CN110598777B - Data processing method and system based on end cloud cooperation - Google Patents

Data processing method and system based on end cloud cooperation Download PDF

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CN110598777B
CN110598777B CN201910831260.6A CN201910831260A CN110598777B CN 110598777 B CN110598777 B CN 110598777B CN 201910831260 A CN201910831260 A CN 201910831260A CN 110598777 B CN110598777 B CN 110598777B
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于成龙
黄艳
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Hunan Shunao Medical Technology Co.,Ltd.
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Abstract

The application provides a data processing method and system based on end cloud cooperation, wherein the method comprises the following steps: based on a high-dimensional SVM, a public model is established at the cloud by using early-stage data, wherein the early-stage data comprises: the method comprises the following steps of (1) acquiring academic level sample data of different components and cognitive ability sample data of different components; the method comprises the steps that a terminal obtains user data, processes the user data to obtain user test data, and the user data comprise: academic level user data of different components and cognitive ability user data of different components; the terminal is connected with the cloud end, the user test data are uploaded to a database of the cloud end, and the public model is updated based on the user test data; the terminal generates a private model according to the public model and the user test data; and the terminal is connected with the cloud, and a training scheme is obtained from a cloud cognitive training library through the user test data. The method and the device can effectively process the relation between the brain cognitive ability level and the academic level of the student, and propose suggestions.

Description

Data processing method and system based on end cloud cooperation
Technical Field
The application relates to the technical field of data processing, in particular to a data processing method and system based on end cloud cooperation.
Background
Education, in essence, shapes the human brain. The teaching process is accompanied by the modification of neurons in the brain, the modeling of nerve circuits and the adjustment of a nervous system.
At present, although the cognitive ability can be detected and the academic achievements can be comprehensively evaluated, the cognitive ability of students reflected by all components of the academic achievements cannot be systematically analyzed due to lack of effective association and comparison between the cognitive ability and the academic achievements, and a targeted training improvement scheme cannot be provided according to the cognitive level. The lack of effective interaction between the two can not effectively link the teaching process and the cognitive ability development in time, greatly influence the teaching efficiency and restrict the mental development of students.
Most of the existing implementation schemes rely on experience and judgment of senior teachers, the academic level of students is fed back to the teachers, and empirical judgment is made on the problems of reaction in the academic level of the students based on the control of the teachers on the teaching stage and the understanding of the cognitive competence and the psychology of the students. The scheme lacks an effective quantification means and relies on the teaching experience of teachers too much. The existing realization scheme can not carry out accurate judgment and training suggestion according to different characteristics of individual students, and can not develop a targeted training improvement scheme according to the characteristics of the students.
Disclosure of Invention
The embodiment of the application provides a data processing method and system based on end cloud cooperation, and solves the problem that a personalized training scheme cannot be provided by effectively and timely associating a teaching process with cognitive ability development.
In a first aspect, an embodiment of the present application provides a data processing method, where the method includes:
based on a high-dimensional SVM, a public model is established at the cloud by using early-stage data, wherein the early-stage data comprises: the method comprises the following steps of (1) acquiring academic level sample data of different components and cognitive ability sample data of different components;
the method comprises the steps that a terminal obtains user data and processes the user data to obtain user test data, wherein the user data comprises: academic level user data of different components and cognitive ability user data of different components;
the terminal is connected with the cloud end, the user test data are uploaded to a database of the cloud end, and the public model is updated based on the user test data;
the terminal generates a private model according to the public model and the user test data;
and the terminal is connected with the cloud, and a training scheme is obtained from a cloud cognitive training library through the user test data.
In the embodiment of the application, a public model is established at the cloud end by utilizing early-stage data based on the high-dimensional SVM, and the high-dimensional SVM has high precision in a high-dimensional classification task and can process a lot of information parameters, so that academic level data of different components and cognitive ability data of different components can be processed more accurately and effectively; the method comprises the steps that a terminal obtains user data, processes the user data to obtain user test data, the terminal is connected with a cloud end and uploads the user test data to a database of the cloud end, the public model is updated based on the user test data, and the terminal generates a private model according to the public model and the user test data; the terminal is connected with the cloud, and the training scheme is obtained from the cloud cognitive training library through the user test data, so that the obtained training scheme is most in line with the current academic level and cognitive ability of the user and has pertinence.
Based on the high-dimensional SVM, a public model is established at the cloud by using early-stage data, and the method comprises the following steps:
processing the early-stage data through an analysis subsystem and an evaluation subsystem in an artificial intelligence analysis system to obtain academic horizontal sample test data of different levels and cognitive ability sample test data of different dimensions;
based on a high-dimensional SVM, the academic proficiency sample test data of different levels are used as input parameters, the cognitive competence sample test data of different dimensions are used as evaluation parameters, and the relation between the input parameters and the evaluation parameters is established.
After the user test data is obtained by processing the user data, the method further includes:
generating an analysis report based on the user test data; and outputting the analysis report.
The generated analysis report is convenient for a user to check the processing result.
The cloud cognitive training library is continuously updated based on cognitive neuroscience research data.
The cloud cognitive training library is continuously updated, so that training schemes in the training library are continuously increased, and the scheme is updated more scientifically along with the continuous update of the latest results of cognitive neuroscience.
The terminal is connected with a cloud, and a training scheme is acquired from a cloud cognitive training library through the user test data, wherein the training scheme comprises at least one of the following items:
matching a training scheme in the cloud cognitive training library according to the user test data to obtain the matched training scheme;
and consulting cloud researchers according to the user test data to obtain the training scheme.
When the user acquires the training scheme, the user can select the existing training scheme in the cloud cognitive training library, can also consult cloud researchers through the consultation window to acquire the training scheme, and if the training scheme cannot be directly matched in the cloud cognitive training library, the user can also consult the cloud researchers through the consultation window to acquire the training scheme.
In a second aspect, an embodiment of the present application provides a data processing system, including:
the first modeling unit is used for establishing a public model at a cloud end by utilizing early-stage data based on a high-dimensional SVM, wherein the early-stage data comprises: the method comprises the following steps of (1) acquiring academic level sample data of different components and cognitive ability sample data of different components;
the processing unit is used for acquiring user data by a terminal and processing the user data to obtain user test data, wherein the user data comprises: academic level user data of different components and cognitive ability user data of different components;
the updating unit is used for establishing connection between the terminal and the cloud, uploading the user test data to a database of the cloud, and updating the public model based on the user test data;
the second modeling unit is used for generating a private model by the terminal according to the public model and the user test data;
and the acquisition unit is used for establishing connection between the terminal and the cloud end and acquiring the training scheme from the cloud end cognitive training library through the user test data.
Wherein the first modeling unit includes:
the acquisition subunit is used for processing the early-stage data through an analysis subsystem and an evaluation subsystem in the artificial intelligence analysis system to obtain academic proficiency sample test data of different levels and cognitive competence sample test data of different dimensions;
and the modeling subunit is used for establishing the relation between the input parameters and the evaluation parameters by taking the academic proficiency sample test data of different levels as the input parameters and the cognitive competence sample test data of different dimensions as the evaluation parameters based on the high-dimensional SVM.
Wherein the system further comprises:
a report generation unit for generating an analysis report based on the user test data;
an output unit for outputting the analysis report.
Wherein the system further comprises:
and the training library updating unit is used for continuously updating the cloud cognitive training library based on cognitive neuroscience research data.
Wherein the obtaining unit comprises at least one of the following subunits:
the matching subunit is used for matching the training scheme in the cloud cognitive training library according to the user test data to obtain the matched training scheme;
and the consultation subunit is used for consulting cloud researchers according to the user test data to obtain the training scheme.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the first aspect of the embodiment of the present invention.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present invention.
In a fifth aspect, the present application provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present invention. The computer program product may be a software installation package.
In the embodiment of the application, the relation between the brain cognitive ability level and the academic level of the student is effectively processed based on the high-dimensional SVM through the end cloud cooperative operation framework, the training suggestion is given in time, and the problem that the teaching process and the cognitive ability development cannot be effectively connected in time and a personalized training scheme is provided is solved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a data processing method provided in an embodiment of the present application;
fig. 2 is a schematic flow chart illustrating public model establishment in a data processing method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart diagram of another data processing method provided in the embodiments of the present application;
FIG. 4 is a schematic flow chart diagram illustrating a further data processing method provided in an embodiment of the present application;
fig. 5 is a schematic flowchart of another data processing method provided in an embodiment of the present application;
fig. 6 is a schematic flowchart of another data processing method provided in an embodiment of the present application;
FIG. 7 is a block diagram of a data processing system according to an embodiment of the present application;
FIG. 8 is a block diagram of another data processing system according to an embodiment of the present application;
FIG. 9 is a block diagram of yet another data processing system provided by an embodiment of the present application;
FIG. 10 is a schematic structural diagram of an artificial intelligence analysis system in a data processing system according to an embodiment of the present application;
FIG. 11 is a schematic diagram of model distribution based on an end cloud collaborative computing framework.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
SVM (Support Vector Machine) refers to a Support Vector Machine, and is a common discrimination method. In the field of machine learning, a supervised learning model is typically used for pattern recognition, classification, and regression analysis.
The SVM method maps a sample space into a high-dimensional or even infinite-dimensional feature space (Hilbert space) through a nonlinear mapping p, so that the problem of nonlinear divisibility in the original sample space is converted into the problem of linear divisibility in the feature space. Simply stated, it is the lifting and linearization. Dimension raising, that is, mapping a sample to a high-dimensional space, generally increases the complexity of calculation and even causes "dimension disaster", so people have little need for help. However, as a problem of classification, regression, etc., a sample set that is likely to be not linearly processed in a low-dimensional sample space may be linearly divided (or regressed) in a high-dimensional feature space by a linear hyperplane. The common dimension increasing brings the complexity of calculation, and the SVM method skillfully solves the problem that the explicit expression of nonlinear mapping is not needed to be known by applying the expansion theorem of the kernel function; because the linear learning machine is built in the high-dimensional feature space, the computational complexity is hardly increased compared with the linear model, and the 'dimensionality disaster' is avoided to some extent.
The high-dimensional SVM is an artificial intelligence classification method, is suitable for a data set with high dimensionality at input and output, and can perform multi-class fine classification under the condition that a plurality of input parameters and output parameters exist. The method has the advantages of higher precision in the high-dimensional classification task and more information processing parameters.
Referring to fig. 1, fig. 1 is a schematic flowchart of a data processing method provided in an embodiment of the present application, and as shown in the figure, the method includes:
s101, based on a high-dimensional SVM, a public model is established at a cloud end by utilizing early-stage data, wherein the early-stage data comprises: academic level sample data of different components and cognitive ability sample data of different components.
The early sample data is the sample data of academic levels of different components and the sample data of cognitive competence of different components in the collected student sample, the academic levels of different components refer to the academic levels of different subjects, such as Chinese, mathematics, foreign languages and other subjects, and the cognitive competence of different components refers to various intelligence-related competencies and skills, such as joint situation, attention, discrimination, imitation, spatial relationship, temporal relationship, causal relationship, reasoning, classification, sequencing, sequence and other cognitive competencies.
The public model is also based on the general principles and recent achievements of cognitive neuroscience.
S102, a terminal acquires user data and processes the user data to obtain user test data, wherein the user data comprises: academic level user data of different components and cognitive ability user data of different components.
The user data is obtained by downloading a public model from the cloud to the terminal and testing on the basis of the public model.
And S103, the terminal is connected with the cloud end, the user test data are uploaded to a database of the cloud end, and the public model is updated based on the user test data.
And S104, the terminal generates a private model according to the public model and the user test data.
The steps S102, S103 and S104 are artificial intelligence models based on end cloud collaborative computing, the internal relation between the academic level and the cognitive ability is deeply analyzed, a public model is built, all components of the academic level and the cognitive ability of the user are quantitatively analyzed, and a personal private model of the user is timely iterated and updated and formed on the basis of the public model and a database generated by the user through self training, so that an individual evaluation system of the academic level and the cognitive ability of the user is formed. For a new user, the public model is first downloaded from the cloud. On the basis, based on the continuous evaluation result of the user, newly generated user test data is uploaded to a cloud database on one hand, the user test data is compared with the existing data, and a public model is updated based on the commonality of the data; and on the other hand, training the private model of the user at the terminal, and generating the private model according to the characteristics of the user.
And S105, the terminal is connected with the cloud, and the training scheme is obtained from the cloud cognitive training library through the user test data.
As shown in fig. 2, based on the high-dimensional SVM, a public model is established at the cloud by using the previous data, which includes:
s201, processing the early-stage data through an artificial intelligence analysis system, wherein the processing method comprises the steps of analyzing the academic level sample data of different components through an analysis subsystem of the artificial intelligence analysis system to obtain academic level sample test data of different levels, and processing the cognitive ability sample data of different components through an evaluation subsystem of the artificial intelligence analysis system to obtain the cognitive ability sample test data of different dimensions.
For example, the analysis subsystem of the artificial intelligence analysis system is used for analyzing the academic horizontal sample data of Chinese, mathematics and the like to obtain the academic horizontal test data of reading, composition and the like in the Chinese and the academic horizontal sample test data of operation, geometry and the like in the mathematics; the evaluation subsystem processes sample data of the understanding ability, the expression ability, the reasoning ability and the spatial relationship ability into sample test data of the understanding ability, the expression ability, the reasoning ability and the spatial relationship ability with different dimensions.
S2021, taking the academic proficiency sample test data of different levels as input parameters;
s2022, taking the cognitive ability sample test data with different dimensionalities as evaluation parameters;
s203, establishing the relation between the input parameters and the evaluation parameters.
For example, reading academic proficiency sample test data and understanding cognitive competence sample test data in the language are respectively used as input parameters and evaluation parameters, and the relation between the reading academic proficiency sample test data and the understanding cognitive competence sample test data is established on the basis of the high-dimensional SVM.
In some possible scenes, user data can be presented to a user in a form of generating an analysis report, and the user can access the cloud cognitive training database according to the analysis report to obtain a training scheme in the cloud cognitive training database. Referring to fig. 3, fig. 3 is a schematic flow chart of another data processing method according to an embodiment of the present invention, where as shown in the figure, the method includes:
s301, based on the high-dimensional SVM, a public model is established at the cloud by using early-stage data, wherein the early-stage data comprises: academic level sample data of different components and cognitive ability sample data of different components.
S302, a terminal acquires user data and processes the user data to obtain user test data, wherein the user data comprises: academic-level user data of different components and cognitive ability user data of different components.
S3031, the terminal is connected with a cloud end, the user test data is uploaded to a database of the cloud end, and the public model is updated based on the user test data.
S3032, generating an analysis report based on the user test data, and outputting the analysis report.
The analysis report content comprises academic-level user test data of different levels and cognitive ability user test data of different dimensions.
S304, the terminal generates a private model according to the public model and the user test data.
S305, the terminal is connected with the cloud, and a training scheme is obtained from a cloud cognitive training library through the user test data.
Here, except for step S3032, the detailed implementation manners of other steps may refer to the descriptions of step S101 to step S105, and are not described herein again.
In some possible scenarios, the method for the user to obtain the training scheme from the cloud cognitive training library through the user test data may be: matching training schemes in a cloud cognitive training library according to user test data to obtain matched targeted training schemes; it can also be: according to the user test data, consulting cloud researchers through a consulting window to obtain a training scheme; the method can also be as follows: the user can select to match the training scheme according to the user test data, and can also select to consult a cloud research staff according to the user test data to obtain the training scheme. Referring to fig. 4, 5, and 6, except S405, the training scheme in the cloud cognitive training library is matched according to the user test data, and the matched training scheme is obtained. And S505, consulting cloud researchers according to the user test data to obtain the training scheme. And S6051, matching the training scheme in the cloud cognitive training library according to the user test data, and acquiring the matched training scheme. S6052, the cloud researcher is consulted according to the user test data to obtain the training scheme, and the specific implementation manners of other steps may refer to the descriptions of step S101 to step S105, which are not described herein again.
The above embodiments of the present application can be applied to a data processing system shown in fig. 7, and the system will be described next.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a data processing system according to an embodiment of the present application, and as shown in the diagram, the data processing system includes: the system comprises a first modeling unit 701, a processing unit 702, an updating unit 703, a second modeling unit 704 and an obtaining unit 705, wherein the first modeling unit 701 further comprises an obtaining subunit 7011 and a modeling subunit 7012, and the obtaining unit comprises an consulting subunit 7051 and a matching subunit 7052.
The first modeling unit 701 is configured to establish a public model at a cloud end by using early-stage data based on a high-dimensional SVM, where the early-stage data includes: the method comprises the following steps of (1) acquiring academic level sample data of different components and cognitive ability sample data of different components;
a processing unit 702, configured to obtain user data by a terminal, and process the user data to obtain user test data, where the user data includes: academic level user data of different components and cognitive ability user data of different components;
the updating unit 703 is configured to establish a connection between the terminal and the cloud, upload the user test data to a database of the cloud, and update the public model based on the user test data;
the second modeling unit 704 is used for generating a private model by the terminal according to the public model and the user test data;
the obtaining unit 705 is configured to establish a connection between the terminal and the cloud, and obtain a training scheme from the cloud cognitive training library through the user test data.
Wherein the first modeling unit includes:
an obtaining subunit 7011, configured to process the early-stage data through an analysis subsystem and an evaluation subsystem in the artificial intelligence analysis system, and obtain academic proficiency sample test data of different levels and cognitive competence sample test data of different dimensions;
and a modeling unit 7012, configured to, based on the high-dimensional SVM, use the academic proficiency sample test data at different levels as an input parameter, use the cognitive competence sample test data at different dimensions as an evaluation parameter, and establish a relationship between the input parameter and the evaluation parameter.
Wherein the acquisition unit comprises at least one of the following subunits:
the matching subunit 7051 is configured to match the training scheme in the cloud cognitive training library according to the user test data, and obtain the matched training scheme;
and a consulting subunit 7052, configured to consult a cloud researcher according to the user test data to obtain the training scheme.
In some possible scenarios, referring to fig. 8, the system shown in fig. 7 further comprises a report generating unit 806, an output unit 807, referring to fig. 9, the system shown in fig. 7 further comprises a training base updating unit 906.
A report generating unit 806 for generating an analysis report based on the user test data,
an output unit 807 for outputting the analysis report,
a training library updating unit 906, configured to continuously update the cloud cognitive training library based on cognitive neuroscience research data.
Referring to fig. 10, fig. 10 is a schematic structural diagram of an artificial intelligence analysis system in a data processing system according to an embodiment of the present disclosure, where the artificial intelligence analysis system includes an analysis subsystem 10 and an evaluation subsystem 20, the analysis subsystem 10 of the artificial intelligence analysis system is configured to analyze academic level sample data of different components to obtain academic level sample test data of different levels, and the evaluation subsystem 20 of the artificial intelligence analysis system is configured to process cognitive ability sample data of different components to obtain cognitive ability sample test data of different dimensions.
Referring to fig. 11, fig. 11 is a schematic diagram of model distribution based on a terminal cloud collaborative computing framework, and a public model between the academic proficiency test result and the brain cognitive ability assessment, that is, a cloud public model, is preliminarily formed based on early-stage data. For a new user, the public model is first downloaded from the cloud. On the basis, based on the continuous evaluation result of the students, uploading the newly generated user test data to the cloud end on one hand, comparing the user test data with the existing data, and updating the public model based on the commonality of the data; on the other hand, the private model of the student is trained at the terminal, and the private model is generated according to the characteristics of the student.
Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to execute part or all of the steps of any one of the methods described in the above method embodiments, and the computer includes an electronic device.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the methods as set out in the above method embodiments. The computer program product may be a software installation package, the computer comprising an electronic device.
It should be noted that for simplicity of description, the above-mentioned embodiments of the method are described as a series of acts, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and elements referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the present application, which are essential or part of the technical solutions contributing to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods of the embodiments of the present invention. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (12)

1. A method of data processing, the method comprising:
based on a high-dimensional SVM, a public model is established at a cloud end by utilizing early-stage data, wherein the early-stage data comprises: the method comprises the following steps of (1) acquiring academic level sample data of different components and cognitive ability sample data of different components;
the method comprises the steps that a terminal obtains user data and processes the user data to obtain user test data, wherein the user data comprises: academic level user data of different components and cognitive ability user data of different components;
the terminal is connected with the cloud end, the user test data are uploaded to a database of the cloud end, and the public model is updated based on the user test data;
the terminal generates a private model according to the public model and the user test data;
and the terminal is connected with the cloud, and a training scheme is obtained from a cloud cognitive training library through the user test data.
2. The method of claim 1, wherein the building of the public model at the cloud using the prior data based on the high-dimensional SVM comprises:
processing the early-stage data through an analysis subsystem and an evaluation subsystem in an artificial intelligence analysis system to obtain academic horizontal sample test data of different levels and cognitive ability sample test data of different dimensions;
based on a high-dimensional SVM, taking the academic proficiency sample test data of different levels as input parameters, taking the cognitive competence sample test data of different dimensions as evaluation parameters, and establishing the relation between the input parameters and the evaluation parameters.
3. The method of claim 1, wherein after processing the user data to obtain user test data, the method further comprises:
generating an analysis report based on the user test data;
and outputting the analysis report.
4. The method of claim 1, wherein the cloud-based cognitive training library is continuously updated based on cognitive neuroscience research data.
5. The method according to any one of claims 1 to 4, wherein the terminal establishes a connection with a cloud, and the training scheme is obtained from a cloud cognitive training library through the user test data, and the method includes at least one of the following steps:
matching a training scheme in the cloud cognitive training library according to the user test data to obtain the matched training scheme;
and consulting cloud researchers according to the user test data to obtain the training scheme.
6. A data processing system, characterized in that the system comprises:
the first modeling unit is used for establishing a public model at a cloud end by utilizing early-stage data based on a high-dimensional SVM, wherein the early-stage data comprises: the method comprises the following steps of (1) acquiring academic level sample data of different components and cognitive ability sample data of different components;
the processing unit is used for acquiring user data by a terminal and processing the user data to obtain user test data, wherein the user data comprises: academic level user data of different components and cognitive ability user data of different components;
the updating unit is used for establishing connection between the terminal and the cloud, uploading the user test data to a database of the cloud, and updating the public model based on the user test data;
the second modeling unit is used for generating a private model by the terminal according to the public model and the user test data;
and the acquisition unit is used for establishing connection between the terminal and the cloud and acquiring the training scheme from the cloud cognitive training library through the user test data.
7. The system of claim 6, wherein the first modeling unit comprises:
the acquisition subunit is used for processing the early-stage data through an analysis subsystem and an evaluation subsystem in the artificial intelligence analysis system to obtain academic proficiency sample test data of different levels and cognitive competence sample test data of different dimensions;
and the modeling subunit is used for establishing the relation between the input parameters and the evaluation parameters by taking the academic proficiency sample test data of different levels as the input parameters and the cognitive competence sample test data of different dimensions as the evaluation parameters based on the high-dimensional SVM.
8. The system of claim 6, further comprising:
a report generation unit for generating an analysis report based on the user test data;
an output unit for outputting the analysis report.
9. The system of claim 6, further comprising:
and the training library updating unit is used for continuously updating the cloud cognitive training library based on cognitive neuroscience research data.
10. The system according to any of claims 6-9, characterized in that the acquisition unit comprises at least one of the following sub-units:
the matching subunit is used for matching the training scheme in the cloud cognitive training library according to the user test data to obtain the matched training scheme;
and the consultation subunit is used for consulting cloud researchers according to the user test data to obtain the training scheme.
11. An electronic device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-5.
12. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any of the claims 1-5.
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