Eye movement data analysis method and device, readable storage medium and electronic equipment
By using an adjacency matrix to describe the degree of correlation between the problem areas in eye movement data analysis, the problem of difficulty in accurately describing user attention switching behavior in the prior art is solved, and the accuracy of the analysis results is significantly improved.
Patent Information
- Application Number
- CN202510108014.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-21
- Publication Date
- 2025-05-30
AI Technical Summary
Existing eye movement data analysis methods are difficult to accurately describe users' attention switching behavior, resulting in poor accuracy of analysis results.
By obtaining eye movement data corresponding to the target question, the connection results between different question areas are determined, and the corresponding adjacency matrix is generated to characterize the degree of correlation between each question area, and finally the analysis results are determined based on the adjacency matrix.
By introducing a structured approach of adjacency matrix, users' attention switching behaviors are accurately described, effectively improving the accuracy of analysis results.
Smart Images
Figure CN120066256A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the technical field of eye movement data analysis, and particularly relates to a method, device, computer-readable storage medium, and electronic device for eye movement data analysis. Background Art
[0002] Eye movement data analysis is a method of obtaining and analyzing the eye movement trajectories of individuals during observation through eye tracking technology, aiming to reveal the patterns and strategies in information processing and attention allocation. Eye movement data analysis can be widely applied in fields such as psychological research, human-computer interaction, market research, education and learning, and medical and health, helping researchers and professionals deeply understand human visual behavior, optimize product design, and improve user experience. However, in existing eye movement data analysis methods, it is often difficult to accurately describe the user's attention switching behavior, resulting in poor accuracy of the obtained analysis results. Summary of the Invention
[0003] In view of this, embodiments of this application provide a method, device, computer-readable storage medium, and electronic device for eye movement data analysis to solve the problem of poor accuracy of analysis results in existing eye movement data analysis methods.
[0004] The first aspect of the embodiments of this application provides a method for eye movement data analysis, which may include:
[0005] Obtain eye movement data corresponding to a target topic; wherein, the target topic includes various different topic regions;
[0006] Determine the connection result between various different topic regions according to the eye movement data;
[0007] Generate an adjacency matrix corresponding to the connection result; wherein, each element in the adjacency matrix is used to characterize the degree of association between two different topic regions;
[0008] Determine the analysis result corresponding to the eye movement data according to the adjacency matrix.
[0009] In a specific implementation manner of the first aspect, the determining the connection result between various different topic regions according to the eye movement data may include:
[0010] Integrate the eye movement data according to various different topic regions to obtain the integrated eye movement data;
[0011] Perform data point connection analysis on the integrated eye movement data to obtain the connection result between various different topic regions.
[0012] In a specific implementation manner of the first aspect, generating the adjacency matrix corresponding to the connection result may include:
[0013] Determining the connection frequency between different question areas according to the connection result;
[0014] Taking each different question area as a vertex and the connection frequency between different question areas as an edge to generate the adjacency matrix corresponding to the connection result.
[0015] In a specific implementation manner of the first aspect, determining the analysis result corresponding to the eye movement data according to the adjacency matrix may include:
[0016] Determining the target question type corresponding to the target question;
[0017] Selecting a target scoring strategy corresponding to the target question type from a preset set of dynamic scoring strategies; wherein, the set of dynamic scoring strategies includes various scoring strategies corresponding to various question types respectively;
[0018] Determining the analysis result corresponding to the eye movement data according to the target scoring strategy and the adjacency matrix.
[0019] In a specific implementation manner of the first aspect, after obtaining the eye movement data corresponding to the target question, it may further include:
[0020] Dividing the eye movement data into time segments to obtain each eye movement data time segment;
[0021] Correspondingly, determining the connection result between different question areas according to the eye movement data includes:
[0022] For each eye movement data time segment, respectively determining the connection result between different question areas.
[0023] In a specific implementation manner of the first aspect, after obtaining the eye movement data corresponding to the target question, it may further include:
[0024] Performing data cleaning and filtering on the eye movement data to obtain filtered eye movement data;
[0025] Correspondingly, determining the connection result between different question areas according to the eye movement data includes:
[0026] Determining the connection result between different question areas according to the filtered eye movement data.
[0027] In a specific implementation of the first aspect, after determining the analysis result corresponding to the eye movement data according to the adjacency matrix, it may further include:
[0028] Generate eye movement data analysis display information corresponding to the analysis result, and perform visual display on the eye movement data analysis display information.
[0029] A second aspect of the embodiments of the present application provides an eye movement data analysis device, which may include:
[0030] An eye movement data acquisition module, configured to acquire eye movement data corresponding to a target question; wherein, the target question includes each different question area;
[0031] A connection result determination module, configured to determine the connection result between each different question area according to the eye movement data;
[0032] An adjacency matrix generation module, configured to generate an adjacency matrix corresponding to the connection result; wherein, each element in the adjacency matrix is used to represent the association degree between two different question areas;
[0033] An analysis result determination module, configured to determine the analysis result corresponding to the eye movement data according to the adjacency matrix.
[0034] In a specific implementation of the second aspect, the connection result determination module may specifically be configured to: perform data integration on the eye movement data according to each different question area to obtain the integrated eye movement data; perform data point connection analysis on the integrated eye movement data to obtain the connection result between each different question area.
[0035] In a specific implementation of the second aspect, the adjacency matrix generation module may specifically be configured to: determine the connection frequency between each different question area according to the connection result; use each different question area as a vertex, and use the connection frequency between each different question area as an edge to generate the adjacency matrix corresponding to the connection result.
[0036] In a specific implementation of the second aspect, the analysis result determination module may specifically be configured to: determine the target question type corresponding to the target question; select a target scoring strategy corresponding to the target question type from a preset set of dynamic scoring strategies; wherein, the set of dynamic scoring strategies includes various scoring strategies corresponding to various question types respectively; determine the analysis result corresponding to the eye movement data according to the target scoring strategy and the adjacency matrix.
[0037] In a specific implementation of the second aspect, the eye movement data analysis device may further include:
[0038] A time period division module, configured to divide the eye movement data into time periods to obtain segmented eye movement data for each time period;
[0039] Correspondingly, the connection result determination module may specifically be configured to: for each segmented eye movement data, determine the connection result between each different question area.
[0040] In a specific implementation manner of the second aspect, the eye movement data analysis device may further include:
[0041] A data preprocessing module, configured to perform data cleaning and filtering on the eye movement data to obtain filtered eye movement data;
[0042] Correspondingly, the connection result determination module may specifically be configured to: determine the connection result between each different question area according to the filtered eye movement data.
[0043] In a specific implementation manner of the second aspect, the eye movement data analysis device may further include:
[0044] An information display module, configured to generate eye movement data analysis display information corresponding to the analysis result and perform visual display on the eye movement data analysis display information.
[0045] A third aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of any of the above eye movement data analysis methods are implemented.
[0046] A fourth aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of any of the above eye movement data analysis methods are implemented.
[0047] A fifth aspect of the embodiments of the present application provides a computer program product, and when the computer program product runs on an electronic device, the electronic device is enabled to execute the steps of any of the above eye movement data analysis methods.
[0048] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: The embodiments of the present application obtain eye movement data corresponding to a target question; wherein, the target question includes various different question areas; determine the connection result between the various different question areas according to the eye movement data; generate an adjacency matrix corresponding to the connection result; wherein, each element in the adjacency matrix is used to represent the degree of association between two different question areas; determine an analysis result corresponding to the eye movement data according to the adjacency matrix. Through the embodiments of the present application, the connection result between the various different question areas can be determined according to the eye movement data, and a corresponding adjacency matrix can be generated to determine the final analysis result. Since the structured method of the adjacency matrix is introduced to accurately describe the user's attention switching behavior, the accuracy of the analysis result can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.
[0050] Figure 1 It is a flowchart of an embodiment of a method for analyzing eye movement data in an embodiment of the present application;
[0051] Figure 2 It is an example diagram of a target question;
[0052] Figure 3 It is an example diagram of integrating eye movement data according to various different question areas;
[0053] Figure 4 It is a schematic flowchart of determining an analysis result corresponding to eye movement data according to an adjacency matrix
[0054] Figure 5 It is a structural diagram of an embodiment of an eye movement data analysis device in an embodiment of the present application;
[0055] Figure 6 It is a schematic block diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0056] To make the objectives, features, and advantages of the present application more apparent and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.
[0057] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.
[0058] It should also be understood that the terms used in this specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.
[0059] It should be further understood that the term "and / or" used in this specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.
[0060] As used in this specification and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" according to the context.
[0061] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.
[0062] Eye movement data analysis is a method of obtaining and analyzing the eye movement trajectories of individuals during observation through eye tracking technology, aiming to reveal the patterns and strategies in information processing and attention allocation. Eye movement data analysis can be widely applied in fields such as psychological research, human-computer interaction, market research, education and learning, and medical health, helping researchers and professionals deeply understand human visual behavior, optimize product design, and improve user experience. However, in existing eye movement data analysis methods, it is often difficult to accurately describe the user's attention switching behavior, resulting in poor accuracy of the obtained analysis results.
[0063] In view of this, the embodiments of the present application provide an eye movement data analysis method, device, computer-readable storage medium, and electronic device to solve the problem of poor accuracy of analysis results existing in existing eye movement data analysis methods.
[0064] In the embodiments of the present application, the connection result between different question areas can be determined based on eye movement data, and a corresponding adjacency matrix can be generated to determine the final analysis result. Since the structured method of the adjacency matrix is introduced to accurately describe the user's attention switching behavior, the accuracy of the analysis result can be effectively improved.
[0065] The execution subject of the embodiments of the present application can be an electronic device, which can include but is not limited to computing devices such as mobile phones, tablet computers, desktop computers, notebooks, handheld computers, and servers.
[0066] Please refer to Figure 1 , an embodiment of an eye movement data analysis method in the embodiments of the present application may include:
[0067] Step S101, obtain eye movement data corresponding to the target question.
[0068] In the embodiments of the present application, a preset eye movement scale can be displayed on the interaction interface of the electronic device, and the eye movement scale can include various questions for the user to answer.
[0069] For the convenience of description, any question in the eye movement scale is taken as an example below and denoted as the target question. The target question can include different question areas, for example, it can include but is not limited to the question stem area and each candidate answer area, etc. Figure 2The figure shown is an example diagram of the target question. As shown in the figure, the target question is a math calculation question, and the question stem is "7 + 9 =?". The area where it is located is the question stem area, and the corresponding candidate answers are "A. 15", "B. 16", "C. 17", and "D. 18" respectively. The areas where they are located are candidate answer area 1, candidate answer area 2, candidate answer area 3, and candidate answer area 4 respectively. It should be noted that in this example diagram, each different question area is in the shape of a rectangular frame. In actual applications, other shapes can also be used according to specific situations, and the embodiments of the present application do not make specific limitations on this.
[0070] Users can make corresponding answer selections through eye movements, that is, after the user selects the candidate answer that they think is correct, they can keep their line of sight on the area where the candidate answer is located. The electronic device can collect the user's eye movement data in real time through an eye movement interaction device such as an eye tracker. Among them, the eye movement data is a sequence composed of a group of continuous data points, and each data point can include, but is not limited to, information such as the fixation point coordinates where the user's line of sight projects on the interaction interface, the fixation duration, and the time stamp at the acquisition moment.
[0071] It should be noted that the data acquisition process and data analysis process involved in the embodiments of the present application are executed with the user's knowledge and permission, that is, the data acquisition process and data analysis process meet the relevant requirements and do not belong to acts that harm the public interest.
[0072] In a specific implementation manner of the embodiments of the present application, in order to improve the reliability of the eye movement data, after obtaining the eye movement data corresponding to the target question, the eye movement data can also be cleaned and filtered to remove noise data (such as errors caused by equipment or human factors) and outliers, so as to obtain the filtered eye movement data. Correspondingly, the eye movement data used in the subsequent process all refers to the filtered eye movement data.
[0073] Step S102: Determine the connection result between different question areas according to the eye movement data.
[0074] In the embodiments of the present application, the eye movement data can be integrated according to different question areas to obtain the integrated eye movement data. Specifically, according to the fixation point coordinates, it can be determined which question area each data point in the eye movement data falls into, so as to establish a matching relationship between the data points and the question areas, and classify and group the eye movement data according to the question areas where they are located, so as to obtain the integrated eye movement data.
[0075] Such as Figure 3As shown in the figure, it is assumed that the eye movement data includes 10 data points, which are sequentially recorded as data point 1, data point 2, data point 3, data point 4, data point 5, data point 6, data point 7, data point 8, data point 9, and data point 10. Among them, data point 1 falls into the stem area, data point 2 falls into candidate answer area 1, data points 3 and 4 fall into candidate answer area 2, data point 5 falls into candidate answer area 3, data point 6 falls into candidate answer area 4, and data points 7, 8, 9, and 10 fall into candidate answer area 2. Then, in the integrated eye movement data, the group corresponding to the stem area is data point 1, the group corresponding to candidate answer area 1 is data point 2, the group corresponding to candidate answer area 2 is data points 3, 4, 7, 8, 9, and 10, the group corresponding to candidate answer area 3 is data point 5, and the group corresponding to candidate answer area 4 is data point 6, and so on.
[0076] After obtaining the integrated eye movement data, data point connection analysis can be performed on the integrated eye movement data to obtain the connection results between different question areas. Continuing with Figure 3 the example shown in the figure, a question area switch occurs between data point 1 and data point 2, that is, from the stem area to candidate answer area 1, then a connection line between the stem area and candidate answer area 1 can be constructed; a question area switch occurs between data point 2 and data point 3, that is, from candidate answer area 1 to candidate answer area 2, then a connection line between candidate answer area 1 and candidate answer area 2 can be constructed; a question area switch occurs between data point 3 and data point 4, that is, from candidate answer area 2 to candidate answer area 3, then a connection line between candidate answer area 2 and candidate answer area 3 can be constructed; a question area switch occurs between data point 4 and data point 5, that is, from candidate answer area 2 to candidate answer area 3, then a connection line between candidate answer area 2 and candidate answer area 3 can be constructed; a question area switch occurs between data point 5 and data point 6, that is, from candidate answer area 3 to candidate answer area 4, then a connection line between candidate answer area 3 and candidate answer area 4 can be constructed; a question area switch occurs between data point 6 and data point 7, that is, from candidate answer area 4 to candidate answer area 2, then a connection line between candidate answer area 4 and candidate answer area 2 can be constructed; and so on.
[0077] Step S103: Generate an adjacency matrix corresponding to the connection result.
[0078] Among them, each element in the adjacency matrix is used to represent the degree of association between two different question areas. Specifically, the connection frequency between each different question area can be determined according to the connection result. Then, taking each different question area as a vertex and the connection frequency between each different question area as an edge, a corresponding graph (Graph) can be constructed, and a corresponding adjacency matrix (Adjacency Matrix) can be further generated.
[0079] Step S104: Determine the analysis result corresponding to the eye movement data according to the adjacency matrix.
[0080] As Figure 4 shown, step S104 may specifically include the following process:
[0081] Step S1041: Determine the target question type corresponding to the target question.
[0082] In the embodiments of the present application, the question type of each question can be preset. The specific setting method of the question type can be flexibly set according to the actual situation, and the embodiments of the present application do not make specific limitations on this. As an example, different question types such as memory, operation, perception, action, and logic can be set for each question respectively, and the corresponding setting information can be recorded. In the case where the question type needs to be determined, the question type corresponding to the target question can be determined according to the setting information and recorded as the target question type.
[0083] Step S1042: Select the target scoring strategy corresponding to the target question type from the preset set of dynamic scoring strategies.
[0084] Among them, the set of dynamic scoring strategies includes various scoring strategies corresponding to various question types respectively. The specific scoring strategy corresponding to each question type can be flexibly set according to the actual situation, and the embodiments of the present application do not make specific limitations on this. After determining the target question type corresponding to the target question, the scoring strategy corresponding to the target question type can be selected from the set of dynamic scoring strategies and recorded as the target scoring strategy.
[0085] Step S1043: Determine the analysis result corresponding to the eye movement data according to the target scoring strategy and the adjacency matrix.
[0086] As an example, the target scoring strategy can be as follows: Determine the question area with the most connection frequencies and the question area with the longest fixation duration based on the adjacency matrix. If the question area with the most connection frequencies or the question area with the longest fixation duration is the question area where the standard answer of the target question is located, the score of the target question can be determined; otherwise, it can be determined that the target question gets no score. It should be noted that the above target scoring strategy is only an example, and the actual target scoring strategy can be flexibly set according to specific situations, and the embodiments of the present application do not make specific limitations on this.
[0087] In a specific implementation manner of the embodiments of the present application, the target question may include two or more sub-questions. After obtaining the eye movement data corresponding to the target question, the eye movement data can be segmented by time period according to each sub-question to obtain each eye movement data time segment. Among them, each sub-question corresponds to an eye movement data time segment. Then, in the subsequent process, eye movement data analysis can be performed on each eye movement data time segment respectively, that is, for each eye movement data time segment, the connection results between different question areas can be determined respectively, an adjacency matrix corresponding to the connection results can be generated, and the analysis result corresponding to the eye movement data time segment can be determined according to the adjacency matrix.
[0088] It should be noted that the above process is described by taking any question in the eye movement scale as an example. For the entire eye movement scale, the above process can be traversed for each question in turn to obtain a complete analysis result.
[0089] In a specific implementation manner of the embodiments of the present application, after obtaining the analysis result, eye movement data analysis display information corresponding to the analysis result can also be generated, and the eye movement data analysis display information can be visually displayed. Among them, the eye movement data analysis display information may include, but is not limited to, information such as the area concerned by the user during the answering process, the attention distribution, and the cognitive path. Through these information, designers and researchers can be helped to deeply understand the specific situation of the user during the answering process, so as to provide data support for optimizing the user experience and adjusting the task design. By analyzing the cognitive path of the user, researchers can timely identify the cognitive bottlenecks of the user and provide guidance for subsequent task design and interaction optimization.
[0090] In summary, the embodiments of the present application obtain eye movement data corresponding to a target question; wherein, the target question includes various different question areas; determine the connection results between the various different question areas according to the eye movement data; generate an adjacency matrix corresponding to the connection results; wherein, each element in the adjacency matrix is used to represent the degree of association between two different question areas; determine the analysis result corresponding to the eye movement data according to the adjacency matrix. Through the embodiments of the present application, the connection results between the various different question areas can be determined according to the eye movement data, and the corresponding adjacency matrix can be generated to determine the final analysis result. Since the structured method of the adjacency matrix is introduced to accurately describe the user's attention switching behavior, the accuracy of the analysis result can be effectively improved.
[0091] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
[0092] Corresponding to the eye movement data analysis method described in the above embodiments, Figure 5 Fig. shows a structural diagram of an embodiment of an eye movement data analysis device provided by an embodiment of the present application.
[0093] In this embodiment, an eye movement data analysis device may include:
[0094] An eye movement data acquisition module 501, configured to acquire eye movement data corresponding to a target question; wherein, the target question includes various different question areas;
[0095] A connection result determination module 502, configured to determine the connection results between the various different question areas according to the eye movement data;
[0096] An adjacency matrix generation module 503, configured to generate an adjacency matrix corresponding to the connection results; wherein, each element in the adjacency matrix is used to represent the degree of association between two different question areas;
[0097] An analysis result determination module 504, configured to determine the analysis result corresponding to the eye movement data according to the adjacency matrix.
[0098] In a specific implementation manner of the embodiments of the present application, the connection result determination module may specifically be configured to: perform data integration on the eye movement data according to the various different question areas to obtain the integrated eye movement data; perform data point connection analysis on the integrated eye movement data to obtain the connection results between the various different question areas.
[0099] In a specific implementation manner of the embodiment of the present application, the adjacency matrix generation module may specifically be used for: determining the connection frequency between different question areas according to the connection result; generating the adjacency matrix corresponding to the connection result with each different question area as a vertex and the connection frequency between different question areas as an edge.
[0100] In a specific implementation manner of the embodiment of the present application, the analysis result determination module may specifically be used for: determining the target question type corresponding to the target question; selecting the target scoring strategy corresponding to the target question type from a preset set of dynamic scoring strategies, where the set of dynamic scoring strategies includes various scoring strategies corresponding to various question types respectively; determining the analysis result corresponding to the eye movement data according to the target scoring strategy and the adjacency matrix.
[0101] In a specific implementation manner of the embodiment of the present application, the eye movement data analysis device may further include:
[0102] A time period division module, configured to divide the eye movement data into time segments of eye movement data.
[0103] Correspondingly, the connection result determination module may specifically be used for: for each time segment of eye movement data, determining the connection result between different question areas respectively.
[0104] In a specific implementation manner of the embodiment of the present application, the eye movement data analysis device may further include:
[0105] A data preprocessing module, configured to perform data cleaning and filtering on the eye movement data to obtain filtered eye movement data.
[0106] Correspondingly, the connection result determination module may specifically be used for: determining the connection result between different question areas according to the filtered eye movement data.
[0107] In a specific implementation manner of the embodiment of the present application, the eye movement data analysis device may further include:
[0108] An information display module, configured to generate eye movement data analysis display information corresponding to the analysis result and perform visual display on the eye movement data analysis display information.
[0109] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described device, modules, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.
[0110] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.
[0111] Figure 6 FIG. shows a schematic block diagram of an electronic device provided by an embodiment of the present application. For the sake of convenience of description, only parts related to the embodiment of the present application are shown.
[0112] As Figure 6 shown, the electronic device 6 of this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60. When the processor 60 executes the computer program 62, the steps in the above embodiments of various eye movement data analysis methods are implemented, such as Figure 1 the steps S101 to S104 shown. Alternatively, when the processor 60 executes the computer program 62, the functions of each module / unit in the above device embodiments are implemented, such as Figure 5 the functions of the modules 501 to 504 shown.
[0113] Exemplarily, the computer program 62 may be divided into one or more modules / units. The one or more modules / units are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 62 in the electronic device 6.
[0114] The electronic device 6 may include, but is not limited to, computing devices such as mobile phones, tablet computers, desktop computers, notebooks, handheld computers, robots, and servers. Those skilled in the art can understand that Figure 6 merely examples of the electronic device 6 do not constitute a limitation on the electronic device 6, and it may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the electronic device 6 may further include input / output devices, network access devices, buses, etc.
[0115] The processor 60 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0116] The memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. The memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard disk equipped on the electronic device 6, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 61 may also include both the internal storage unit of the electronic device 6 and the external storage device. The memory 61 is used to store the computer program and other programs and data required by the electronic device 6. The memory 61 may also be used to temporarily store data that has been output or will be output.
[0117] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.
[0118] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0119] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.
[0120] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.
[0121] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0122] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.
[0123] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable storage medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.
[0124] The above-described embodiments are only used to illustrate the technical solutions of this application, rather than to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included in the protection scope of this application.
Claims
1. A method for analyzing eye movement data, characterized in that: include: Acquire eye movement data corresponding to a target topic; wherein the target topic includes various topic areas; Determine the connection results between different question areas according to the eye movement data; Generate an adjacency matrix corresponding to the connection result; wherein each element in the adjacency matrix is used to represent the degree of association between two different topic areas; An analysis result corresponding to the eye movement data is determined according to the adjacency matrix.
2. The eye movement data analysis method according to claim 1, characterized in that: The step of determining the connection results between different question areas according to the eye movement data includes: Integrating the eye movement data according to different question areas to obtain integrated eye movement data; The integrated eye movement data is subjected to data point connection analysis to obtain the connection results between different question areas.
3. The eye movement data analysis method according to claim 1, characterized in that: The generating an adjacency matrix corresponding to the connection result includes: Determine the connection frequency between different question areas according to the connection result; The adjacency matrix corresponding to the connection result is generated by taking the different topic areas as vertices and the connection frequencies between the different topic areas as edges.
4. The eye movement data analysis method according to claim 1, characterized in that: The determining, according to the adjacency matrix, an analysis result corresponding to the eye movement data comprises: Determining a target topic type corresponding to the target topic; Selecting a target scoring strategy corresponding to the target question type from a preset dynamic scoring strategy set; wherein the dynamic scoring strategy set includes various scoring strategies corresponding to various question types respectively; The analysis result corresponding to the eye movement data is determined according to the target scoring strategy and the adjacency matrix.
5. The eye movement data analysis method according to claim 1, characterized in that: After obtaining the eye movement data corresponding to the target question, it also includes: Dividing the eye movement data into time periods to obtain time segments of each eye movement data; Accordingly, determining the connection results between different question areas according to the eye movement data includes: For each time segment of the eye movement data, the connection results between different question areas are determined respectively.
6. The eye movement data analysis method according to claim 1, characterized in that: After obtaining the eye movement data corresponding to the target question, it also includes: Cleaning and filtering the eye movement data to obtain filtered eye movement data; Accordingly, determining the connection results between different question areas according to the eye movement data includes: The connection results between different question areas are determined according to the filtered eye movement data.
7. The eye movement data analysis method according to any one of claims 1 to 6, characterized in that: After determining the analysis result corresponding to the eye movement data according to the adjacency matrix, the method further includes: Generate eye movement data analysis display information corresponding to the analysis result, and visualize the eye movement data analysis display information.
8. An eye movement data analysis device, characterized in that: include: An eye movement data acquisition module, used to acquire eye movement data corresponding to a target topic; wherein the target topic includes various topic areas; A connection result determination module, used for determining connection results between different question areas according to the eye movement data; An adjacency matrix generation module is used to generate an adjacency matrix corresponding to the connection result; wherein each element in the adjacency matrix is used to represent the degree of association between two different question areas; An analysis result determination module is used to determine the analysis result corresponding to the eye movement data according to the adjacency matrix.
9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the eye movement data analysis method according to any one of claims 1 to 7 are implemented.
10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the eye movement data analysis method according to any one of claims 1 to 7 are implemented.