CN114388055A - Protein cross section generation method based on brain infrared control - Google Patents
Protein cross section generation method based on brain infrared control Download PDFInfo
- Publication number
- CN114388055A CN114388055A CN202210037362.2A CN202210037362A CN114388055A CN 114388055 A CN114388055 A CN 114388055A CN 202210037362 A CN202210037362 A CN 202210037362A CN 114388055 A CN114388055 A CN 114388055A
- Authority
- CN
- China
- Prior art keywords
- brain
- protein
- infrared
- user
- dimensional space
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
- G16B15/20—Protein or domain folding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a method, a device and equipment for generating a protein section based on brain infrared control and a computer readable storage medium, wherein the method for generating the protein section based on the brain infrared control comprises the following steps: establishing a three-dimensional space in a terminal, and loading a three-dimensional image of a protein to be observed in the three-dimensional space; mapping the three-dimensional space to a display module connected with the terminal in real time; establishing a datum plane in the three-dimensional space; acquiring brain infrared signals of a user in real time, and adjusting the reference surface according to the brain infrared signals; and after receiving the confirmation instruction, generating a cross-sectional view of the protein to be observed by taking the datum plane as a reference. The method has the advantages of convenience in operation and wide applicability.
Description
Technical Field
The invention relates to the technical field of protein cross section generation, in particular to a method, a device, equipment and a computer readable storage medium for generating a protein cross section based on brain infrared control.
Background
In order to understand the microstructure of a spatial structure in which proteins are stacked spatially and disorderly from polypeptide chains, a computer three-dimensional imaging technique of proteins is widely used.
However, only the arrangement of the basic amino acid functional units is focused on, and the dense distribution and porosity of the protein are not well understood.
In order to visually display the conditions of dense distribution, porosity and the like in the protein, the conventional method is to perform a boolean reduction operation on the protein by using a virtual plane on a computer so as to obtain a corresponding sectional view. The section operation needs to select a plurality of references and set a plurality of parameters, so that the operation is complex and the requirement on the professional ability of a user is high.
Disclosure of Invention
The embodiment of the application aims to simplify the cross section generation mode of the protein by providing the protein cross section generation method based on brain infrared control.
In order to achieve the above object, an embodiment of the present application provides a method for generating a protein cross section based on brain infrared control, including:
establishing a three-dimensional space in a terminal, and loading a three-dimensional image of a protein to be observed in the three-dimensional space;
mapping the three-dimensional space to a display module connected with the terminal in real time;
establishing a datum plane in the three-dimensional space;
acquiring brain infrared signals of a user in real time, and adjusting the reference surface according to the brain infrared signals;
and after receiving the confirmation instruction, generating a cross-sectional view of the protein to be observed by taking the datum plane as a reference.
In one embodiment, adjusting the reference plane according to the brain infrared signal comprises:
identifying the type of the current brain infrared signal according to the digital classification model finished by pre-training;
confirming a preset adjusting instruction corresponding to the current brain infrared signal;
and adjusting the reference surface based on the preset adjusting instruction.
In an embodiment, before acquiring the brain infrared signals of the user, the method further comprises:
establishing a corresponding table of an adjusting instruction and a digital visual object;
displaying the digital visual object in the adjustment instruction and digital visual object corresponding table to a user;
collecting brain infrared signals when a user observes the digital visual object, and pre-classifying the collected brain infrared signals to establish a historical brain infrared database of the user;
establishing a digital classification model for classifying brain infrared signals;
and training the digital classification model based on the historical brain infrared database of the user until the training of the digital classification model is completed.
In one embodiment, prior to pre-classifying the acquired brain infrared signals, the method further comprises:
and carrying out noise reduction processing on the acquired brain infrared signals.
In one embodiment, establishing a reference plane in the three-dimensional space comprises:
acquiring the geometric center of the three-dimensional image of the protein to be observed;
and establishing a plane reference plane by taking the geometric center as the center of the reference plane and taking any reference axis of the three-dimensional space as a normal of the plane reference plane.
In an embodiment, the method further comprises:
and collecting the confirmation instruction from the brain infrared signal of the user.
In one embodiment, generating a cross-sectional view of the protein to be observed with reference to the reference plane comprises:
taking the intersecting surface of the reference surface and the three-dimensional image as a cross-sectional position, and performing Boolean subtraction on the three-dimensional image of the protein to be observed to generate a cross-sectional view of the protein to be observed;
displaying the cross-sectional view through the display module.
In order to achieve the above object, an embodiment of the present application further provides a device for generating a protein cross section based on brain infrared control, including:
the terminal is used for establishing a three-dimensional space and loading a three-dimensional image of the protein to be observed;
the display module is connected with the terminal and is used for displaying the three-dimensional space in real time;
the establishing module is used for establishing a reference plane in the three-dimensional space;
the brain infrared controller is used for acquiring brain infrared signals of a user in real time and adjusting the datum plane according to the brain infrared signals, and the brain infrared controller is further used for controlling a terminal to generate a cross-sectional view of the protein to be observed by taking the datum plane as a reference.
In order to achieve the above object, an embodiment of the present application further provides a brain infrared control-based protein section generation device, which includes a memory, a processor, and a brain infrared control-based protein section generation program stored in the memory and executable on the processor, where the processor implements the brain infrared control-based protein section generation method according to any one of the above items when executing the brain infrared control-based protein section generation program.
In order to achieve the above object, an embodiment of the present application further provides a computer-readable storage medium, where a brain infrared control-based protein section generation program is stored on the computer-readable storage medium, and when the brain infrared control-based protein section generation program is executed by a processor, the brain infrared control-based protein section generation method according to any one of the above aspects is implemented.
According to the brain infrared control-based protein section generation method, the datum plane is established in the three-dimensional space, then the brain infrared of a user is used for adjusting the datum plane in the three-dimensional space, and finally the datum plane is used as a reference for generating the section of the protein to be observed, so that any required protein section can be generated without inputting complex parameters, and the technical difficulty of obtaining the protein section is reduced; moreover, the mode of controlling the moving reference surface by brain infrared does not need a user to perform voice or motion operation (hand operation or foot operation), so that the required protein section diagram can be obtained under the condition of inconvenient voice or motion, and the applicability of the protein section is improved. Therefore, compared with the traditional mode of generating the protein sectional view by setting complex parameters, the method has the advantages of convenience in operation and wide applicability.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a block diagram of an embodiment of the apparatus for generating protein cross-section based on brain infrared control according to the present invention;
FIG. 2 is a schematic flow chart of an embodiment of a method for generating a protein cross section based on brain infrared control according to the present invention;
FIG. 3 is a schematic flow chart of an embodiment of a method for generating a protein cross section based on brain infrared control according to the present invention;
FIG. 4 is a schematic flow chart of an embodiment of a method for generating a protein cross section based on brain infrared control according to the present invention;
FIG. 5 is a block diagram of an embodiment of the apparatus for generating a protein cross section based on brain infrared control according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
For a better understanding of the above technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of "first," "second," and "third," etc. do not denote any order, and such words are to be interpreted as names.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a server 1 (also called a brain infrared control-based protein cross section generation device) in a hardware operating environment according to an embodiment of the present invention.
The server in the embodiment of the invention is equipment with a display function, such as Internet of things equipment, AR/VR equipment with a networking function, a PC, a smart phone, a tablet personal computer, a portable computer and the like.
As shown in fig. 1, the server 1 includes: memory 11, processor 12, and network interface 13.
The memory 11 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the server 1, for example a hard disk of the server 1. The memory 11 may also be an external storage device of the server 1 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the server 1.
Further, the memory 11 may also include an internal storage unit of the server 1 and also an external storage device. The memory 11 may be used not only to store application software installed in the server 1 and various types of data, such as codes of the protein section generation program 10 based on brain infrared control, etc., but also to temporarily store data that has been output or is to be output.
The network interface 13 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), typically used for establishing a communication connection between the server 1 and other electronic devices.
The network may be the internet, a cloud network, a wireless fidelity (Wi-Fi) network, a Personal Area Network (PAN), a Local Area Network (LAN), and/or a Metropolitan Area Network (MAN). Various devices in the network environment may be configured to connect to the communication network according to various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, at least one of: transmission control protocol and internet protocol (TCP/IP), User Datagram Protocol (UDP), hypertext transfer protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE, IEEE 802.11, optical fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communications, wireless Access Points (APs), device-to-device communications, cellular communication protocol, and/or bluetooth (Blue Tooth) communication protocol, or a combination thereof.
Optionally, the server may further comprise a user interface, which may include a Display (Display), an input unit such as a Keyboard (Keyboard), and an optional user interface may also include a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is used for displaying information processed in the server 1 and for displaying a visualized user interface.
Fig. 1 only shows the server 1 with the components 11-13 and the brain infrared control based protein section generation program 10, and it will be understood by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the server 1, and may comprise fewer or more components than shown, or combine certain components, or a different arrangement of components.
In this embodiment, the processor 12 may be configured to call the protein section generation program based on brain infrared control stored in the memory 11, and perform the following operations:
establishing a three-dimensional space in a terminal, and loading a three-dimensional image of a protein to be observed in the three-dimensional space;
mapping the three-dimensional space to a display module connected with the terminal in real time;
establishing a datum plane in the three-dimensional space;
acquiring brain infrared signals of a user in real time, and adjusting the reference surface according to the brain infrared signals;
and after receiving the confirmation instruction, generating a cross-sectional view of the protein to be observed by taking the datum plane as a reference.
In one embodiment, the processor 12 may be configured to call the brain infrared control-based protein section generation program stored in the memory 11, and perform the following operations:
identifying the type of the current brain infrared signal according to the digital classification model finished by pre-training;
confirming a preset adjusting instruction corresponding to the current brain infrared signal;
and adjusting the reference surface based on the preset adjusting instruction.
In one embodiment, the processor 12 may be configured to call the brain infrared control-based protein section generation program stored in the memory 11, and perform the following operations:
establishing a corresponding table of an adjusting instruction and a digital visual object;
displaying the digital visual object in the adjustment instruction and digital visual object corresponding table to a user;
collecting brain infrared signals when a user observes the digital visual object, and pre-classifying the collected brain infrared signals to establish a historical brain infrared database of the user;
establishing a digital classification model for classifying brain infrared signals;
and training the digital classification model based on the historical brain infrared database of the user until the training of the digital classification model is completed.
In one embodiment, the processor 12 may be configured to call the brain infrared control-based protein section generation program stored in the memory 11, and perform the following operations:
and carrying out noise reduction processing on the acquired brain infrared signals.
In one embodiment, the processor 12 may be configured to call the brain infrared control-based protein section generation program stored in the memory 11, and perform the following operations:
acquiring the geometric center of the three-dimensional image of the protein to be observed;
and establishing a plane reference plane by taking the geometric center as the center of the reference plane and taking any reference axis of the three-dimensional space as a normal of the plane reference plane.
In one embodiment, the processor 12 may be configured to call the brain infrared control-based protein section generation program stored in the memory 11, and perform the following operations:
and collecting the confirmation instruction from the brain infrared signal of the user.
In one embodiment, the processor 12 may be configured to call the brain infrared control-based protein section generation program stored in the memory 11, and perform the following operations:
taking the intersecting surface of the reference surface and the three-dimensional image as a cross-sectional position, and performing Boolean subtraction on the three-dimensional image of the protein to be observed to generate a cross-sectional view of the protein to be observed;
displaying the cross-sectional view through the display module.
Based on the hardware framework of the protein cross section generation equipment based on brain infrared control, the embodiment of the protein cross section generation method based on brain infrared control is provided. The invention discloses a protein cross section generation method based on brain infrared control, and aims to simplify a protein cross section generation mode.
Referring to fig. 2, fig. 2 is a diagram of an embodiment of a brain infrared control-based protein cross section generation method according to the present invention, where the brain infrared control-based protein cross section generation method includes the following steps:
s10, establishing a three-dimensional space in the terminal, and loading a three-dimensional image of the protein to be observed in the three-dimensional space.
The terminal may be a local computing device, such as a PC, a portable computer, a tablet computer, a local server, or the like, or may be a cloud computing device, such as a cloud server, or the like.
Specifically, a desired three-dimensional space can be established by enabling a specific three-dimensional program, such as UG, SolidWorks, 3Dmax, blender, etc., on the terminal. After the three-dimensional space is established, the data of the protein to be observed is loaded, and a three-dimensional image corresponding to the protein can be generated in the three-dimensional space. The three-dimensional image can fully display the three-dimensional shape of the protein to be observed.
And S20, mapping the three-dimensional space to a display module connected with the terminal in real time.
The display module may be a display screen integrated with the terminal itself, or an external display, a projector, etc. connected to the terminal.
Specifically, after the three-dimensional image of the protein to be observed is loaded into the three-dimensional space, the content of the three-dimensional space can be displayed in real time through the display module, so that a user can observe the shape and the structure of the protein to be observed in the three-dimensional space more intuitively, and the position of the reference plane in the three-dimensional space can be adjusted intuitively.
And S30, establishing a reference plane in the three-dimensional space.
Specifically, when the reference plane is established in the three-dimensional space, the method can be realized by the following steps:
and S31, acquiring the geometric center of the three-dimensional image of the protein to be observed.
The geometric center of the three-dimensional image is the geometric center of the protein to be observed. Specifically, when calculating the geometric center of the protein to be observed, the three-dimensional image may be equivalent to a regular polyhedron, such as a regular tetrahedron, a regular pentahedron, a regular hexahedron, or the like, and then the geometric center of the protein to be observed may be obtained based on the equivalent regular polyhedron.
And S32, establishing a plane reference plane by taking the geometric center as the center of the reference plane and taking any reference axis of the three-dimensional space as a normal of the plane reference plane.
Since the three-dimensional space is established based on the X-axis, the Y-axis, and the Z-axis that are perpendicular to each other, when the plane reference plane is established, any one of the three axes of the X-axis, the Y-axis, and the Z-axis may be used as a normal line of the plane.
Further, after the center and normal of the plane reference plane are established, the desired plane reference plane can be established accordingly.
Specifically, after the brain infrared control is connected to the terminal, a reference plane may be established in a three-dimensional space of the terminal based on the brain infrared control, and the reference plane may be moved in the three-dimensional space.
And S40, acquiring the brain infrared signals of the user in real time, and adjusting the reference surface according to the brain infrared signals.
In this case, spontaneous biopotentials of the cerebral cortex of the brain can be amplified from the scalp of the user by a precise instrument and recorded as a brain infrared image to obtain brain infrared signals of the user.
In particular, since the brain infrared signals may reflect brain activity of the user, the brain infrared of the user may be analyzed to determine the adjustment the user wants to make in three-dimensional space, thereby adjusting the datum in three-dimensional space in real time. Therefore, the reference plane can be adjusted without any voice or action operation (hand operation or foot operation) by the user, so that the technical requirement for adjusting the reference plane is low, the reference plane can be adjusted under the condition that the hands of the user are inconvenient (such as the condition that the hands are disabled), and the use by the user is greatly facilitated.
And S50, generating a cross-sectional view of the protein to be observed by taking the reference surface as a reference after receiving the confirmation instruction.
Specifically, when the reference plane is adjusted in the three-dimensional space, if the reference plane moves to any cross-sectional position required by a user, a confirmation instruction can be sent to the terminal, and after the terminal receives the corresponding confirmation instruction, the interface between the reference plane and the three-dimensional image can be taken as a cross section to generate a cross-sectional view of the protein to be observed.
The method for generating the protein section based on brain infrared control can be understood that the datum plane is established in the three-dimensional space, then the datum plane in the three-dimensional space is adjusted through the brain infrared of a user, and finally the section of the protein to be observed is generated by taking the datum plane as a reference, so that any required protein section can be generated without inputting complex parameters, and the technical difficulty in obtaining the protein section is reduced; moreover, the mode of controlling the moving reference surface by brain infrared does not need a user to perform voice or motion operation (hand operation or foot operation), so that the required protein section diagram can be obtained under the condition of inconvenient voice or motion, and the applicability of the protein section is improved. Therefore, compared with the traditional mode of generating the protein sectional view by setting complex parameters, the method has the advantages of convenience in operation and wide applicability.
As shown in fig. 3, in an embodiment, adjusting the reference plane according to the brain infrared signal includes:
and S110, identifying the type of the current brain infrared signal according to the pre-trained digital classification model.
Specifically, the pre-trained digital classification model is built based on a compressed and excited neural network (i.e., SENET). Through the digital classification model finished through pre-training, the currently acquired brain infrared signal diagram of the user can be classified, and then the type of the current brain infrared signal can be identified.
And S120, confirming a corresponding preset adjusting instruction of the current brain infrared signal.
Specifically, after the digital classification model identifies the type of the current brain infrared signal, the preset adjustment instruction corresponding to the current brain infrared signal can be further searched according to a preset brain infrared-adjustment instruction correspondence table, where the adjustment instruction refers to an instruction for adjusting the reference plane by the input terminal, such as rotation, movement, amplification, reduction, and the like.
And S130, adjusting the reference surface based on the preset adjusting instruction.
Specifically, after the adjustment instruction corresponding to the current brain infrared is determined, the reference plane can be adjusted accordingly according to the adjustment instruction.
It should be noted that, since the brain infrared signal of the user is collected in real time, the reference plane can be continuously adjusted according to the brain infrared signal of the user before the confirmation signal is received.
The method can be understood that the brain infrared of the user is classified and identified through the pre-trained digital classification model, so that the interference of invalid brain infrared on the adjustment of the reference surface can be reduced, and the accuracy of the adjustment of the reference surface is improved.
As shown in fig. 4, in an embodiment, before acquiring the brain infrared signal of the user, the method further includes:
s210, establishing a corresponding table of the adjusting instruction and the digital visual object.
The digital visual object is a digital object for stimulating the vision of the user to enable the user to generate corresponding brain infrared, such as arabic numerals, chinese numerals, and the like.
Specifically, the table of the adjustment instruction and the digital visual object is a table in which the adjustment instruction and the digital visual object are in one-to-one correspondence, and the table defines the adjustment instruction represented by different digital visual objects, for example, the reference surface corresponding to the palm flattening gesture is a horizontal reference surface. It should be noted that the adjustment instruction, the adjustment instruction in the digital visual object table, and the corresponding digital visual object can be adaptively adjusted according to the actual situation.
And S220, displaying the digital visual object in the adjustment instruction and digital visual object corresponding table to the user.
Specifically, after the adjustment instruction and the digital visual object corresponding table are established, the digital visual objects in the table can be displayed to the user one by one, so that the corresponding digital visual objects are generated in the mind of the user, and the impression of the user on different digital visual objects is deepened. For example, when the user observes a digital visual object of red color, the user's brain infrared at that time is apparently associated with the digital visual object of red color.
And S230, collecting brain infrared signals when the user observes the digital visual object, and pre-classifying the collected brain infrared signals to establish a historical brain infrared database of the user.
Specifically, when the user observes the corresponding digital visual object, the brain has a corresponding impression, so that the brain infrared generated by the user at the moment can be considered to be matched with the digital visual object being observed, and the currently acquired brain infrared can be classified into the corresponding digital visual object, so as to establish the required historical brain infrared database. For example, when the user observes a digital visual object of the number "1", the brain infrared acquired at this time may be classified as the brain infrared corresponding to the object of the number "1".
In addition, the targeted object stimulation can reduce the noise in the collected database and improve the quality of the database so as to improve the training precision of the model.
And S240, establishing a digital classification model for classifying the brain infrared signals.
In particular, the required digital classification model may be built based on a convolutional neural network.
And S250, training the digital classification model based on the historical brain infrared database of the user until the training of the digital classification model is completed.
Specifically, the digital classification model is trained according to the historical brain infrared database collected in a targeted manner, so that the high-precision digital classification model can be obtained, and the accuracy of adjusting the reference surface is further improved.
It can be understood that by establishing a preset adjusting instruction and digital visual object corresponding table, stimulating the vision generated by the user by the corresponding table, generating corresponding brain infrared signals in the brain of the user, establishing a brain infrared database of the user by the brain infrared signals, and then training a digital classification model, a high-precision digital classification model can be obtained, so as to improve the precision of passing through a brain infrared reference plane.
In one embodiment, prior to pre-classifying the acquired brain infrared signals, the method further comprises:
and carrying out noise reduction processing on the acquired brain infrared signals.
Specifically, the noise reduction processing is performed on the brain infrared signals, that is, the data of the brain infrared signals are filtered to filter out noise in the acquired brain infrared data. By the operation, the data quality of the acquired brain infrared data can be improved, so that a high-precision digital classification model is obtained, and the accuracy of adjusting the reference surface is improved.
In an embodiment, the method further comprises:
and collecting the confirmation instruction from the brain infrared signal of the user.
That is, after the reference plane is adjusted to the desired position, the user can imagine the digital visual object corresponding to the confirmation instruction in the mind, and further generate the brain infrared signal matched with the adjustment instruction, and the terminal can consider that the confirmation instruction is received after acquiring the corresponding brain infrared signal. It can be understood that the user can adjust the reference plane and confirm the protein section only by brain infrared, so that the user operation mode can be more uniform, and the user operation is facilitated. Of course, the design of the present application is not limited thereto, and in other embodiments, the confirmation command may come from a separate switch module, such as a separate manual switch, a foot switch, or the like.
In one embodiment, generating a cross-sectional view of the protein to be observed with reference to the reference plane comprises:
and S310, taking the intersecting surface of the reference surface and the three-dimensional image as a cross-sectional position, and performing Boolean subtraction on the three-dimensional image of the protein to be observed to generate a cross-sectional view of the protein to be observed.
Among these, the boolean reduction operation is a boul reduction operation. Specifically, when a protein cross section is generated, a cross section of the protein to be observed at the current position can be obtained by performing a boolean subtraction operation on the three-dimensional image of the protein to be observed, using the intersection of the current reference plane and the three-dimensional image of the protein to be observed as the cross section position.
And S320, displaying the section view through the display module.
Specifically, after the cross-sectional view of the protein to be observed is generated, the cross-sectional view is displayed by the display module, so that the user can observe the cut cross-sectional view of the protein in real time. The user may then determine whether to re-acquire a new protein profile based on the current protein profile.
Based on the above embodiments, the method for generating a protein cross section based on brain infrared control of the present application is exemplarily performed as follows: firstly, a user can see different numbers, data of brain infrared signals are generated through visual stimulation, the data are filtered and used for training a digital classification model (the digital classification model is used for converting real-time brain infrared signals into corresponding numbers, different numbers correspond to different control instructions (namely adjusting instructions) through codes), and after the training of the digital classification model is finished, the real-time brain infrared control can be carried out. Specifically, the encoding rule of the present invention is: the number 1 corresponds to the horizontal section operation, the number 2 represents the vertical section operation, the number 3 represents the section operation parallel to the computer screen, the number 4 represents the confirmation operation, the number 5 represents the decrease in coordinates, and the number 6 represents the increase in coordinates. When the virtual section reaches a satisfactory position, a user only needs to imagine the number 4 to generate a confirmation signal, and after the computer receives the confirmation signal, the computer performs a bol subtraction operation on the three-dimensional image to complete the generation and display of the section graph.
In addition, referring to fig. 5, an embodiment of the present invention further provides a brain infrared control-based protein cross section generation apparatus, including:
a terminal 110 for establishing a three-dimensional space and loading a three-dimensional image of a protein to be observed;
a display module 120 connected to the terminal, the display module being configured to display the three-dimensional space in real time;
an establishing module 130 for establishing a reference plane in the three-dimensional space;
and the brain infrared controller 140 is used for acquiring brain infrared signals of the user in real time and adjusting the reference surface according to the brain infrared signals, and is also used for controlling the terminal to generate a cross-sectional view of the protein to be observed by taking the reference surface as a reference.
The steps implemented by each functional module of the brain infrared control-based protein cross section generation device can refer to each embodiment of the brain infrared control-based protein cross section generation method of the present invention, and are not described herein again.
In addition, the embodiment of the present invention further provides a computer-readable storage medium, which may be any one of or any combination of a hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, and the like. The computer-readable storage medium includes a brain infrared control-based protein section generation program 10, and the specific embodiment of the computer-readable storage medium of the present invention is substantially the same as the specific embodiment of the brain infrared control-based protein section generation method and the server 1, and will not be described herein again.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for generating a protein section based on brain infrared control is characterized by comprising the following steps:
establishing a three-dimensional space in a terminal, and loading a three-dimensional image of a protein to be observed in the three-dimensional space;
mapping the three-dimensional space to a display module connected with the terminal in real time;
establishing a datum plane in the three-dimensional space;
acquiring brain infrared signals of a user in real time, and adjusting the reference surface according to the brain infrared signals;
and after receiving the confirmation instruction, generating a cross-sectional view of the protein to be observed by taking the datum plane as a reference.
2. The method for generating protein sections based on brain infrared control according to claim 1, wherein adjusting the reference plane according to the brain infrared signal comprises:
identifying the type of the current brain infrared signal according to the digital classification model finished by pre-training;
confirming a preset adjusting instruction corresponding to the current brain infrared signal;
and adjusting the reference surface based on the preset adjusting instruction.
3. The brain infrared control-based protein section generation method of claim 2, wherein before acquiring brain infrared signals of a user, the method further comprises:
establishing a corresponding table of an adjusting instruction and a digital visual object;
displaying the digital visual object in the adjustment instruction and digital visual object corresponding table to a user;
collecting brain infrared signals when a user observes the digital visual object, and pre-classifying the collected brain infrared signals to establish a historical brain infrared database of the user;
establishing a digital classification model for classifying brain infrared signals;
and training the digital classification model based on the historical brain infrared database of the user until the training of the digital classification model is completed.
4. The brain infrared control-based protein cross-section generation method of claim 3, wherein before pre-classifying the acquired brain infrared signals, the method further comprises:
and carrying out noise reduction processing on the acquired brain infrared signals.
5. The method for generating protein sections based on brain infrared control according to claim 1, wherein establishing a reference plane in the three-dimensional space comprises:
acquiring the geometric center of the three-dimensional image of the protein to be observed;
and establishing a plane reference plane by taking the geometric center as the center of the reference plane and taking any reference axis of the three-dimensional space as a normal of the plane reference plane.
6. The method for generating protein sections based on brain infrared control according to claim 1, further comprising:
and collecting the confirmation instruction from the brain infrared signal of the user.
7. The method for generating protein cross section based on brain infrared control as claimed in claim 1, wherein generating the cross section of the protein to be observed with reference to the datum plane comprises:
taking the intersecting surface of the reference surface and the three-dimensional image as a cross-sectional position, and performing Boolean subtraction on the three-dimensional image of the protein to be observed to generate a cross-sectional view of the protein to be observed;
displaying the cross-sectional view through the display module.
8. A protein section generation device based on brain infrared control is characterized by comprising:
the terminal is used for establishing a three-dimensional space and loading a three-dimensional image of the protein to be observed;
the display module is connected with the terminal and is used for displaying the three-dimensional space in real time;
the establishing module is used for establishing a reference plane in the three-dimensional space;
the brain infrared controller is used for acquiring brain infrared signals of a user in real time and adjusting the datum plane according to the brain infrared signals, and the brain infrared controller is further used for controlling a terminal to generate a cross-sectional view of the protein to be observed by taking the datum plane as a reference.
9. A brain infrared control-based protein section generation device is characterized by comprising a memory, a processor and a brain infrared control-based protein section generation program which is stored on the memory and can run on the processor, wherein the processor executes the brain infrared control-based protein section generation program to realize the brain infrared control-based protein section generation method according to any one of claims 1-7.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores thereon a brain infrared control-based protein section generation program, which when executed by a processor implements the brain infrared control-based protein section generation method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210037362.2A CN114388055B (en) | 2022-01-13 | 2022-01-13 | Protein section generation method based on brain infrared control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210037362.2A CN114388055B (en) | 2022-01-13 | 2022-01-13 | Protein section generation method based on brain infrared control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114388055A true CN114388055A (en) | 2022-04-22 |
| CN114388055B CN114388055B (en) | 2023-06-20 |
Family
ID=81202076
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210037362.2A Active CN114388055B (en) | 2022-01-13 | 2022-01-13 | Protein section generation method based on brain infrared control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114388055B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004090100A2 (en) * | 2003-04-04 | 2004-10-21 | Agilent Technologies, Inc. | Visualizing expression data on chromosomal graphic schemes |
| US20190231230A1 (en) * | 2018-01-30 | 2019-08-01 | Soochow University | Cerebral function state evaluation device based on brain hemoglobin information |
-
2022
- 2022-01-13 CN CN202210037362.2A patent/CN114388055B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004090100A2 (en) * | 2003-04-04 | 2004-10-21 | Agilent Technologies, Inc. | Visualizing expression data on chromosomal graphic schemes |
| US20190231230A1 (en) * | 2018-01-30 | 2019-08-01 | Soochow University | Cerebral function state evaluation device based on brain hemoglobin information |
Non-Patent Citations (1)
| Title |
|---|
| 王雪平 等: ""基于统计方法描述的蛋白质三维结构相似性比较"" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114388055B (en) | 2023-06-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10832039B2 (en) | Facial expression detection method, device and system, facial expression driving method, device and system, and storage medium | |
| EP3274916B1 (en) | Automated three dimensional model generation | |
| WO2016110199A1 (en) | Expression migration method, electronic device and system | |
| US20170038829A1 (en) | Social interaction for remote communication | |
| CN111369428B (en) | Virtual head portrait generation method and device | |
| CN112198959A (en) | Virtual reality interaction method, device and system | |
| JP2021536609A (en) | Gaze point estimation method and system | |
| CN113262465A (en) | Virtual reality interaction method, equipment and system | |
| CN111882674A (en) | Virtual object adjusting method and device, electronic equipment and storage medium | |
| CN109659006B (en) | Facial muscle training method and device and electronic equipment | |
| WO2016165614A1 (en) | Method for expression recognition in instant video and electronic equipment | |
| CN110478903B (en) | Control method and device for virtual camera | |
| CN114615455A (en) | Teleconference processing method, teleconference processing device, teleconference system, and storage medium | |
| CN114388055B (en) | Protein section generation method based on brain infrared control | |
| KR20230031914A (en) | Track usage of augmented reality content | |
| JP3854229B2 (en) | Image processing device | |
| CN114385010A (en) | Protein section generation method based on brain wave control | |
| CN110597397A (en) | Augmented reality implementation method, mobile terminal and storage medium | |
| CN114120448B (en) | Image processing method and device | |
| CN112785490B (en) | Image processing method and device and electronic equipment | |
| CN103916426B (en) | A kind of paxos examples update method, equipment and system | |
| CN114388056B (en) | AR-based protein section generation method | |
| CN112927330A (en) | Method and system for generating virtual human body image | |
| CN114388060B (en) | Round controller-based protein spherical section generation method | |
| CN114388059B (en) | Protein section generation method based on three-dimensional force feedback controller |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |