Batch processing method and device for near-infrared data, computer program product and storage medium

Abstract

The invention provides a near-infrared data batch processing method and device, a computer program product and a storage medium, a user can perform grouping import on to-be-analyzed block average results through batch import operation according to a grouping dimension set by the user, and multiple groups of to-be-analyzed block average results are obtained. In addition, data analysis processing in the groups and / or among the groups can be achieved through further batch data analysis operation, then data visualization operation is conducted on the obtained batch data analysis result, and the batch data analysis result which is collaboratively presented and the corresponding visualization content are obtained. Therefore, analysis of intra-group data and / or inter-group data can be carried out according to user requirements in the batch data and analysis process, diversified data analysis requirements of scientific research users can be met, batch data analysis results and corresponding visual contents are cooperatively presented, data visualization requirements of the scientific research users can be further met, and the scientific research user experience is improved. The operation threshold is reduced and the data processing efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of near-infrared data processing. Specifically, it relates to a method, device, computer program product, and storage medium for batch processing of near-infrared data. Background Art

[0002] With the rapid development of near-infrared brain functional imaging technology, more and more scientific researchers have started to use near-infrared brain functional imaging devices to collect data and conduct research. After a large amount of data is collected, in order to more intuitively reveal the correlation between near-infrared data and specific research directions, scientific researchers often have a strong need for data visualization of data analysis results.

[0003] Existing near-infrared data analysis software can often only achieve data visualization of the processing results of a small amount of near-infrared data. When scientific researchers want to perform data visualization processing on batch near-infrared data, they often need to use dedicated data processing software (such as MATLAB, etc.), which will increase the operation complexity and also have relatively high requirements for programming ability, with a high threshold. Summary of the Invention

[0004] In view of the above technical problems existing in the prior art, this application is proposed. This application aims to provide a method, device, computer program product, and storage medium for batch processing of near-infrared data, which can meet the diverse data analysis needs of scientific research users, and further collaboratively present the batch data analysis results and their corresponding visualization content, can further meet the data visualization needs of scientific research users, reduce the operation threshold, and improve the data processing efficiency.

[0005] In a first aspect, an embodiment of this application provides a method for batch processing of near-infrared data. The batch processing method is applied to near-infrared data analysis software. The processing method includes: obtaining target near-infrared data to be batch processed; in response to a block averaging operation on the target near-infrared data, performing block averaging processing on the target near-infrared data according to the block averaging processing method set by the user to obtain a block averaging result, and storing the block averaging result in a storage format different from that of the target near-infrared data; in response to a batch import operation initiated on the block averaging result, sequentially importing the block averaging result according to the grouping dimension set by the user to obtain multiple groups of block averaging results to be analyzed; in response to a batch data analysis operation initiated on the imported multiple groups of block averaging results to be analyzed, performing intra-group and / or inter-group data analysis processing on each group of block averaging results to be analyzed according to the selected batch data analysis type by the user to obtain batch data analysis results corresponding to the multiple groups of block averaging results to be analyzed; in response to a data visualization operation on the batch data analysis results, collaboratively presenting the batch data analysis results and their corresponding visualization content on a data visualization interface.

[0006] In a second aspect, an embodiment of the present application further provides a batch processing device for near-infrared data. The batch processing device includes a processor, and the processor executes the following steps by running near-infrared data analysis software: obtaining target near-infrared data to be batch processed; in response to a block averaging operation on the target near-infrared data, performing block averaging processing on the target near-infrared data according to the block averaging processing method set by the user to obtain a block averaging result, and storing the block averaging result in a storage manner different from that of the target near-infrared data; in response to a batch import operation initiated for the block averaging result, sequentially importing the block averaging result according to the grouping dimension set by the user to obtain multiple groups of block averaging results to be analyzed; in response to a batch data analysis operation initiated for the imported multiple groups of block averaging results to be analyzed, performing intra-group and / or inter-group data analysis processing on each group of block averaging results to be analyzed according to the batch data analysis type selected by the user to obtain batch data analysis results corresponding to the multiple groups of block averaging results to be analyzed; in response to a data visualization operation on the batch data analysis results, collaboratively presenting the batch data analysis results and their corresponding visualization content on a data visualization interface.

[0007] According to a third aspect of the present application, there is provided a computer program product, including computer program instructions which are used to execute the steps of the method for batch processing of near-infrared data as described in various embodiments of the present application when running.

[0008] According to a fourth aspect of the present application, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor is caused to execute the steps of the method for batch processing of near-infrared data as described in various embodiments of the present application. The batch processing method includes: obtaining target near-infrared data to be batch processed; in response to a block averaging operation on the target near-infrared data, performing block averaging processing on the target near-infrared data according to the block averaging processing method set by the user to obtain a block averaging result, and storing the block averaging result in a storage manner different from that of the target near-infrared data; in response to a batch import operation initiated for the block averaging result, sequentially importing the block averaging result according to the grouping dimension set by the user to obtain multiple groups of block averaging results to be analyzed; in response to a batch data analysis operation initiated for the imported multiple groups of block averaging results to be analyzed, performing intra-group and / or inter-group data analysis processing on each group of block averaging results to be analyzed according to the batch data analysis type selected by the user to obtain batch data analysis results corresponding to the multiple groups of block averaging results to be analyzed; in response to a data visualization operation on the batch data analysis results, collaboratively presenting the batch data analysis results and their corresponding visualization content on a data visualization interface.

[0009] Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows: For the batch processing method, device, computer program product and storage medium of near-infrared data provided by the embodiments of the present application, a user can perform block averaging processing on target near-infrared data to be batch processed in near-infrared data analysis software, and the block averaging result can be stored in a storage format different from that of the target near-infrared data, which is convenient for the user to distinguish whether the near-infrared data has been processed. It can also ensure the data integrity and stability of the unprocessed target near-infrared data, and at the same time store the results obtained during the calculation process in a timely manner, so as to facilitate meeting the user's requirements for reproducing the results of each processing stage and more diverse data processing requirements. Further, the block averaging results to be analyzed can be grouped and imported according to the grouping dimension set by the user through a batch import operation to obtain multiple groups of block averaging results to be analyzed, and through further batch data analysis operations, data analysis and processing within and / or between groups can be realized. Then, data visualization operations can be performed on the obtained batch data analysis results to obtain batch data analysis results presented collaboratively and their corresponding visualization content. In this way, the user can quickly implement batch analysis operations on near-infrared data in the near-infrared data analysis software, and can analyze intra-group data and / or inter-group data according to user requirements during batch data and the analysis process, which can meet the diverse data analysis requirements of scientific research users. Further, presenting the batch data analysis results and their corresponding visualization content collaboratively can further meet the data visualization requirements of scientific research users, reduce the operation threshold, and improve the data processing efficiency.

[0010] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above description and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are given below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. The drawings here are incorporated into the specification and constitute a part of this specification. These drawings show embodiments that conform to the present application and are used together with the specification to illustrate the technical solutions of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 Shows a flowchart of a batch processing method for near-infrared data provided by an embodiment of the present application; Figure 2Shows the schematic diagram of the block averaging processing interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 3 Shows the schematic diagram of the block averaging processing configuration interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 4 Shows the schematic diagram of the block averaging preview interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 5 Shows the schematic diagram of the data batch import interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 6 Shows the schematic diagram of the file preview interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 7 Shows the schematic diagram of the group statistics operation interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 8 Shows the schematic diagram of the inter-group data analysis operation interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 9 Shows the schematic diagram of the custom grouping editing interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 10 Shows the schematic diagram of the data visualization interface including the activated topographic map in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 11 Shows the schematic diagram of the data visualization interface including the activated waveform graph in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 12 Shows the schematic diagram of the activated waveform parameter configuration interface in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 13 Shows the schematic diagram of the data visualization interface including the standard deviation visualization element in the batch processing method of near-infrared data provided by the embodiments of the present application; Figure 14 Shows the schematic diagram of the data visualization interface including the brain visualization content and the data chart visualization content in the batch processing method of near-infrared data provided by the embodiments of the present application. Detailed implementation manners

[0013] To enable those skilled in the art to better understand the technical solutions of this application, the following provides a detailed description of this application in conjunction with the accompanying drawings and specific implementation manners. The following further describes the embodiments of this application in detail with reference to the accompanying drawings and specific examples, but it does not limit this application.

[0014] The "first", "second" and similar terms used in this application do not indicate any order, quantity or importance, but are only used for distinction. The terms such as "including" or "comprising" used in this application mean that the elements before this word cover the elements listed after this word, and do not exclude the possibility of also covering other elements. In this application, the arrows shown in the figures for each step are only examples of the execution order, rather than limitations. The technical solutions of this application are not limited to the execution order described in the embodiments. The steps in the execution order can be combined, decomposed, or the order can be swapped, as long as the logical relationship of the execution content is not affected.

[0015] All terms used in this application (including technical terms or scientific terms) have the same meaning as understood by those of ordinary skill in the art to which this application belongs, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense, unless specifically defined as such here. Technologies and devices known to those of ordinary skill in the relevant fields may not be discussed in detail, but where appropriate, such technologies and devices should be regarded as part of the specification.

[0016] In this application, the arrows shown in the figures for each step are only examples of the execution order, rather than limitations. The technical solutions of this application are not limited to the execution order described in the embodiments. The steps in the execution order can be combined, decomposed, or the order can be swapped, as long as the logical relationship of the execution content is not affected.

[0017] The batch processing method for near-infrared data provided by the embodiments of this application is executed by running near-infrared data analysis software. The near-infrared data analysis software in this application can be used to perform operations such as data preprocessing, block averaging, within-group data analysis, between-group data analysis, graph theory calculation processing, etc. on near-infrared data in batches.

[0018] The above-mentioned near-infrared data analysis software deployment can be deployed on a computer device with a certain computing power. Such a computer device may include, for example: a terminal device, a server, or other processing devices. The terminal device can be an intelligent terminal device with a display function. For example, it can be a smart phone, a tablet computer, a smart wearable device, etc. In some possible implementation manners. In some possible implementation manners, the method for batch processing of near-infrared data can be implemented by a processor calling computer-readable instructions stored in a memory.

[0019] See Figure 1 As shown, it is a flowchart of the method for batch processing of near-infrared data provided by an embodiment of the present application. The batch processing method includes S101 to S105, where: S101: Obtain target near-infrared data to be batch processed.

[0020] S102: In response to a block averaging operation on the target near-infrared data, perform block averaging processing on the target near-infrared data according to the block averaging processing method set by the user to obtain a block averaging result, and store the block averaging result in a storage format different from that of the target near-infrared data.

[0021] S103: In response to a batch import operation initiated on the block averaging result, sequentially import the block averaging result according to the grouping dimension set by the user to obtain multiple groups of block averaging results to be analyzed.

[0022] S104: In response to a batch data analysis operation initiated on the imported multiple groups of block averaging results to be analyzed, perform intra-group and / or inter-group data analysis processing on each group of block averaging results to be analyzed according to the batch data analysis type selected by the user to obtain batch data analysis results corresponding to the multiple groups of block averaging results to be analyzed.

[0023] S105: In response to a data visualization operation on the batch data analysis results, cooperatively present the batch data analysis results and their corresponding visualization content on a data visualization interface.

[0024] The following is a detailed introduction to the above steps.

[0025] For S101 and S102, the near-infrared data acquisition device can be controlled by near-infrared data acquisition software to collect near-infrared data from the subject, and the near-infrared data can be obtained. One instance of near-infrared data can be obtained by performing one near-infrared data collection on the subject. After the near-infrared data is obtained, the collected near-infrared data can be stored. The near-infrared data can be stored in the first storage format, and the first storage format is a storage format that can be read by the near-infrared data analysis software. For example, it can be in the.hcx format or other custom file formats. The target near-infrared data can be understood as multiple instances of near-infrared data selected by the user to be batch-processed. The block averaging operation can be an operation initiated by the user on the block averaging processing interface. The block averaging result can be stored in the second storage format, and the second storage format is a storage format supported by the operating environment used by the near-infrared data analysis software. Taking the operating environment used by the near-infrared data analysis software as MATLAB Compiler Runtime (MCR) as an example, the second storage format can be in the.mat format, etc.

[0026] Exemplarily, the schematic diagram of the block averaging processing interface can be as Figure 2 shown Figure 2 In, the block averaging processing interface includes a file import area and a block processing parameter setting area. Among them, the file import area is located on the left side of the block averaging processing interface. The user can import the target near-infrared data to be batch-processed in the file import area through corresponding triggering operations. For example, after clicking "Load File", a file containing the target near-infrared data can be imported. The block processing parameter setting area is located on the right side of the block averaging processing interface. The user can set the block averaging processing method and the preprocessing method in the block processing parameter setting area.

[0027] For example, the configurable parameters corresponding to the preprocessing method include high-pass filtering parameters and low-pass filtering parameters. The block averaging processing method can be defined by a configuration file, that is, the user can set the block averaging processing method by importing a configuration file containing block averaging configuration parameters.

[0028] In some other embodiments, the block averaging processing interface can include parameter setting items for the block averaging processing method. The user can directly set the specific parameters of the block averaging processing method in the block averaging processing interface. The specific interface layout of the block averaging processing interface in the embodiments of the present application is not limited.

[0029] In some embodiments, when the user sets the block averaging processing method, it can be achieved by importing block averaging processing configuration parameters, etc. The block averaging processing configuration parameters can be defined by a corresponding configuration file. The user can trigger Figure 2The "Import Configuration File" button in imports the block averaging processing configuration parameters.

[0030] For example, the block averaging processing configuration parameters can be configured on the block averaging processing configuration interface. Among them, the block averaging processing configuration interface can be presented after the user triggers the "Import Configuration File" button, or it can also be presented after the user triggers other buttons related to the "Block Averaging Processing Configuration Interface" in the near-infrared data analysis software. The embodiments of the present application do not limit the triggering mechanism of the block averaging processing configuration interface.

[0031] Exemplarily, the schematic diagram of the block averaging processing configuration interface can be as Figure 3 shown, Figure 3 In, the block averaging processing configuration interface includes a processing condition selection area and a processing condition configuration area. Among them, the processing condition selection area is located on the left side of the block averaging processing configuration interface, and the user can select the corresponding block averaging processing method through corresponding triggering operations in the processing condition selection area. The processing condition configuration area is located on the right side of the block averaging processing configuration interface, and the user can set the specific parameters in the block averaging processing method in the processing condition configuration area.

[0032] Among them, the processing condition selection area includes "Add", "Delete", "Modify", and "Preview" buttons. The user can perform corresponding operations on Figure 3 conditions 1 to 3 in by triggering the corresponding buttons. For example, condition 3 can be deleted by the "Delete" button. After the user selects condition 1, the corresponding editable content of condition 1 will be presented in the processing condition configuration area. The editable content includes the name of the processing condition, the start and end of block averaging, the start and end of the front baseline, and the start and end of the back baseline.

[0033] Further, after the user sets the block averaging processing method, a block averaging preview interface can also be presented in response to the user's preview operation on the set block averaging processing method for the user to preview. For example, when the user triggers the preview button in the block averaging processing configuration interface, the block averaging preview interface can be presented accordingly; or, before the user sets the block averaging processing method, a block averaging preview interface can be presented in response to the user's initiated preview operation for the user to preview. For example, when the user triggers the preview button in the block averaging interface, the block averaging preview interface can be presented accordingly.

[0034] Exemplarily, the schematic diagram of the block averaging preview interface can be as Figure 4 shown, Figure 4 In, the block averaging preview interface includes a waveform data preview area, a channel data preview area, a preview content setting area, and a block averaging processing method preview area.

[0035] Among them, the preview area of the block averaging processing method is located at the lower right side of the block averaging preview interface, which is used to present the selectable block averaging processing methods ( Figure 4 including Condition 1 to Condition 3). The waveform data preview area is located on the left side of the block averaging preview interface, which is used to present preview content according to the selected block averaging processing method and the imported near-infrared data, so as to at least indicate a part of the near-infrared data to be processed by the currently selected block averaging processing method (the block averaging processing method corresponding to Condition 1) (such as Figure 4 shown by the red rectangular area in the figure). The channel data preview area is located at the upper right side of the block averaging preview interface, which is used to indicate the connection status (or signal strength) of each acquisition channel. The preview content setting area is located on the right side of the block averaging preview interface. For example, the editable content it contains may include hemoglobin type and plotting method. Users can update the preview content presented in the waveform data preview area and / or the channel data preview area through editing operations in the preview content setting area.

[0036] It should be noted that the interface layout of the above block averaging preview interface is only an exemplary layout. Developers or users can change the interface layout of the block averaging preview interface according to usage requirements. The embodiments of the present application do not limit the interface layout of the block averaging preview interface.

[0037] For S103, the grouping dimension can be understood as an attribute of the subject, such as gender, age, treatment method received, etc.

[0038] In some embodiments, the batch import operation initiated for the block averaging result may include a batch import operation initiated for the target storage file storing the block averaging result.

[0039] Among them, the storage format of the target storage file can be, for example, in the format of.mat, etc.; one target storage file can contain the calculation results obtained during the processing of near-infrared data by near-infrared data analysis software and their associated calculation parameters. That is, for any imported near-infrared data, its original near-infrared data can still be stored in the first storage format (i.e., only read the near-infrared data without modifying its content), and the calculation results and their associated calculation parameters after analysis and processing in the near-infrared data analysis software can be independently stored in the target storage file. Therefore, the near-infrared data analysis software can centrally store the calculation results and their associated calculation parameters obtained during the analysis process without changing the original data content, and the files at each stage (before processing, parameters during processing, processing results) can be retained locally on the device, facilitating the satisfaction of result reproduction requirements and more diverse processing requirements of users. For example, if a user wants to use the processed results for further custom analysis to meet their specific scientific research needs, they can select the files at the corresponding stage for subsequent processing according to their needs. Compared with the end-to-end (i.e., not retaining intermediate files) data processing method, the processing method of this embodiment can better meet the diverse data usage needs of scientific research users and reserve more possibilities for personalized data processing.

[0040] Exemplarily, taking the storage of near-infrared data in the.hcx format and the storage of calculation results and their associated calculation parameters in the.mat format as an example, after the near-infrared data analysis software reads the near-infrared data in the hcx format, it can perform operations such as data preprocessing, block averaging processing, within-group data analysis processing, between-group data analysis processing, and graph theory calculation processing in response to user operations in the near-infrared data analysis software. The processing results and their associated calculation parameters can be stored in the newly generated target storage file in the.mat format, thereby realizing the independent storage of the original data and the processing results.

[0041] In some embodiments, in response to a batch import operation for data in the target grouping dimension, according to the grouping labels and target data paths set by the user in the target grouping dimension, the target storage files located under the target data paths can be read to obtain a set of block averaging results to be analyzed.

[0042] Among them, the batch import operation of data can be an operation initiated by the user in the data batch import interface. For example, under the grouping dimension of "gender", grouping labels of "male" and "female" are set, and the corresponding target data paths are set respectively to read the target storage files under the target data paths.

[0043] Exemplarily, the schematic diagram of the data batch import interface can be as Figure 5 shown, Figure 5Among them, the data batch import interface includes a grouping label setting area and a file import area. Among them, the grouping label setting area is located on the left side of the data batch import interface, and is used to edit the grouping dimension (age) and the grouping labels it contains (corresponding to "10", "20", "30" in the figure). The file import area is located on the right side of the data batch import interface. After the user selects the age "10", the file import area can present the editable content corresponding to the age "10", which can be used to respond to the user's setting operation, read the target storage file under the target data path according to the target data path corresponding to the grouping label "10" set by the user, and after reading the target storage file, the file identifier of the imported target storage file can be presented in the file import area to indicate the completion of the import operation of the target storage file.

[0044] Among them, when the user wants to add a grouping dimension or a grouping label, it can be achieved through corresponding addition operations. The addition operation can be, for example, an operation such as triggering the "Add" button. When the user sets the target data path, it can be set on the file path setting interface presented after triggering the "Import" button. The embodiments of the present application do not limit the layout and operation form in the file path setting interface.

[0045] In some other embodiments, the batch import operation can also be automatically imported according to the grouping dimension set by the user, in addition to the above-mentioned method of reading the target storage file according to the target data path set by the user.

[0046] Among them, the attribute information of the subject, such as age, gender, etc., can be stored in association with the storage file of the target near-infrared data. Therefore, after the user sets the grouping dimension, the target near-infrared data can be screened according to the grouping dimension to obtain multiple groups of average results of the blocks to be analyzed, so as to achieve the effect of automatic grouping.

[0047] In some embodiments, a file preview interface can be presented in response to a preview operation on the imported target storage file.

[0048] Among them, the preview operation can be, for example, a triggering operation on a preview button ( Figure 5 not shown in the figure and can be added according to requirements). The embodiments of the present application do not limit the triggering mechanism of the file preview interface. The file preview interface includes preview items corresponding to each imported target storage file, and the preview items include the grouping labels of each target storage file under each grouping dimension.

[0049] In some embodiments, the file preview interface may further include at least one data check result, which is used to characterize whether the target storage file contains predetermined content or has undergone predetermined processing. For example, it may be whether it contains block average results, whether it has undergone preprocessing, etc. In this way, by setting up a data check and presentation mechanism, it can visually present to the user whether the target storage file they import can be used for subsequent analysis and processing, avoiding the problem that the target storage file imported by the user cannot be used in subsequent data processes due to not containing predetermined content or not having undergone predetermined processing, and improving the processing efficiency and the accuracy of processing results when the user batch processes data.

[0050] Exemplarily, the schematic diagram of the file preview interface may be as Figure 6 shown. Figure 6 In, the file preview interface contains 14 imported target storage files, namely files 1 to 14. The preview items include the grouping labels of each target storage file under the grouping dimensions of "age" and "gender", and the data check results corresponding to data check items 1 to 4 respectively.

[0051] For S104, the within-group data analysis and processing may be processing methods such as within-group averaging, and the between-group data analysis and processing may be processing methods such as between-group one-sample T-test, independent-sample T-test, paired-sample T-test, etc.; the between-group data analysis and processing includes data analysis and processing for any two of the to-be-analyzed block average results in each group of to-be-analyzed block average results; the batch data analysis operation may be an operation initiated by the user on the group statistics operation interface.

[0052] Exemplarily, the schematic diagram of the group statistics operation interface may be as Figure 7 shown. Figure 7 In, the group statistics operation interface from top to bottom is successively a data viewing area, a between-group data processing area, and a within-group data processing area. Among them, the data viewing area can be used to view data characteristics such as the normality and homogeneity of variance of near-infrared data. The between-group data processing area contains various between-group data analysis and processing methods, specifically including one-sample T-test, independent-sample T-test, and paired-sample T-test. The within-group data processing area contains the batch data analysis types corresponding to the within-group data analysis and processing methods, specifically including activated topographic maps and waveform diagrams. The between-group data analysis and processing methods and the within-group data analysis and processing methods will be illustrated with examples in conjunction with the accompanying drawings below.

[0053] In some embodiments, in response to a between-group data analysis operation initiated for each group of to-be-analyzed block average results, data analysis and processing are performed on any two of the to-be-analyzed block average results in each group of to-be-analyzed block average results according to the selected batch data analysis type by the user, to obtain the batch data analysis results corresponding to the multiple groups of to-be-analyzed block average results.

[0054] Among them, the inter-group data analysis operation may be an operation initiated by a user on an inter-group data analysis operation interface. The inter-group analysis operation interface may be presented, for example, after the user triggers any selectable option (which may be a button) presented in the inter-group data processing area. For example, when the user triggers the "independent samples T-test" button, the inter-group data analysis operation interface corresponding to the independent samples T-test may be presented. Figure 7 Exemplarily, a schematic diagram of the inter-group data analysis operation interface may be as shown in

[0055] As shown in Figure 8 In the inter-group data analysis operation interface, there are a group list area, a file list area, and a parameter configuration area. The group list area is located in the upper left side of the inter-group data analysis operation interface and is used to sequentially import the average results of multiple groups of blocks to be analyzed according to the grouping dimension set by the user (specifically, refer to the relevant introduction in S103 above). The file list area is located in the upper right side of the inter-group data analysis operation interface and is used to read the target storage file according to the data path set by the user (specifically, refer to the relevant introduction above). The parameter configuration area is located in the lower side of the inter-group data analysis operation interface and is used to perform inter-group data analysis processing according to the batch data analysis type set by the user, specifically including data analysis processing of the average results of any two groups of blocks to be analyzed according to the selected multiple comparison option. The embodiment of the present application does not limit the specific data analysis processing algorithm used, as long as it can be realized. Figure 8 It should be noted that the interface layout of the above inter-group data analysis operation interface is only an exemplary layout. Developers or users can change the interface layout of the inter-group data analysis operation interface according to usage requirements. The embodiment of the present application does not limit the interface layout of the inter-group data analysis operation interface.

[0056] Regarding S105, the data visualization operation may be, for example, an operation initiated for buttons such as "activation topographic map" and "waveform diagram" in

[0057] In some embodiments, the visualization content may also be presented according to the following steps A1~A2: Figure 7 A1: In response to a data clustering operation initiated for multiple said grouping dimensions, generate a custom grouping according to at least one grouping label selected by the user under each grouping dimension.

[0058] A2: Perform data analysis processing according to the batch data analysis type selected by the user and the custom grouping, and collaboratively present the data analysis results and their corresponding visualization content on the data visualization interface. A1: In response to a data clustering operation initiated for multiple said grouping dimensions, generate a custom grouping according to at least one grouping label selected by the user under each grouping dimension.

[0059] A2: Perform data analysis processing according to the batch data analysis type selected by the user and the custom grouping, and collaboratively present the data analysis results and their corresponding visualization content on the data visualization interface.

[0060] Among them, the data clustering operation can be an operation initiated on a custom grouping editing interface, and the custom grouping editing interface can be presented in response to an editing operation initiated by the user for a grouping dimension (or grouping label). For example, the user can initiate an editing operation for the grouping dimension (or grouping label) included in Figure 5 or Figure 8 to trigger the custom grouping editing interface.

[0061] Exemplarily, taking the grouping dimensions of "gender" and "age" as an example, the schematic diagram of the custom grouping editing interface can be as shown in Figure 9 shown, Figure 9 Among them, there are 2 grouping labels of "male" and "female" under the grouping dimension of "gender", and 3 grouping labels of "10", "20", and "30" under the grouping dimension of "age". The user can select at least one grouping label under each grouping dimension. For example, select "male", "female", and "10" to form a custom grouping.

[0062] In this way, by setting the custom grouping editing interface and the corresponding interaction mechanism, the user can conveniently select the grouping labels under each grouping dimension to form a custom grouping, and then the user can view the data visualization content corresponding to the near-infrared data of each custom grouping, which can meet the diverse and refined data visualization needs of scientific research users.

[0063] In some embodiments, when the data visualization type selected by the user is an activation topographic map, multiple first data selection items can be presented in the data display area of the data visualization interface, and at least the activation topographic map can be presented in the visualization content display area.

[0064] Among them, the first data selection items include batch data analysis result selection items, and the activation topographic map is presented in coordination with the target batch data analysis result selected by the batch data analysis result selection items. The user can set the first data selection items according to needs to obtain the desired visualization content, and then export the desired visualization content through the corresponding export operation.

[0065] Exemplarily, the schematic diagram of the data visualization interface including the activation topographic map can be as shown in Figure 10 shown, Figure 10 Among them, the data display area of the data visualization interface includes multiple first data selection items, and the first data selection items include data type, hemoglobin type, condition, group, spectral pattern, etc. In the visualization content display area of the data visualization interface, an activation waveform diagram (located at the lower left side of the visualization content display area), a schematic diagram of the acquisition channel (located at the upper left side of the visualization content display area), and an activation topographic map (located on the right side of the visualization content display area) are presented in coordination.

[0066] In this way, by collaboratively presenting each first data selection item and the activation waveform diagram, the acquisition channel schematic diagram, and the activation topographic map in the data visualization interface, users can view various data visualization contents and make real-time adjustments according to their needs, meeting the diverse data visualization needs of scientific research users.

[0067] In some embodiments, when the selected data visualization type by the user is the activation waveform diagram, an activation waveform diagram containing multiple waveform data and a legend can be presented in the data visualization interface.

[0068] Among them, the average results of each group of blocks to be analyzed respectively correspond to one waveform data, and different waveform data have different display attributes. The legend is used to indicate the association relationship between the display attributes and the batch data analysis results, and the display attributes can be colors, sizes, etc.

[0069] Exemplarily, a schematic diagram of the data visualization interface containing the activation waveform diagram can be as Figure 11 shown. Figure 11 In it, the data visualization interface contains 5 waveform data, namely "(Age: 1) & (Gender: female, male)", "(Age: 2) & (Gender: female, male)", "(Age: 3) & (Gender: female, male)", "(Age: 1, 2, 3) & (Gender: female)", "(Age: 1, 2, 3) & (Gender: male)". These 5 waveform data have different colors, and the association relationship between the colors of the waveform data and the batch data analysis results is presented in the upper right side of the activation waveform diagram in the form of a legend.

[0070] In some embodiments, the within-group data analysis and processing includes within-group data averaging processing, and the data visualization interface can also be presented through the following steps B1 to B2: B1: In response to the standard deviation display operation for the activation waveform diagram, determine the standard deviation of the average results of each group of blocks to be analyzed during within-group data averaging processing.

[0071] Here, the standard deviation display operation can be initiated by the user on the activation waveform parameter configuration interface, which contains various configurable parameters. The activation waveform parameter configuration interface can be presented, for example, after triggering the parameter configuration button in the data visualization interface.

[0072] Exemplarily, a schematic diagram of the activation waveform parameter configuration interface can be as Figure 12 shown. Figure 12Among them, the configurable parameters included in the activation waveform parameter configuration interface include blood oxygen type, data type, current display group, etc. Users can configure the configurable parameters through corresponding configuration operations. Among them, the current display group can be increased / decreased accordingly according to user settings. After the user triggers the corresponding add button, the custom grouping editing interface as shown in Figure 9 will be presented, so that users can edit the custom grouping to be newly added according to their needs.

[0073] B2: According to the standard deviations respectively corresponding to the average results of the blocks to be analyzed in each group, present multiple waveform data and their corresponding standard deviation visualization elements in the data visualization interface in a coordinated manner; among them, the standard deviation visualization elements are presented with a significance weaker than that of the waveform data.

[0074] Among them, the standard deviation visualization elements are presented with a significance weaker than that of the waveform data, which can be understood as that the display effect of the waveform data is more prominent than that of the standard deviation visualization elements. For example, the color of the waveform data when displayed is deeper, and the area ratio when displayed is larger, etc.

[0075] Exemplarily, a schematic diagram of the data visualization interface including the standard deviation visualization elements can be as shown in Figure 13 as follows Figure 13 In it, the data visualization interface includes 5 waveform data presented in a coordinated manner and their corresponding standard deviation visualization elements respectively. The display color of the waveform data when displayed is deeper than the display color of the standard deviation visualization elements, so that on the basis of presenting the waveform data and its standard deviation visualization elements in a coordinated manner, the waveform data can be prominently presented.

[0076] In this way, by presenting the standard deviation visualization elements corresponding to the waveform data in the data visualization interface in a manner with a significance weaker than that of the waveform data, the waveform data and its associated standard deviation can be presented in a visual manner at the same time in the data visualization interface, and the presentation effect of the standard deviation will not reduce the presentation effect of the waveform data. It can take into account the visualization needs of both the waveform data and its associated standard deviation at the same time, and can meet the diverse and refined data visualization needs of scientific research users.

[0077] In some embodiments, in response to a data visualization operation for the batch data analysis results, multiple second data selection items can be presented in the data display area of the data visualization interface, and at least the brain visualization content and the data chart visualization content can be presented in a coordinated manner in the visualization content display area; among them, the second data selection items include a block average result group selection item for indicating data visualization, and the block average result group selection item includes options composed of any two groups of the average results of the blocks to be analyzed.

[0078] Here, the brain visualization content can be visualization content associated with a brain model such as an activation topographic map, and the data chart visualization content can be a statistical chart obtained by statistically analyzing the block average results to be analyzed.

[0079] Exemplarily, a schematic diagram of a data visualization interface including brain visualization content and data chart visualization content can be as Figure 14 shown, Figure 14 In it, the data display area of the data visualization interface contains multiple second data selection items, and the second data selection items include data types, statistical types, block average result group selection items, atlas modes, etc. In the visualization content display area of the data visualization interface, a schematic diagram of the acquisition channels (located in the upper left side of the visualization content display area), an activation topographic map (located on the right side of the visualization content display area), and a statistical chart (located in the lower left side of the visualization content display area) that match the block average result group selection item are presented in coordination.

[0080] In this way, by presenting each second data selection item, the statistical chart, the schematic diagram of the acquisition channels, and the activation topographic map in coordination in the data visualization interface, users can view various data visualization contents corresponding to any two sets of near-infrared data and can make real-time adjustments according to their needs, which can meet the diverse and refined data visualization needs of scientific research users.

[0081] The batch processing method, device, computer program product and storage medium for near-infrared data provided by the embodiments of the present application enable users to perform block averaging on target near-infrared data to be batch processed in near-infrared data analysis software, and the block averaging results can be stored in a storage format different from that of the target near-infrared data, facilitating users to distinguish whether the near-infrared data has been processed. It can also ensure the data integrity and stability of the unprocessed target near-infrared data, and at the same time store the results obtained during the calculation process in a timely manner, facilitating the reproduction requirements of users for the results of each processing stage and more diverse data processing requirements. Further, the block averaging results to be analyzed can be grouped and imported according to the grouping dimension set by the user through a batch import operation to obtain multiple groups of block averaging results to be analyzed, and through further batch data analysis operations, data analysis and processing within and / or between groups can be realized. Then, data visualization operations can be performed on the obtained batch data analysis results to obtain batch data analysis results presented collaboratively and their corresponding visualization content. In this way, users can quickly perform batch analysis operations on near-infrared data in near-infrared data analysis software, analyze intra-group data and / or inter-group data according to user needs during batch data and the analysis process, can meet the diverse data analysis needs of scientific research users, and further present the batch data analysis results and their corresponding visualization content collaboratively, which can further meet the data visualization needs of scientific research users, reduce the operation threshold, and improve data processing efficiency.

[0082] In some embodiments of the present application, a batch processing device for near-infrared data is provided. The batch processing device includes a processor, and the processor executes the following steps by running near-infrared data analysis software: obtaining target near-infrared data to be batch processed; in response to a block averaging operation on the target near-infrared data, performing block averaging on the target near-infrared data according to the block averaging processing method set by the user to obtain a block averaging result, and storing the block averaging result in a storage format different from that of the target near-infrared data; in response to a batch import operation initiated for the block averaging result, sequentially importing the block averaging result according to the grouping dimension set by the user to obtain multiple groups of block averaging results to be analyzed; in response to a batch data analysis operation initiated for the imported multiple groups of block averaging results to be analyzed, performing intra-group and / or inter-group data analysis and processing on each group of block averaging results to be analyzed according to the selected batch data analysis type to obtain batch data analysis results corresponding to the multiple groups of block averaging results to be analyzed; in response to a data visualization operation on the batch data analysis results, presenting the batch data analysis results and their corresponding visualization content collaboratively on a data visualization interface. Details or examples in the methods and steps described in the embodiments of the present application can be combined independently or in combination, and will not be elaborated here.

[0083] The processor may be a processing device including more than one general-purpose processing device, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The processor may also be more than one dedicated processing device, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), etc.

[0084] This application describes various operations or functions, which may be implemented as software code or instructions or defined as software code or instructions. Such content may be source code that can be directly executed or differential code ("incremental" or "patch" code) ("object" or "executable" form). The software code or instructions may be stored in a computer-readable storage medium, and when executed, may cause a machine to perform the described functions or operations, and include any mechanism for storing information in a form accessible to a machine (e.g., a computing device, an electronic system, etc.), such as a recordable or non-recordable medium (e.g., read only memory (ROM), random access memory (RAM), magnetic disk storage medium, optical storage medium, flash device, etc.).

[0085] The exemplary methods described in this application may be at least partially implemented by a machine or a computer. In some embodiments, a computer-readable storage medium stores computer program instructions, and when the computer program instructions are run by a processor, the processor is caused to execute the batch processing method of near-infrared data described in various embodiments of this application, which can meet the diverse data analysis needs of scientific research users, and further collaboratively present the batch data analysis results and their corresponding visualization content, which can further meet the data visualization needs of scientific research users, reduce the operation threshold, and improve the data processing efficiency.

[0086] The method includes: obtaining target near-infrared data to be batch-processed; in response to a block averaging operation on the target near-infrared data, performing block averaging processing on the target near-infrared data according to the block averaging processing method set by the user to obtain a block averaging result, and storing the block averaging result in a storage manner different from that of the target near-infrared data; in response to a batch import operation initiated for the block averaging result, sequentially importing the block averaging result according to the grouping dimension set by the user to obtain multiple groups of block averaging results to be analyzed; in response to a batch data analysis operation initiated for the imported multiple groups of block averaging results to be analyzed, performing intra-group and / or inter-group data analysis processing on each group of block averaging results to be analyzed according to the batch data analysis type selected by the user to obtain batch data analysis results corresponding to the multiple groups of block averaging results to be analyzed; in response to a data visualization operation for the batch data analysis results, presenting the batch data analysis results and their corresponding visualization content collaboratively on a data visualization interface. Details or examples of the methods and steps described in various embodiments of the present application can be combined independently or in combination here, and will not be elaborated herein.

[0087] The implementation of such a method may include software code, such as microcode, assembly language code, high-level language code, etc. Various software programming techniques can be used to create various programs or program modules. For example, a program part or program module can be designed in or with the help of Java, Python, C, C++, assembly language, or any known programming language. One or more of such software parts or modules can be integrated into a computer system and / or a computer-readable medium. Such software code can include computer-readable instructions for performing various methods. The software code can form part of a computer program product or a computer program module. In addition, in an example, the software code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable disks, removable optical disks (such as optical disks and digital video disks), cassette tapes, memory cards or storage sticks, random access memory (RAM), read-only memory (ROM), etc.

[0088] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present application having equivalent elements, modifications, omissions, combinations (such as solutions that cross various embodiments), adaptations, or changes. The elements in the claims will be broadly interpreted based on the language used in the claims and are not limited to the examples described in this specification or during the implementation of the present application, and the examples will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered only as examples, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0089] The foregoing description is intended to be illustrative and not restrictive. For example, the above examples (or one or more aspects thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. Additionally, in the above detailed description, various features may be grouped together to simplify the present application. This should not be construed as an intention that the disclosed features not claimed are necessary for any claim. On the contrary, the subject matter of the present application may be less than all the features of a particular disclosed embodiment. Thus, the claims are hereby incorporated into the detailed description by way of example or embodiment, where each claim stands on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the present application should be determined with reference to the appended claims and the full scope of equivalents to which those claims are entitled.

[0090] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions within the essence and protection scope of the present application, and such modifications or equivalent substitutions should also be regarded as falling within the protection scope of the present application.

Claims

1. A near infrared data batch processing method, characterized in that: The batch processing method is applied to near infrared data analysis software, and the processing method comprises: Acquire target near infrared data to be processed in batches; In response to a block averaging operation on the target near infrared data, performing block averaging processing on the target near infrared data according to a block averaging processing mode set by a user to obtain a block averaging result, and storing the block averaging result in a storage format different from that of the target near infrared data; In response to a batch import operation initiated for the block average results, the block average results are imported in sequence according to a grouping dimension set by a user to obtain multiple groups of block average results to be analyzed; In response to the batch data analysis operation initiated for the imported multiple groups of average results of the blocks to be analyzed, according to the batch data analysis type selected by the user, intra-group and / or inter-group data analysis processing is performed on each group of average results of the blocks to be analyzed to obtain batch data analysis results corresponding to the multiple groups of average results of the blocks to be analyzed; In response to a data visualization operation on batch data analysis results, the batch data analysis results and corresponding visualization contents are collaboratively presented on a data visualization interface.

2. The near infrared data batch processing method according to claim 1, characterized in that: Each of the grouping dimensions includes at least one grouping label, and the batch processing method further includes: In response to the data clustering operation initiated for the plurality of grouping dimensions, generating a custom group according to at least one grouping label selected by the user under each grouping dimension; Data analysis processing is performed according to the batch data analysis type selected by the user and the custom grouping, and the data analysis results and their corresponding visualization content are collaboratively presented on a data visualization interface.

3. The near infrared data batch processing method according to claim 1 or 2, characterized in that: The batch import operation initiated for the block average result includes a batch import operation initiated for a target storage file storing the block average result; The batch processing method further comprises: In response to a preview operation on an imported target storage file, a file preview interface is presented; wherein the file preview interface includes preview items corresponding to each imported target storage file, and the preview items include grouping labels of each target storage file under each grouping dimension.

4. The near infrared data batch processing method according to claim 1 or 2, characterized in that: In response to the data visualization operation on the batch data analysis results, collaboratively presenting the batch data analysis results and corresponding visualization content on the data visualization interface includes: When the data visualization type selected by the user is an activated topographic map, multiple first data selection items are presented in the data display area of ​​the data visualization interface, and at least an activated topographic map is presented in the visualization content display area; wherein the first data selection items include a batch data analysis result selection item, and the activated topographic map is presented in coordination with the target batch data analysis result selected by the batch data analysis result selection item.

5. The near infrared data batch processing method according to claim 1 or 2, characterized in that: In response to the data visualization operation on the batch data analysis results, collaboratively presenting the batch data analysis results and corresponding visualization content on the data visualization interface includes: When the data visualization type selected by the user is an activated waveform graph, an activated waveform graph and a legend containing multiple waveform data are presented in the data visualization interface; wherein each group of average results of the blocks to be analyzed corresponds to a piece of waveform data respectively, and different waveform data have different display attributes, and the legend is used to indicate the correlation between the display attributes and the analysis results of each batch of data.

6. The near infrared data batch processing method according to claim 5, characterized in that: The intra-group data analysis process includes intra-group data average processing, and the batch processing method further includes: In response to the standard deviation display operation for the activated waveform graph, determining the standard deviation of the average results of each group of blocks to be analyzed during the average processing of the data within the group; According to the standard deviations corresponding to the average results of each group of blocks to be analyzed, multiple waveform data and their corresponding standard deviation visualization elements are collaboratively presented in the data visualization interface; wherein the standard deviation visualization element is presented with a significance weaker than that of the waveform data.

7. The near infrared data batch processing method according to claim 1 or 2, characterized in that: The batch processing method includes importing a set of block average results to be analyzed according to the following steps, including: In response to the data batch import operation for the target grouping dimension, according to the grouping label and target data path set by the user under the target grouping dimension, the target storage file under the target data path is read to obtain a set of average results of the blocks to be analyzed.

8. The near infrared data batch processing method according to claim 1 or 2, characterized in that: The inter-group data analysis processing includes data analysis processing of average results of any two groups of blocks to be analyzed in the average results of each group of blocks to be analyzed; The batch processing method comprises performing inter-group data analysis processing on the average results of each group of blocks to be analyzed according to the following steps: In response to the inter-group data analysis operation initiated for each group of block average results to be analyzed, data analysis of any two groups of block average results to be analyzed are processed according to the batch data analysis type selected by the user to obtain batch data analysis results corresponding to the multiple groups of block average results to be analyzed.

9. The near infrared data batch processing method according to claim 8, characterized in that: In response to the data visualization operation on the batch data analysis results, collaboratively presenting the batch data analysis results and corresponding visualization content on the data visualization interface includes: In response to a data visualization operation for batch data analysis results, a plurality of second data selection items are presented in the data display area of ​​the data visualization interface, and at least brain visualization content and data chart visualization content are collaboratively presented in the visualization content display area; wherein the second data selection items include a block average result group selection item for indicating data visualization, and the block average result group selection item includes an option consisting of any two groups of block average results to be analyzed.

10. A near infrared data batch processing device, characterized in that: The batch processing device includes a processor, and the processor executes the following steps by running near infrared data analysis software: Acquire target near infrared data to be processed in batches; In response to a block averaging operation on the target near infrared data, performing block averaging processing on the target near infrared data according to a block averaging processing mode set by a user to obtain a block averaging result, and storing the block averaging result in a manner different from that of storing the target near infrared data; In response to a batch import operation initiated for the block average results, the block average results are imported in sequence according to a grouping dimension set by a user to obtain multiple groups of block average results to be analyzed; In response to the batch data analysis operation initiated for the imported multiple groups of average results of the blocks to be analyzed, according to the batch data analysis type selected by the user, intra-group and / or inter-group data analysis processing is performed on each group of average results of the blocks to be analyzed to obtain batch data analysis results corresponding to the multiple groups of average results of the blocks to be analyzed; In response to a data visualization operation on batch data analysis results, the batch data analysis results and corresponding visualization contents are collaboratively presented on a data visualization interface.

11. A computer program product comprising computer program instructions, characterized in that The computer program instructions are used to execute the steps of the near infrared data batch processing method according to any one of claims 1 to 9 when running.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the near-infrared data batch processing method according to any one of claims 1 to 9 are executed.

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