequence greater than the first pressure value threshold can be partially divided to form one or more pressure value sequences used for representing one or more handwriting strokes actually written by the user.
After the pressure value sequences are determined, the acceleration value sequence corresponding to each pressure value sequence can be further searched, and based on the second acceleration value threshold, the acceleration value sequences are subjected to cutting operation to form a plurality of acceleration value sequences. The handwriting data of the user in the pause state can be filtered through the second acceleration threshold, so that the segmented handwriting data is further simplified. And finally, dividing the handwriting data based on a time sequence corresponding to the acceleration value sequence.
As an alternative way, 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, so that one or more handwriting matrixes corresponding to the handwriting data segments are formed.
As an alternative way, the characteristic value corresponding to the handwriting matrix can be sent to a data acquisition module in the cloud service platform, so that the data acquisition module can inquire whether the stored value similar to the characteristic value exists in the handwriting data stored in the cloud service platform; when the cloud service platform has the storage value similar to the characteristic value, directly calling the storage matrix corresponding to the storage value as the characteristic matrix corresponding to the characteristic value, and when the cloud service platform does not have the storage value similar to the characteristic value, informing an intelligent pen client side generating the characteristic data to upload the handwriting matrix to the data acquisition module.
The stored value is a writing characteristic value formed based on the writing handwriting before the user, whether the stored matrix in the cloud service platform is called or not can be determined by comparing whether the characteristic value is similar to the stored value, and the data in the handwriting matrix is directly replaced by the numerical value in the stored matrix, so that the transmission and calculation amount of the data are further reduced, and the handwriting processing efficiency is improved.
The method can further reduce the calculation amount of the data and simplify the calculation process of the data by uploading the characteristic value.
According to a specific implementation manner of the embodiment of the disclosure, the obtaining handwriting data of the smart pen includes: monitoring whether pressure data are generated by the intelligent pen; and if the handwriting data exists, collecting the 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: and dividing the pressure value sequences based on a first pressure value threshold to form a plurality of pressure value sequences. Based on the first pressure value threshold, the pressure value sequence is divided to form a plurality of pressure value sequences, for example, a pressure value sequence greater than the first pressure value threshold can be partially divided to form one or more pressure value sequences used for representing one or more handwriting strokes actually written by the user.
Searching acceleration value sequences corresponding to each pressure value sequence; and cutting the acceleration value sequences based on a second acceleration value threshold value to form a plurality of acceleration value sequences. And cutting the acceleration value sequences based on a second acceleration value threshold value to form a plurality of acceleration value sequences. The handwriting data of the user in the pause state can be filtered through the second acceleration threshold, so that the segmented handwriting data is 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.
According to a specific implementation manner of the embodiment of the present disclosure, the packaging 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 a matrix, and forming a handwriting matrix corresponding to the handwriting data segment in a time sequence.
According to a specific implementation manner of the embodiment of the present disclosure, before the characteristic values corresponding to the handwriting matrix are sent to the data acquisition module in the 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.
According to a specific implementation of an embodiment of the disclosure, the method further includes:
And carrying out graphical processing on the handwriting data obtained by the data acquisition module by utilizing a graphical module in the cloud service platform to obtain handwriting image data of the intelligent pen.
According to a specific implementation of an embodiment of the disclosure, the method further includes: aiming at the handwriting image data, character recognition is carried out by utilizing a character recognition module in a cloud service platform, so that character data corresponding to the handwriting image data is obtained; and carrying out 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, and can collect writing data of a user in a pressure, acceleration value and other modes 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 transmitted to the cloud service platform in a wireless or wired mode.
The cloud service platform is a platform which is in communication connection 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, writing data generated by the intelligent pen are processed and analyzed through the processing modules, and therefore handwriting recognition and identification of a user become more accurate and efficient.
As a way, 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, can timely adjust resource allocation according to data acquisition requirements, ensures quick response of the system, and avoids data blocking caused by quick expansion of traffic.
The data acquisition module is provided with a data storage service unit which is used for adopting the distributed data storage service in the big data architecture, supporting the high concurrency data storage service and providing support for distributed computation.
The handwriting of the user collected by the data collection module is usually stored in a mode of time, position coordinates, pressure values, acceleration values and the like, and therefore, the collected handwriting data is required to be subjected to imaging processing and restored into the actual handwriting of the user.
Therefore, various data such as time, configuration, movement, pressure and the like of the 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, finally, the image and video data are output in various output formats such as bitmaps, vector diagrams, dynamic videos and the like, and the handwriting of a fixed-line user is obtained in the form of an image.
After handwriting image data corresponding to writing are obtained, the graphical characters can be identified by utilizing a character identification module arranged in the cloud service platform, so that character data corresponding to the handwriting image are obtained.
The character recognition function of the handwriting can be set in the character recognition module, so that the data written by the user can be quickly converted into standard characters which can be recognized by a computer, for example, recognition characters of Chinese characters, letters, symbols, formulas and the like can be set.
As an alternative way, a semantic understanding function based on a natural language processing technology can be added in a character recognition module in the handwriting recognition process, the probability of character content can be calculated according to the text content of the context, and the accuracy of character recognition is improved.
After the handwriting of the user is identified as the standard character, a content analysis module arranged on the cloud service platform can be utilized to analyze the content by utilizing artificial intelligent technologies such as natural language processing, machine learning, deep learning and the like, and the services such as entity identification, relation extraction, semantic understanding, abstract extraction, keyword extraction, knowledge graph construction and the like of the character content are included.
By analyzing the content, the total judgment and analysis of the written content of the user can be performed by integrating all the context content of the handwriting of the user, and the accuracy of the written content data is further improved.
By the content and the scheme of the embodiment, the writing handwriting of the user can be processed at the cloud, so that the processing efficiency and accuracy of the writing handwriting of the intelligent pen are improved.
According to a specific implementation manner of the embodiment of the present disclosure, after the forming of the writing content data corresponding to the handwriting data, the method further includes: and carrying out characteristic analysis on the writing behaviors of the user based on the content data to form a writing characteristic font library corresponding to the user. For example, the writing behavior of the user can be extracted and analyzed to include writing characteristics of single characters, specific drawing writing characteristics, overall writing habit, writing speed and other writing characteristics, a unique character characteristic library of the specific user can be generated, handwriting identification of the user is realized, each character written by the user through an intelligent pen can identify a writer, and the method can be applied to scenes such as file signature authenticity identification, examination cheating prevention and the like.
According to a specific implementation manner of the embodiment of the present disclosure, after the forming of the writing content data corresponding to the handwriting data, the method further includes: and comparing and analyzing the target characteristics of the handwriting image data with preset target handwriting data, and determining an analysis result of the handwriting image data based on the comparison and analysis result. For example, the system can receive target characters/graphics of preset writing/drawing, collect the content written by the user, calculate the similarity between the target and the writing result by using methods such as graphic hash value comparison, cosine similarity comparison, mutual information comparison and the like, and be used for judging the similarity between the content written by the user and the target, and can be applied to the scenes such as handwriting learning, drawing learning and the like.
According to a specific implementation manner of the embodiment of the present disclosure, after the forming of the writing content data corresponding to the handwriting data, 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 by a user in the process of conducting an examination or the like, and the target data may be answer data corresponding to the examination content, and the content data and the target data may be compared to form a comparison result.
And secondly, determining a similarity value between the content data and the target data based on the content comparison result.
Through the comparison result formed by the steps, the similarity value between the content data and the target data can be determined, so that the accuracy of handwriting data solved by a user is further determined. With the content of this embodiment, it is possible to further judge whether the content written by the user is correct or not based on the writing data of the user.
According to a specific implementation manner of the embodiment of the present disclosure, after the forming of the writing content data corresponding to the handwriting data, the method further includes: and simultaneously transmitting the handwriting image data and the content data to a client so that the client can display the handwriting image data or the content data.
According to a specific implementation manner of the embodiment of the present disclosure, after the forming of the writing content data corresponding to the handwriting data, the method further includes: identifying the content data and judging whether table content data exists in the content data or not; if yes, the table content data are displayed in a table form.
By the method, the data to be displayed in the form of the table can be identified, and the part of the content is displayed in the form of the table, 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 forming of the writing content data corresponding to the handwriting data, the method further includes: and carrying out 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 pathology data of a user written by a doctor by handwriting or the like, and prescription data (recommended data) corresponding to the pathology data may be recommended by analyzing the pathology data, thereby facilitating the doctor to select a part of the recommended data according to actual needs. If so, generating recommendation data corresponding to the content data. With this embodiment, the writing efficiency of writing content data can be further improved.
Corresponding to the above embodiment, referring to fig. 5, an embodiment of the present application also discloses a medical information processing apparatus 50, including:
the acquisition module 501 is configured to acquire handwriting data formed by the smart pen in a preset writing area, where the handwriting data is used for describing 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, so as to form an analysis content corresponding to the handwriting data;
A clustering module 503, configured to perform cluster analysis on the parsed content to form a cluster analysis result;
And the determining module 504 is configured to determine, based on the result of the cluster analysis, a medical model corresponding to the pathology information and recommendation information corresponding to the medical model.
The parts of this embodiment, which are not described in detail, are referred to the content described in the above method embodiment, and are not described in detail herein.
Referring to fig. 6, an embodiment of the present disclosure also provides an electronic device 60, comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
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 which, when executed by a computer, cause the computer to perform the medical information processing method in the foregoing 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., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
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, which may perform various appropriate actions and processes according to 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 device 60 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic 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 an electronic device 60 having various means is shown, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to 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 shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this 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 the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated 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 an internet protocol address from the at least two internet protocol addresses and returns the internet protocol address; receiving an Internet protocol address returned by the node evaluation equipment; wherein the acquired internet protocol address indicates an edge node in the content distribution network.
Or 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 of the present disclosure may be written in 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring 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 foregoing is merely specific embodiments of the disclosure, but the protection scope of the 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 disclosure are intended to be covered by the protection scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.