A training behavior correction method based on big data

Abstract

The present invention belongs to the technical field of practical training behavior correction, and discloses a practical training behavior correction method based on big data; the system includes a learning data acquisition module, a learning data preprocessing module, a user login module, a user program management module, a practical training analysis module, a correction suggestion generation module and a user management module, collects status data sets and progress data, preprocesses the learning data sets to obtain a second learning data set; collects supervision data; analyzes the second learning data set to obtain an efficiency reference value, and introduces supervision data for processing to obtain a supervision report; processes the efficiency reference value and the supervision report to obtain a correction suggestion report; analyzes the correction suggestion report to obtain a calibration report, and transmits it to the user login module. The present invention has the significant advantages of strong data overall processing capabilities and good effect in assisting trainees in correcting their behavior.

Description

Technical Field

[0001] The present invention relates to the technical field of practical training behavior correction, and more specifically, to a practical training behavior correction method based on big data. Background Art

[0002] Practical training refers to the teaching process of training trainees' vocational and technical application abilities in accordance with the laws and goals of talent cultivation under the control of schools or enterprises. Traditional practical training is to place trainees in a designated area to complete preset tasks, and then manually assess the degree of task completion and conduct evaluation. However, due to their own factors during the practical training process, trainees may easily encounter various situations in the process of completing preset tasks, resulting in poor performance of the trainees. Thanks to the rapid development of science and technology, the current practical training system can perform more accurate behavior monitoring through various high-tech products. However, because the practical training process is too easily affected by its own factors, it makes it difficult for trainees to correct their training behaviors. Therefore, how to perform more accurate practical training behavior correction for different trainees based on big data is particularly important.

[0003] The patent application publication number CN117711231A discloses a fire training method based on XR training big data analysis. By using XR equipment to obtain environmental data, perception data and behavioral data of each firefighting member during fire training, the environmental data, perception data and behavioral data of each firefighting member are integrated, and the integrated data is analyzed using big data analysis methods to obtain data analysis results. The data analysis results are displayed through visualization tools, and a fire training learning model is constructed based on the data analysis results to train each firefighting member. By analyzing big data, it can support the decision-making of educational institutions and training providers, improve courses and training plans, adjust training content in real time according to the needs and performance of trainees, provide real-time feedback, and improve teaching methods, thereby improving trainees' learning effects.

[0004] However, the above-mentioned fire training method based on XR training big data analysis, although it integrates the environmental data, perception data and behavioral data of each firefighter during the fire training and then analyzes them, to a certain extent, it obtains data analysis results with reference value. However, during the training process, trainees are easily affected by their own factors, such as low concentration, which leads to poor training results. Therefore, how to identify and judge the learning ability of trainees and whether it can be corrected through external factors is particularly important for schools or enterprises.

[0005] In view of this, the present invention proposes a training behavior correction method based on big data to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned objectives, the present invention provides the following technical solutions: comprising:

[0007] The learning data acquisition module is used to collect status data sets and progress data, where the status data sets include eye movement data, expression data and operation data;

[0008] The learning data preprocessing module is used to preprocess the learning data set to obtain a second learning data set;

[0009] Furthermore, the methods for preprocessing the learning dataset include:

[0010] Obtain a second learning data set;

[0011] Substitute the eye movement data and operation data into the calculation formula: Get the processed data, where AL is the Lth parameter input in the learning data set, μ is the mean, and σ is the standard deviation;

[0012] Traverse and process the data Aa and integrate the expression data to obtain the second learning data set;

[0013] The supervisory data collection module is used to collect supervisory data;

[0014] The user login module is used for users to log in to the training behavior correction system and read user information data;

[0015] The user plan management module is used to read the calibration report corresponding to the user in the database according to the user information data sent by the user login module;

[0016] The training analysis module is used to analyze the second learning data set and introduce supervision data for processing to obtain a supervision report;

[0017] Furthermore, the training analysis module also includes a historical data scheduling module, a standard data scheduling module, a model building module and a data transmission module, wherein:

[0018] A historical data scheduling module is used to retrieve a second learning data set stored in a database;

[0019] The supervision data scheduling module is used to retrieve the supervision data obtained by the supervision data acquisition module;

[0020] Model building module, used to support the establishment of models required by the system;

[0021] Data transmission module, used to output supervision reports;

[0022] Furthermore, the step of analyzing the second learning data set includes:

[0023] Step 1: Based on the historical data scheduling module, a set of historical second learning data sets is obtained and labeled as U1, U2, U3...Un from far to near based on the timestamps of the second learning data sets;

[0024] Step 2: Based on the model building module, a first efficiency calculation model is established according to the historical second learning data set;

[0025] Step 3: Substitute into the calculation formula:

[0026] Get the efficiency reference value and perform weight correction on the expression data, where Bt is the weight of the t-th training number, Bs is the total number of training times, Bb is the eye movement data, Bc is the expression data, Bd is the operation data, Be is the progress data, B1, B2, B3 and B4 are the corresponding weight factors, and satisfy B1+B2+B3+B4=1;

[0027] Step 4: Obtain supervision data based on the supervision data scheduling module;

[0028] Step 5: Substitute into the calculation formula: Get the supervision reference value, where λ is the supervision gain coefficient, Bf is the supervision data, and Bfz is the total supervision time;

[0029] Step 6: Repeat steps 3 to 6 until a preset number of iterations is reached to obtain a second efficiency calculation model;

[0030] Step 7: Input the second learning data set and the supervision data into the second efficiency calculation model to obtain a supervision report, and output the supervision report based on the data transmission module;

[0031] Furthermore, the weight correction methods for expression data include:

[0032] By substituting into the calculation formula: Get the modified weight factor;

[0033] The corrective suggestion generating module is used to process the efficiency reference value and the supervision report to obtain a corrective suggestion report;

[0034] Furthermore, the methods for processing efficiency reference values ​​and supervision reports include:

[0035] By substituting into the calculation formula: Get the supervision gain value;

[0036] When the supervisory gain value is less than C1, a negative gain report is generated; when the supervisory gain value is greater than or equal to CI, a positive gain report is generated;

[0037] Negative gain reports include information indicating that the user's training efficiency is low under supervision, and recommending that supervisors reduce or stop supervision.

[0038] Positive gain reports include information indicating that users are more efficient in training under supervision, and recommendations for supervisors to maintain or increase supervision time.

[0039] Package the negative gain report and the positive gain report to get the correction suggestion report;

[0040] The user management module is used to analyze the correction suggestion report, obtain a calibration report, and transmit it to the user login module;

[0041] Furthermore, the analysis of the corrective suggestion report includes:

[0042] Obtain the content and progress data of the corrective suggestion report;

[0043] Adjust the monitoring data according to the contents of the correction suggestion report to obtain a calibration report;

[0044] Send the calibration report to the user login module and integrate it into the user information data;

[0045] Further, S1: collecting state data sets and progress data, the state data sets including eye movement data, expression data and operation data;

[0046] S2: preprocess the learning data set to obtain a second learning data set;

[0047] S3: Collect supervision data;

[0048] S4: The user logs into the training behavior correction system and reads the user information data;

[0049] S5: Based on the user information data sent by the user login module, read the calibration report corresponding to the user in the database;

[0050] S6: Analyze the second learning data set to obtain an efficiency reference value, introduce supervision data for processing, and obtain a supervision report;

[0051] S7: Process the efficiency reference value and supervision report to obtain a correction suggestion report;

[0052] S8: Analyze the correction suggestion report to obtain a calibration report.

[0053] The technical effects and advantages of the present invention's big data-based training behavior correction method are as follows:

[0054] The present invention analyzes the second learning data set and introduces supervision data for processing. The resulting supervision report can effectively reflect the user's training efficiency under the condition of correction by external factors, greatly reducing the labor cost caused by the traditional training that requires multi-faceted and multi-data collection, evaluation and correction of users. The efficiency reference value and the supervision report are processed to obtain a correction suggestion report, which can more intuitively show the user's training efficiency under supervised and unsupervised conditions, and can assist schools or enterprises to better implement different training correction methods for different trainees, greatly increasing the accuracy and assistance of training behavior correction. The correction suggestion report is analyzed to obtain a calibration report, which enables schools or enterprises to implement targeted corrective measures based on the training efficiency of different trainees, and the calibration report is integrated into the user information data, so that the supervisor can obtain the calibration information of the trainee in the first time, greatly increasing the intuitiveness of the training behavior correction information. Overall, the present invention has the significant advantages of strong data coordination and processing capabilities and good effect in assisting trainees in behavior correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of a big data-based training behavior correction system of the present invention;

[0056] Figure 2 This is a schematic diagram of a big data-based training behavior correction method of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a," "an," "the," and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0059] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0060] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0061] In fact, the server-side device deployed by the big data-based practical training behavior correction system may be composed of one or more devices. The above-mentioned big data-based practical training behavior correction system can be implemented as: a business instance, a virtual machine, and a hardware device. For example, the big data-based practical training behavior correction system can be implemented as a business instance deployed on one or more devices in a cloud node. In simple terms, the big data-based practical training behavior correction system can be understood as a software deployed on a cloud node, which is used to provide a big data-based practical training behavior correction system for each user terminal. Alternatively, the big data-based practical training behavior correction system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. The virtual machine is installed with application software for managing each user terminal. Alternatively, the big data-based practical training behavior correction system can also be implemented as a server-side composed of many hardware devices of the same or different types, and one or more hardware devices are provided to provide a big data-based practical training behavior correction system for each user terminal.

[0062] In terms of implementation, the big data-based training behavior correction system and the user end are mutually compatible. That is, if the big data-based training behavior correction system is an application installed on a cloud service platform, the user end is the client that establishes a communication connection with the application; or if the big data-based training behavior correction system is implemented as a website, the user end is implemented as a webpage; or if the big data-based training behavior correction system is implemented as a cloud service platform, the user end is implemented as a mini-program in an instant messaging application.

[0063] like Figure 1 , which is a system architecture diagram of a big data-based training behavior correction system provided by one embodiment of the present invention.

[0064] The practical training behavior correction system based on big data of the present invention can be set in a cloud server. In terms of implementation, it can be used as one or more service devices, or it can be installed as an application on the cloud (such as a server of a mobile service operator, a server cluster, etc.), or it can be developed as a website. According to the functions implemented, the practical training behavior correction system based on big data can include a learning data acquisition module, a learning data preprocessing module, a user login module, a user program management module, a practical training analysis module, a correction suggestion generation module and a user management module. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0065] In an embodiment of the present invention, in the practical training behavior correction system based on big data, each of the above modules can be implemented independently and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. For example, the sharing evaluation module can call the same information acquisition module to obtain the information collected by the information acquisition module. Based on the above characteristics, in the practical training behavior correction system based on big data provided by an embodiment of the present invention, there is no need to modify the program code. The scope of application of the practical training behavior correction system architecture based on big data can be adjusted by adding modules and directly calling them, thereby realizing cluster-type horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the practical training behavior correction system based on big data. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0066] Example 1

[0067] See also Figure 1 As shown, the present embodiment of the present invention provides a big data-based training behavior correction system, including:

[0068] The learning data acquisition module is used to collect status data sets and progress data, where the status data sets include eye movement data, expression data and operation data;

[0069] It should be explained that by installing a camera, the user's gaze point, gaze duration and blink frequency are collected, and weighted summation is performed to obtain eye movement data, where the gaze points are specifically valid gaze points and invalid gaze points, and they are assigned values ​​respectively, for example, valid gaze points are assigned a value of 1, and invalid gaze points are assigned a value of 0; by installing a camera and cooperating with an expression analysis model, the user's micro-expression state is collected to obtain expression data, for example, the user has three states: serious, frequent yawning, and wandering eyes, which are assigned values ​​of 2, 1, and 0 respectively; by installing a third-party tool, the user's operation frequency, pause time, and number of operation errors are collected, and weighted summation is performed to obtain operation data, where the number of operations per unit time is compared with the preset operation threshold interval to obtain a value, for example, if the number of operations is greater than the maximum operation threshold, the value is 1, and if the number of operations is less than the minimum operation threshold, the value is 0; by reading the task status, the number of tasks completed when the user exits the training correction system is collected, and divided by the total number of tasks to obtain progress data;

[0070] The learning data preprocessing module is used to preprocess the learning data set to obtain a second learning data set;

[0071] Furthermore, the methods for preprocessing the learning dataset include:

[0072] Obtain a second learning data set;

[0073] Substitute the eye movement data and operation data into the calculation formula: Get the processed data, where AL is the Lth parameter input in the learning data set, μ is the mean, and σ is the standard deviation;

[0074] Traverse and process the data Aa and integrate the expression data to obtain the second learning data set;

[0075] The supervisory data collection module is used to collect supervisory data;

[0076] It should be explained that by installing cameras, the duration of supervision behavior in the area where the user is located is collected to obtain supervision data. Supervision behavior refers to supervision by groups such as teachers or parents who are near the user.

[0077] The user login module is used for users to log in to the training behavior correction system and read user information data;

[0078] The user plan management module is used to read the calibration report corresponding to the user in the database according to the user information data sent by the user login module;

[0079] The training analysis module is used to analyze the second learning data set and introduce supervision data for processing to obtain a supervision report;

[0080] Furthermore, the training analysis module also includes a historical data scheduling module, a standard data scheduling module, a model building module and a data transmission module, wherein:

[0081] A historical data scheduling module is used to retrieve a second learning data set stored in a database;

[0082] The supervision data scheduling module is used to retrieve the supervision data obtained by the supervision data acquisition module;

[0083] Model building module, used to support the establishment of models required by the system;

[0084] Data transmission module, used to output supervision reports;

[0085] Furthermore, the step of analyzing the second learning data set includes:

[0086] Step 1: Based on the historical data scheduling module, a set of historical second learning data sets is obtained and labeled as U1, U2, U3...Un from far to near based on the timestamps of the second learning data sets;

[0087] Step 2: Based on the model building module, a first efficiency calculation model is established according to the historical second learning data set;

[0088] Step 3: Substitute into the calculation formula:

[0089] Get the efficiency reference value and perform weight correction on the expression data, where Bt is the weight of the t-th training number, Bs is the total number of training times, Bb is the eye movement data, Bc is the expression data, Bd is the operation data, Be is the progress data, B1, B2, B3 and B4 are the corresponding weight factors, and satisfy B1+B2+B3+B4=1;

[0090] Step 4: Obtain supervision data based on the supervision data scheduling module;

[0091] Step 5: Substitute into the calculation formula: Get the supervision reference value, where λ is the supervision gain coefficient, Bf is the supervision data, and Bfz is the total supervision time;

[0092] It should be explained that the supervision gain coefficient is the gain value of the user's learning efficiency when supervision exists, which is obtained through experimental calibration and manual input;

[0093] Step 6: Repeat steps 3 to 6 until a preset number of iterations is reached to obtain a second efficiency calculation model;

[0094] Step 7: Input the second learning data set and the supervision data into the second efficiency calculation model to obtain a supervision report, and output the supervision report based on the data transmission module;

[0095] Furthermore, the methods for weight correction of expression data include:

[0096] By substituting into the calculation formula: Get the modified weight factor;

[0097] It should be explained that the modified weight factor means that the larger the supervision data, the worse the user autonomy, and because the expression data will be interfered by supervision, the weight of the expression data needs to be reduced;

[0098] The corrective suggestion generating module is used to process the efficiency reference value and the supervision report to obtain a corrective suggestion report;

[0099] Furthermore, the methods for processing efficiency reference values ​​and supervision reports include:

[0100] By substituting into the calculation formula: Get the supervision gain value;

[0101] When the supervisory gain value is less than C1, a negative gain report is generated; when the supervisory gain value is greater than or equal to CI, a positive gain report is generated;

[0102] Negative gain reports include information indicating that the user's training efficiency is low under supervision, and recommending that supervisors reduce or stop supervision.

[0103] Positive gain reports include information indicating that users are more efficient in training under supervision, and recommendations for supervisors to maintain or increase supervision time.

[0104] Package the negative gain report and the positive gain report to get the correction suggestion report;

[0105] The user management module is used to analyze the correction suggestion report, obtain a calibration report, and transmit it to the user login module;

[0106] Further, the methods for analyzing the corrective suggestion report include:

[0107] Obtain the content and progress data of the corrective suggestion report;

[0108] Adjust the monitoring data according to the contents of the correction suggestion report to obtain a calibration report;

[0109] Send the calibration report to the user login module and integrate it into the user information data;

[0110] This embodiment has the beneficial effect of analyzing the second learning data set and introducing supervision data for processing, so that the obtained supervision report can effectively reflect the user's training efficiency under the condition of correction by external factors, greatly reducing the labor cost caused by the traditional training that requires multi-faceted and multi-data collection, evaluation and correction of users. The efficiency reference value and the supervision report are processed to obtain a correction suggestion report, which can more intuitively show the user's training efficiency under supervised and unsupervised conditions, and can assist schools or enterprises to better implement different training correction methods for different trainees, greatly increasing the accuracy and assistance of training behavior correction. The correction suggestion report is analyzed to obtain a calibration report, which enables schools or enterprises to implement targeted corrective measures based on the training efficiency of different trainees, and integrating the calibration report into the user information data can enable supervisors to obtain the trainee's calibration information at the first time, greatly increasing the intuitiveness of the training behavior correction information. Overall, the present invention has the significant advantages of strong data coordination and processing capabilities and good effect in assisting trainees in behavior correction.

[0111] Example 2

[0112] See also Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description of embodiment 1. A method for correcting behavior in training based on big data is provided, comprising: S1: collecting a state data set and progress data, the state data set including eye movement data, expression data, and operation data;

[0113] S2: preprocess the learning data set to obtain a second learning data set;

[0114] S3: Collect supervision data;

[0115] S4: The user logs into the training behavior correction system and reads the user information data;

[0116] S5: Based on the user information data sent by the user login module, read the calibration report corresponding to the user in the database;

[0117] S6: Analyze the second learning data set to obtain an efficiency reference value, introduce supervision data for processing, and obtain a supervision report;

[0118] S7: Process the efficiency reference value and supervision report to obtain a correction suggestion report;

[0119] S8: Analyze the correction suggestion report to obtain a calibration report.

[0120] Example 3

[0121] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0122] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0123] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0124] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A training behavior correction system based on big data, characterized by: include: Learning data acquisition module, learning data preprocessing module, supervision data acquisition module, user login module, user program management module, training analysis module, correction suggestion generation module and user management module, among which: The learning data acquisition module is used to collect status data sets and progress data, where the status data sets include eye movement data, expression data and operation data; The learning data preprocessing module is used to preprocess the learning data set to obtain a second learning data set; The supervisory data collection module is used to collect supervisory data; The user login module is used for users to log in to the training behavior correction system and read user information data; The user plan management module is used to read the calibration report corresponding to the user in the database according to the user information data sent by the user login module; The training analysis module is used to analyze the second learning data set and introduce supervision data for processing to obtain a supervision report; The corrective suggestion generating module is used to process the efficiency reference value and the supervision report to obtain a corrective suggestion report; The user management module is used to analyze the correction suggestion report, obtain a calibration report, and transmit it to the user login module; wherein: The training analysis module also includes a historical data scheduling module, a standard data scheduling module, a model building module and a data transmission module, among which: A historical data scheduling module is used to retrieve a second learning data set stored in a database; The supervision data scheduling module is used to retrieve the supervision data obtained by the supervision data acquisition module; Model building module, used to support the establishment of models required by the system; Data transmission module, used to output supervision reports; The steps for analyzing the second learning dataset include: Step 1: Based on the historical data scheduling module, a set of historical second learning data sets is obtained and labeled as U1, U2, U3...Un from far to near based on the timestamps of the second learning data sets; Step 2: Based on the model building module, a first efficiency calculation model is established according to the historical second learning data set; Step 3: Substitute into the calculation formula: Get the efficiency reference value and make weight correction on the expression data, where For the The weight of the number of training sessions, is the total number of training sessions, For eye movement data, For expression data, To operate data, For progress data, 、 、 and are the corresponding weight factors, and satisfy ; Step 4: Obtain supervision data based on the supervision data scheduling module; Step 5: Substitute into the calculation formula: Get the supervision reference value, where is the supervisory gain coefficient, To supervise the data, is the total supervision time; Step 6: Repeat steps 3 to 5 until a preset number of iterations is reached to obtain a second efficiency calculation model; Step 7: Input the second learning data set and the supervision data into the second efficiency calculation model to obtain a supervision report, and output the supervision report based on the data transmission module; The methods for handling efficiency reference values ​​and supervision reports include: By substituting into the calculation formula: Get the supervision gain value; When the supervisory gain value is less than C1, a negative gain report is generated; when the supervisory gain value is greater than or equal to C1, a positive gain report is generated; Negative gain reports include information indicating that the user's training efficiency is low under supervision, and recommending that supervisors reduce or stop supervision. Positive gain reports include information indicating that users are more efficient in training under supervision, and recommendations for supervisors to maintain or increase supervision time. Package the negative gain report and the positive gain report to get the correction suggestion report.

2. A big data-based training behavior correction system according to claim 1, characterized in that: Ways to preprocess the learning dataset include: Obtain a second learning data set; Substitute the eye movement data and operation data into the calculation formula: Get processed data, where The first input in the learning dataset parameters, is the mean, is the standard deviation; Traverse and process data , and integrate the expression data to obtain the second learning dataset.

3. The big data-based training behavior correction system according to claim 1, characterized in that: Methods for weighting expression data include: By substituting into the calculation formula: Get the modified weight factor.

4. The big data-based training behavior correction system according to claim 1, characterized in that: Methods of analyzing the corrective recommendation report include: Obtain the content and progress data of the corrective suggestion report; Adjust the monitoring data according to the contents of the correction suggestion report to obtain a calibration report; The calibration report is sent to the user login module and integrated into the user information data.

5. A method for correcting practical training behavior based on big data, implemented according to a system for correcting practical training behavior based on big data according to any one of claims 1 to 4, characterized in that: The specific steps include: S1: Collect status data sets and progress data. The status data sets include eye movement data, expression data, and operation data. S2: preprocess the learning data set to obtain a second learning data set; S3: Collect supervision data; S4: The user logs into the training behavior correction system and reads the user information data; S5: Based on the user information data sent by the user login module, read the calibration report corresponding to the user in the database; S6: Analyze the second learning data set to obtain an efficiency reference value, introduce supervision data for processing, and obtain a supervision report; S7: Process the efficiency reference value and supervision report to obtain a correction suggestion report; S8: Analyze the correction suggestion report to obtain a calibration report.

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