CN114469089B - Multi-mode data pressure resistance evaluation method and system based on virtual reality technology - Google Patents
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Abstract
The invention provides a multi-mode data pressure resistance evaluation method and a system based on a virtual reality technology, wherein the method comprises the following steps: determining the type of performing pressure resistance evaluation in the virtual reality scene according to personal information of an operator; displaying the task type of the compressive capacity assessment based on the determined type of the compressive capacity assessment; receiving an evaluation task selected by an operator aiming at the displayed task type; the method comprises the steps of testing an operator based on a received evaluation task selected by the operator, obtaining a test result of the operator, and collecting characteristic data of the operator in real time, wherein the characteristic data comprise subjective report pressure data, eye movement data, physiological data and behavior performance data; and determining compressive strength indexes of the operators according to the test results of the operators, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data. The method and the system combine the multi-mode data, and improve the accuracy of evaluation.
Description
Technical Field
The invention relates to the technical field of pressure resistance evaluation, in particular to a method and a system for evaluating multi-mode data pressure resistance based on a virtual reality technology.
Background
Under certain pressure environments, operators who participate in the system are required to have certain pressure resistance and countermeasures so as to adapt to dynamic changes and interaction influences between human-machine-environment or human-information-system, and the operation efficiency is improved under the condition of safety guarantee. Such as athletes on arenas, firefighters in high pressure environments, avionics pilots/astronauts, naval craft aeronautics, land warfare teams, shore protection soldiers, armor soldiers, infantries, air protection soldiers, artillery and railway drivers, etc. Only with certain compressive capacity, the device has good strain flexibility in emergency and makes correct decisions.
When the pressure resistance of the special personnel is evaluated, the pressure degree of the special personnel tested in the laboratory can only reach a medium degree due to the fact that the pressure induced by the laboratory is large in difference with the pressure in the real task situation, the real pressure resistance of the special personnel cannot be reflected, and therefore the test result cannot be applied to the pressure resistance training and the selection of the special personnel. In the conventional testing process, the attention of a special person is generally measured in a verbal report mode, namely, an operator is asked to trace back the testing task after the task is finished. Therefore, how to improve the accuracy of the measurement of the compressive capacity of a special person is a technical problem to be solved.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for evaluating the compressive capacity of multi-modal data based on virtual reality technology, so as to solve one or more problems in the prior art.
According to one aspect of the invention, a method for evaluating multi-mode data compressive capacity based on virtual reality technology is disclosed, the method comprising:
Determining the type of performing pressure resistance evaluation in the virtual reality scene according to personal information of an operator;
displaying the task type of the compressive capacity assessment based on the determined type of the compressive capacity assessment; the task type comprises a first level pressure task and a second level pressure task, and an evaluation task selected by an operator for the displayed task type is received;
The method comprises the steps of testing an operator based on the received evaluation task selected by the operator, obtaining a test result of the operator, and collecting characteristic data of the operator in real time, wherein the characteristic data comprise subjective report pressure data, eye movement data, physiological data and behavior performance data; wherein the eye movement data comprises pupil diameter, blink frequency and gaze class data comprising gaze time; and determining compressive strength indexes of the operators according to the test results of the operators, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data.
In some embodiments of the present invention, the first level pressure task includes a task total amount condition and a total time threshold condition, and the second level pressure task includes a task total amount condition, a total time threshold condition, a task division time threshold condition, and a task score condition; and/or
The task types also include third-level pressure tasks including conditions for rewarding and conditions for penalizing.
In some embodiments of the invention, the method further comprises:
based on the corresponding evaluation tasks of each operator in the operators, respectively carrying out corresponding tests on each operator to obtain test results of each operator, and simultaneously obtaining subjective report pressure data, eye movement data, physiological data and behavior performance data of each operator;
Respectively generating data normal models corresponding to the features according to test results of operators, subjective report pressure data, eye movement data, physiological data and behavior performance data;
And determining the normal mode level of the operator according to the test result and the characteristic data of the operator.
In some embodiments of the present invention, determining the compressive strength index of the operator according to the test result of the operator, the collected subjective report pressure data, eye movement data, physiological data, and performance data, includes:
Generating a training sample set, wherein each sample data in the training sample set comprises a test result, characteristic data and a data normal mode of the operator;
pre-training the classification model based on the training sample set to obtain a pre-training classification model;
and inputting the test result and the characteristic data of the operator into the pre-training classification model to obtain the compressive strength index of the operator.
In some embodiments of the present invention, collecting the characteristic data of the operator in real time includes:
And receiving subjective report pressure questionnaires which are made by operators for the assessment tasks after the assessment is finished, and obtaining subjective report pressure data.
In some embodiments of the invention, the method further comprises:
Filtering the collected characteristic data of the operator;
extracting characteristic indexes of the characteristic data after filtering treatment, wherein the characteristic indexes comprise a dermatologic data average value, a low-high frequency power ratio of heart rate variability data, eye movement interest area total fixation time, pupil diameter and blink frequency;
The standard score of each characteristic index is calculated.
In some embodiments of the present invention, determining the compressive strength index of the operator according to the test result of the operator, the collected subjective report pressure data, eye movement data, physiological data, and performance data, includes:
Weighting each characteristic index; wherein the sum of the weights of the characteristic indexes is 1;
Calculating the product of the standard score of each characteristic index and the weight of each characteristic index;
And summing products of the standard scores of the characteristic indexes and the weights of the characteristic indexes to obtain the compressive strength score of the operator.
In some embodiments of the present invention, the method further comprises generating a test report comprising the assessment task, the operator characteristic information, the operator's test results, eye movement data, physiological data, performance data, compressive strength indicators, questionnaire results, and normal mode levels.
According to another aspect of the present invention, there is also disclosed a compressive capacity evaluation system based on virtual reality technology, the system comprising:
The user management module is used for acquiring personal information of an operator and determining the type for evaluating the pressure resistance in the virtual reality scene according to the personal information of the operator;
The task scene module is used for displaying the task type of the compressive capacity evaluation according to the determined type of the compressive capacity evaluation, receiving an evaluation task selected by an operator according to the displayed task type, and testing the operator based on the received evaluation task selected by the operator to obtain a test result of the operator; wherein the task types include a first level pressure task and a second level pressure task;
The data management module is used for collecting subjective report pressure data, eye movement data, physiological data and behavior performance data of the operators in real time, and determining compressive strength indexes of the operators according to test results of the operators, collected subjective report pressure data, eye movement data, physiological data and behavior performance data of the operators; wherein the eye movement data comprises pupil diameter, blink frequency and gaze class data comprising gaze time; the physiological data comprises heart rate variability data and skin electricity data; the behavior performance data comprises accuracy and response time when the task is completed.
In some embodiments of the invention, the system further comprises a report management module for generating a test report.
According to the compressive capacity assessment method and the compressive capacity assessment system based on the virtual reality technology, disclosed by the embodiment of the invention, the compressive capacity of a special person is assessed by combining the virtual reality eye movement technology with the physiological technology, the implementation site is not limited, namely, a real scene can be simulated in a laboratory, so that the test result can directly reflect the real compressive capacity of the special person, and the test result can be applied to compressive training of the special person and can be used as one of indexes of multidimensional pulling. In addition, the evaluation method and the evaluation system measure the attention characteristics of special personnel by using an eye movement tracking technology in task execution, so that the attention change condition of the special personnel in the whole task interaction process can be measured, and the test result is not influenced by illumination.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present invention are not limited to the above-described specific ones, and that the above and other objects that can be achieved with the present invention will be more clearly understood from the following detailed description.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and together with the description serve to explain the application. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. Corresponding parts in the drawings may be exaggerated, i.e. made larger relative to other parts in an exemplary device actually manufactured according to the present application, for convenience in showing and describing some parts of the present application. In the drawings:
fig. 1 is a flow chart of a method for evaluating multi-modal data compression resistance based on virtual reality technology according to an embodiment of the invention.
Fig. 2 is a flow chart of a method for evaluating compressive capacity based on virtual reality technology according to another embodiment of the invention.
Fig. 3 is an exemplary diagram of a task type of the method of evaluating the compressive capacity.
Fig. 4 is an exemplary diagram of another task type of the compressive capacity testing method.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
It should be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present invention are shown in the drawings, while other details not greatly related to the present invention are omitted.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
When the pressure resistance of an operator is evaluated, a portable eye-movement instrument is generally adopted to sample the eye-movement data of the operator; the portable eye tracker has low sampling rate and is greatly influenced by illumination, which can cause great loss of sampling data and is not suitable for real task situation measurement outdoors; in addition, the data obtained by sampling the portable eye tracker is difficult to synchronize with the physiological index data of the operator; therefore, the invention discloses a multi-mode data pressure resistance evaluation method and system based on a virtual reality technology. The method and the system for evaluating the compressive capacity mainly apply the virtual reality eye movement technology to the processes of teaching, scientific research, training, selecting and pulling and the like, quantitatively analyze the virtual reality eye movement technology, further perform data mining, evaluate and further extend the virtual reality eye movement technology. As shown in FIG. 1, the method for evaluating the compressive capacity comprises steps S10 to S40.
Step S10: and determining the type of the compressive capacity evaluation in the virtual reality scene according to the personal information of the operator.
The personal information of the operator includes the name, age, occupation, training task type and the like of the operator. And the operator may be, for example, one of the special persons, and the personal information of the operator at this time may include the age, physical health information, work type, and the like of the special person. The virtual reality scene can be presented through virtual reality equipment, namely, a real evaluation scene is built through a virtual reality technology; the virtual reality device may be, for example, virtual reality glasses that are worn by an operator. The types of the pressure resistance evaluation are various, but the virtual reality scene is a scene matched with an operator; if the special person to be tested is a shooter, a virtual shooting scene is created through a virtual reality technology; when the operator is the person of other tasks, in order to simulate the task scene more accurately, further obtain more accurate evaluation results, the task scene created based on the virtual reality technology is matched with the operation field of the special person.
In the step, a real simulation task scene is created for an operator based on a virtual reality technology, compared with a result measured in a laboratory, the evaluation method is more accurate based on the task scene under virtual reality, is more interesting, and can bring testing fun to the operator while testing and evaluating. In addition, the testing method can select different task scenes aiming at different testing objects, so that the testing method can be popularized and applied to different types of groups, and the equipment utilization rate is high.
Step S20: displaying the task type of the compressive capacity assessment based on the determined type of the compressive capacity assessment; the task types comprise a first-level pressure task and a second-level pressure task, and an evaluation task selected by an operator according to the displayed task type is received.
In this step, the determined type of the compression resistance test is a type of the compression resistance test matched with the operator, and the type of the compression resistance test is specifically selected by the operator or can be obtained based on personal information matching of the operator. The task types are specifically displayed in a virtual reality scenario, and may be set or selected, and exemplary task types include a first-level pressure task and a second-level pressure task, and the pressure levels of the first-level pressure task and the second-level pressure task are different, e.g., the first-level pressure task is a low-pressure task, and the second-level pressure task is a high-pressure task.
The task conditions of the first-level pressure task are a task total amount condition and a total time threshold condition, and the task conditions of the second-level pressure task comprise a task total amount condition, a total time threshold condition, a task dividing time threshold condition and a task scoring condition. By way of example, taking a shooting type task scenario as an example, the first and second level pressure tasks are specifically a low pressure scenario task and a high pressure scenario task under a time pressure scenario task. As shown in fig. 3, in the low pressure situation task, the operator is required to complete 6 groups of 60 shooting tasks within a prescribed time, while in the high pressure situation task, in addition to the operator is required to complete 6 groups of 60 shooting tasks within a prescribed time, each shooting task is required to be completed within 3s, and each group must rest for 30s after completion; further, in the process of recording the score, for a high-pressure situation task, if the current shooting task is not shot in a specified time, the score is zero; if the last firing of each group of firing tasks does not fire or the number of rounds of firing is less than 7, the group score does not count into the total score. Under this time-pressure context task, the high-pressure context task imposes a time threshold for the operator for each sub-task to be completed.
In another embodiment of the present invention, the task types may further include a third-level pressure task that applies a higher pressure to the operator than the second-level pressure task. The task conditions of the third-level pressure task also comprise a task total amount condition and a task dividing time threshold condition; the task total amount condition refers to the total amount of tasks which need to be completed by an operator, such as 6 groups 60 firing tasks which need to be completed by the operator in shooting tasks; the task-dividing time threshold is the highest time spent by an operator for completing each task, namely, the task is required to be completed within the specified time; for example, in a shooting task, the time required to complete each design task is required. In addition, the third-level pressure task also comprises conditions for rewarding and conditions for obtaining punishment, namely, the sub-tasks are required to be completed within a specified time, and rewarding or punishment is additionally obtained under the condition that a certain condition is met. For example, during a shooting mission, if the operator shoots seven rings or more, a prize is obtained, and if the operator shoots seven rings or less, a corresponding penalty is obtained. It can be seen that the rewarding and punishing conditions further increase the pressure experienced by the operator in completing the test task.
Step S30: the method comprises the steps of testing an operator based on the received evaluation task selected by the operator, obtaining a test result of the operator, and collecting characteristic data of the operator in real time, wherein the characteristic data comprise subjective report pressure data, eye movement data, physiological data and behavior performance data; the eye movement data comprise pupil diameter, blink frequency and gazing data, the gazing data comprise gazing time, the physiological data comprise heart rate variability data and skin electricity data, and the behavior performance data comprise accuracy and response time when tasks are completed; in order to collect characteristic data of an operator in real time, the operator can wear the intelligent multi-mode physiological measurement device. Specifically, the skin electricity EDA, skin temperature SKT and heart rate HR/heart rate variability HRV data of the operator are recorded in real time through the intelligent wearable physiological measurement device, and in addition, the virtual eye movement data of the operator can also be recorded through the virtual reality device. The gazing time can be further used for evaluating the attention characteristics of the operator, and the physiological data are used for reflecting the effort degree of the operator in completing the test task. The invention combines the virtual reality technology and the physiological technology to evaluate the compression resistance of special groups such as special personnel, and has the advantage of accurate evaluation results compared with the existing commonly adopted evaluation system; and eye movement data obtained in the test process cannot be lost due to too low sampling rate of the portable eye movement instrument.
In this step, the test results may include the score the operator obtains in the mission test, the number of rewards obtained, the number of penalties obtained, and the like. It is specifically related to the task type selected by the operator, for example, when the current test is a low-pressure situation task, the test result may only include the total score obtained by the operator and the score corresponding to each score task.
Step S40: and determining compressive strength indexes of the operators according to the test results of the operators, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data.
After each characteristic data and measurement result of the operator are obtained in the above steps, the compressive strength index of the operator is further calculated according to the characteristic data and measurement result. The compressive strength index reflects the compressive strength that an operator can withstand, and based on this result, a particular person can be further compression trained or preferentially pulled out.
In addition, a questionnaire which is made by an operator for the evaluation task can be recorded in the test process. The questionnaire is used to reflect the operator's ability to pay attention to the test task conditions during the assessment process. Illustratively, a subjective questionnaire is provided for the operator to answer after each stage task is completed, and the questionnaire results after the operator answer may further be used as part of the test report.
In one embodiment of the invention, the method further comprises the steps of: based on the evaluation tasks corresponding to each operator in the operators, respectively carrying out corresponding tests on each operator to obtain test results of each operator, and simultaneously obtaining subjective report pressure data, eye movement data, physiological data and behavior performance data of each operator; respectively generating data normal models corresponding to the features according to test results of operators, subjective report pressure data, eye movement data, physiological data and behavior performance data; and determining the normal mode level of the operator according to the test result and the characteristic data of the operator.
In this embodiment, by evaluating a plurality of persons, evaluation results corresponding to the respective operators and feature data are obtained, and large data normals corresponding to the physiological data and the eye movement data can be generated based on the physiological data and the eye movement data of the plurality of persons to be tested, respectively. The multiple persons can be a large number of special persons belonging to the same task item, and the normal mode level of the operators can be intuitively analyzed based on the big data normal mode of the formed dimension characteristic indexes.
Further, the compressive strength index of the operator can be determined based on the pre-training classification model, at this time, the collected test results, subjective report pressure data, eye movement data, physiological data, behavior performance data and the like of a plurality of tested persons can be subjected to feature extraction, and a training sample set comprising a plurality of training samples is generated based on the extracted data; the VR simulation scene is further presented, and data such as test results, subjective report pressure data, eye movement data, physiological data, behavior performance data and the like, which are tested by an operator in the simulation scene, are collected and input into a trained pre-training classification model, so that compressive strength indexes of the operator can be correspondingly obtained. The classification model may be a support vector machine model, a random forest model, a gradient lifting model, or the like, and the support vector machine model is preferably selected.
Specifically, the compressive strength grade index can be calculated by the following steps: data measurement, data processing, characteristic index extraction, compression index weight weighting, compression strength grade division and the like. For data measurement, specifically, subjective and objective data of an operator are obtained in real time in a test process, wherein the subjective data are received questionnaire investigation results which are made by the operator for an evaluation task; the objective data mainly comprises test results, and collected eye movement data and physiological data of an operator, wherein the physiological data comprise heart rate variability data and skin electricity data. In the data processing step, filtering processing and characteristic signal processing are respectively carried out on the collected subjective report pressure data, eye movement data, heart rate variability data, skin electricity data and behavior performance data; and then extracting characteristic indexes of the processed characteristic data, wherein the characteristic indexes specifically comprise a dermatologic data average value, a low-high frequency power ratio of heart rate variability data and total fixation time.
In addition, the extracted index data are subjected to normalization processing uniformly according to the normal mode index result, and each characteristic data result is converted into a standard score of 0-100. Exemplary, specific conversion formulas are: Wherein, T refers to the standard score of the characteristic index, X refers to the numerical value corresponding to each characteristic index of the operator, X refers to the normal mode mean value in the normal mode of big data corresponding to the X characteristic index, and SD refers to the normal mode standard deviation corresponding to the X characteristic index. Through the calculation mode, the characteristic value index standard score of each characteristic of the operator can be calculated, namely, the heart rate variability data standard score can be expressed as T 1, the dermatologic data standard score can be expressed as T 2,, the eye movement data standard score can be expressed as T 31,T32,T33, the behavior performance data standard score can be expressed as T 4, and the subjective report data standard score can be expressed as T 5.
Furthermore, the characteristic indexes can be weighted, the weight of the heart rate variability index can be represented by a 1, the weight of the skin electric data can be represented by a 2, the weight of the eye movement data can be represented by a 3, the weight of the behavior performance data can be represented by a 4, the weight of the subjective report data can be represented by a 5, and the sum of a 1、a2、a3,a4,a5 is 1. Further, respectively calculating the product of the standard score of each characteristic index and the weight of each characteristic index; and then the product of the standard score of each characteristic index and the weight of the standard score is summed to obtain the compressive strength score of the operator. Specifically, the compressive strength fraction of the operator is calculated ：Y compressive Strength =a1T1+a2T2+a3T3+a4T4+a5T5+b; by the following formula, where b is a coefficient.
After the compressive strength score of the operator is calculated, the grade of the compressive strength of the operator can be determined based on the compressive strength score. Before that, the compressive strength fraction may be divided into five grades in advance, and the compressive strength fraction is exemplified as 0 to 20 minutes, being high compressive strength; the compression strength fraction is 20-40, namely higher compression strength; the compression strength fraction is 40-60 minutes and is common compression strength; the compression strength fraction is 60-80 minutes, which is lower compression strength; and the compressive strength fraction is 90-100 minutes, which is low compressive strength. In the step, based on the calculated compressive strength fraction of the operator, the matched compressive strength grade of the operator can be determined, and the result can also be used as a pulling reference result of excellent special personnel.
In another embodiment of the present invention, a test report including an evaluation task, operator characteristic information, an operator test result, eye movement data, heart rate variability data, skin electricity data, performance data, a compressive strength index, a questionnaire result, and a normal mode level may be further generated. The normal mode level can specifically reflect the comparison result between the operator and other special personnel, and the percentage of the operator exceeding the other special personnel can be directly obtained.
In order to more specifically describe each step of the method for evaluating the compressive capacity of the present invention, a different pressure environment is created in a shooting range environment, and other similar pressure environments are similar to the example, and based on this, relevant evaluation, training, selection and relevant scientific research are performed on specific personnel. In the process, the advanced intelligent wearable multi-mode physiological measurement equipment is combined with the virtual reality technology; the virtual reality equipment is adopted to display a virtual shooting scene, different task types are set in the scene, different pressure sources are tested, and meanwhile, related technologies such as a virtual eye movement technology, an intelligent wearable multi-mode physiological measurement technology, a behavior observation technology and the like are combined to collect physiological and behavior data of a person in the process, generate big data normal models, and select and pull the person according to the collected big data. And meanwhile, the method is popularized to other related crowds.
As shown in fig. 2, the main logic flow of this example is: setting up shooting task scenes with different levels of pressure, and simulating the situation of a real competition field; collecting data, and performing basic research to form basic research results; the method comprises the steps of collecting large-scale data of excellent special personnel, forming a big data normal model of each dimension characteristic index, forming a standard evaluation index and a compressive strength grade index through a weight weighting method, and providing a reference basis for selection and drawing of the special personnel according to the standard grade of the compressive strength index.
For this shooting task, the task test can be performed specifically under two pressure types, one of which is to complete the shooting task under time pressure and the other is to complete the shooting task under punishment pressure. The task under time pressure aims to measure the performance of shooting tasks under high pressure and low pressure respectively, as shown in fig. 3, and the specific task flow is as follows: (1) preheating; (2) baseline testing; (3) low pressure contextual tasks; (4) high pressure contextual tasks. In the preheating process, operators are familiar with scene contents, and perform trial shooting, wherein shooting tasks are simulated 10-meter pistol projects; the operator shoots the bullets 10 meters away from the target, the bullets are continuously shot, the time is not limited, and the times are 10 times (1 group); when the handle is used for shooting bullets, the shot rays and indication points are not displayed; the primary purpose of this warm-up process is to familiarize the user with the scenario. Wherein the baseline test requires that the operator try to hit a relatively high number of loops without any restrictions. In a low pressure scenario task, the user is only required to complete 6 sets of 60 firing tasks within a specified time. In the high-pressure situation task, the user is required to complete 6 groups of 60 shooting tasks within a specified time, each shooting task needs to be completed within 3s, and each group has to rest for 30s after completion; if the shooting task is not completed within the specified time, the test shooting score is recorded as 0; if the number of rings which are not sent out in time or are played in the last 1 of each group is not more than 7 rings, the group score is not counted.
The task under the rewarding and punishing pressure aims at measuring the shooting task performance under the rewarding and punishing pressure respectively, and as shown in fig. 4, the specific task flow is as follows: (1) preheating; (2) baseline testing; (3) value-linked learning; (4) rewarding and punishing pressure situation tasks; wherein the preheating and baseline testing is the same as described above under time pressure. The value linkage learning is mainly used for the operators to learn and understand the rules of rewards and punishments. There are two forms, a: red light represents incentive to rewards the coupling, green light represents incentive to punish the coupling; b: red light represents punishment of the coupling and green light represents punishment of the coupling; in the process of punishment and punishment pressure test, one task needs to be selected to be executed each time.
For the task A, the operator is specifically required to complete 6 groups of 60 shooting tasks, and starts shooting after the prompt lamp is lighted, and the operator must complete shooting within 3 s; the operator also has an integral of 50 minutes before starting the shot. In the shooting process, when the red light is turned on, the operators shoot the scores of 7 rings and more than 7 rings, and can be rewarded for 5 points; if the shot is below 7, there is no prize. When the green lamp is turned on, the operator needs to deduct 5 minutes when shooting 7 rings below; whereas 7 rings and above 7 rings are shot without penalty. In addition, when the red light and the green light are simultaneously lightened, more than 7 rings in the shot are indicated to obtain 5-unit extra rewards; whereas shooting 7 loops down will be punished an additional 5 points. For the task B and the task A, the task A is similar to the task B, the operator is required to complete 6 groups of 60 shooting tasks, and shooting is started after the prompt lamp is lighted, and the operator must complete shooting within 3 seconds; the operator also has an integral of 50 minutes before starting the shot. The task B is different from the task A in that in the task B, when a green lamp is turned on, an operator shoots the scores of 7 rings and more than 7 rings and can be rewarded for 5 minutes; if the shot number is less than 7, no rewards are generated; when the red light is on, an operator needs to punish 5 points when shooting under 7 rings, and punishment is not needed when shooting over 7 rings and 7 rings; also similar to task a, when green and red lights are on at the same time, it is shown that more than 7 rings in the shot will get 5 additional awards and less than 7 rings in the shot will be punished 5 additional points.
After the operator selects the task type according to the value connection, further testing formal reward and punishment pressure situation tasks according to the value connection selected task, and answering the meaning represented by the current red light and green light after each group of tests are finished.
In the method for evaluating the compressive capacity disclosed in the above embodiment of the present invention, all scenes are presented in virtual reality, that is, the pressure requirements are simulated by setting up shooting scenes under different pressure tasks. For this virtual reality scenario, information about clocks, scoreboards, rewards earned, etc. may also be presented in the scenario in real time.
The invention also discloses a compression resistance evaluation system corresponding to the compression resistance evaluation method, which comprises a user management module, a task scene module and a data management module. The user management module is used for acquiring personal information of the operators and determining the type of the pressure resistance evaluation in the virtual reality scene according to the personal information of the operators. The task scene module is used for displaying the task type of the compressive capacity evaluation according to the determined type of the compressive capacity evaluation, receiving an evaluation task selected by an operator according to the displayed task type, and testing the operator based on the received evaluation task selected by the operator to obtain a test result of the operator; wherein the task types include a first level pressure task and a second level pressure task. The data management module is used for collecting subjective report pressure data, eye movement data, physiological data and behavior performance data of the operators in real time, and determining compressive strength indexes of the operators according to test results of the operators, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data of the operators; wherein the eye movement data comprises pupil diameter, blink frequency and gaze class data comprising gaze time; the physiological data includes heart rate variability data and skin electrical data; the performance data includes accuracy and response time when the task is completed.
For example, the task scenario module may include a temporal pressure task and a punishment pressure task, and the relevant parameters of the selected corresponding task may be set in the task scenario. Parameters settable under the time pressure task include: the distance of shots, the size, shape and color of the scoreboard pattern, the size, shape and color of the clock pattern, the size, shape and color of the resulting bonus pattern, the number of shot sets, the number of shots per set, the countdown of shots per shot (under high pressure), the rest time per set (under high pressure), etc. Parameters which can be set under the punishment and punishment pressure task include: the distance of shots, the size, shape and color of the scoreboard pattern, the size, shape and color of the clock pattern, the size, shape and color of the resulting bonus pattern, the size and form of the indicator light, the number of shot groups, the shot count of each group, the points, the bonus points, the penalty points, etc.
Furthermore, the compressive capacity evaluation system further comprises a report management module, wherein the report management module is mainly used for generating a test report. The test report contains information such as an evaluation task, characteristic information of an operator, a test result of the operator, eye movement data, heart rate variability data, skin electricity data, eye movement data, behavior performance data, a compressive strength index, a questionnaire result, a normal mode level and the like.
According to the embodiment, the pressure resistance evaluation system integrates evaluation, training, selection and scientific research, can be used for pressure model training and recognition by combining big data, normal model, machine learning and other technologies, and can be applied to different groups. In addition, the system classifies the pressure sources in the tasks and can comprehensively evaluate the physiological and psychological characteristics of specific personnel under different pressures.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. The present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
In this disclosure, features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. The method for evaluating the multi-mode data pressure resistance based on the virtual reality technology is characterized by comprising the following steps of:
Determining the type of performing pressure resistance evaluation in the virtual reality scene according to personal information of an operator;
Displaying the task type of the compressive capacity assessment based on the determined type of the compressive capacity assessment; the task type comprises a first level pressure task and a second level pressure task, and an evaluation task selected by an operator for the displayed task type is received; the first-level pressure task comprises a task total amount condition and a total time threshold condition, and the second-level pressure task comprises a task total amount condition, a total time threshold condition, a task dividing time threshold condition and a task scoring condition; and/or the task type further comprises a third-level pressure task, wherein the third-level pressure task comprises a condition for obtaining rewards and a condition for obtaining penalties;
The method comprises the steps of testing an operator based on the received evaluation task selected by the operator, obtaining a test result of the operator, and collecting characteristic data of the operator in real time, wherein the characteristic data comprise subjective report pressure data, eye movement data, physiological data and behavior performance data; the eye movement data comprise pupil diameter, blink frequency and gazing data, the gazing data comprise gazing time, the physiological data comprise heart rate variability data and skin electricity data, and the behavior performance data comprise accuracy and response time when tasks are completed;
Determining compressive strength indexes of operators according to test results of the operators, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data; the subjective report pressure data are obtained by receiving subjective report pressure questionnaires which are made by operators for the assessment tasks after the assessment is finished;
The method further comprises the steps of: based on the corresponding evaluation tasks of each operator in the operators, respectively carrying out corresponding tests on each operator to obtain test results of each operator, and simultaneously obtaining subjective report pressure data, eye movement data, physiological data and behavior performance data of each operator; respectively generating data normal models corresponding to the features according to test results of operators, subjective report pressure data, eye movement data, physiological data and behavior performance data; determining a normal mode level of the operator according to the test result and the characteristic data of the operator;
Determining compressive strength indexes of the operators according to test results of the operators, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data, wherein the compressive strength indexes comprise: generating a training sample set, wherein each sample data in the training sample set comprises a test result, characteristic data and a data normal mode of the operator; pre-training the classification model based on the training sample set to obtain a pre-training classification model; inputting the test result and the characteristic data of the operator to the pre-training classification model to obtain the compressive strength index of the operator;
And/or determining compressive strength indexes of the operators according to the test results of the operators, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data, wherein the compressive strength indexes comprise: weighting each characteristic index; wherein the sum of the weights of the characteristic indexes is 1; calculating the product of the standard score of each characteristic index and the weight of each characteristic index; summing products of the standard scores of the characteristic indexes and the weights of the characteristic indexes to obtain compressive strength scores of operators;
The method further comprises the steps of: filtering the collected characteristic data of the operator; extracting characteristic indexes of the characteristic data after filtering treatment, wherein the characteristic indexes comprise a dermatologic data average value, a low-high frequency power ratio of heart rate variability data, eye movement interest area total fixation time, pupil diameter and blink frequency; calculating standard scores of the characteristic indexes;
The method further comprises the step of generating a test report comprising an evaluation task, operator characteristic information, an operator test result, eye movement data, physiological data, behavior performance data, compressive strength indexes, questionnaire results and normal mode level.
2. A compressive capacity assessment system based on virtual reality technology, the system comprising:
The user management module is used for acquiring personal information of an operator and determining the type for evaluating the pressure resistance in the virtual reality scene according to the personal information of the operator;
The task scene module is used for displaying the task type of the compressive capacity evaluation according to the determined type of the compressive capacity evaluation, receiving an evaluation task selected by an operator according to the displayed task type, and testing the operator based on the received evaluation task selected by the operator to obtain a test result of the operator; wherein the task types include a first level pressure task and a second level pressure task; the first-level pressure task comprises a task total amount condition and a total time threshold condition, and the second-level pressure task comprises a task total amount condition, a total time threshold condition, a task dividing time threshold condition and a task scoring condition; and/or the task type further comprises a third-level pressure task, wherein the third-level pressure task comprises a condition for obtaining rewards and a condition for obtaining penalties;
The data management module is used for collecting subjective report pressure data, eye movement data, physiological data and behavior performance data of the operators in real time, and determining compressive strength indexes of the operators according to test results of the operators, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data of the operators; wherein the eye movement data comprises pupil diameter, blink frequency and gaze class data comprising gaze time; the physiological data includes heart rate variability data and skin electrical data; the behavior performance data comprise the accuracy and the response time when the task is completed;
the report management module is used for generating a test report;
The method for determining the compressive strength index of the operator according to the test result of the operator, the collected subjective report pressure data, eye movement data, physiological data and behavior performance data of the operator comprises the following steps: generating a training sample set, wherein each sample data in the training sample set comprises a test result, characteristic data and a data normal mode of the operator; pre-training the classification model based on the training sample set to obtain a pre-training classification model; inputting the test result and the characteristic data of the operator to the pre-training classification model to obtain the compressive strength index of the operator;
And/or weighting each characteristic index; wherein the sum of the weights of the characteristic indexes is 1; calculating the product of the standard score of each characteristic index and the weight of each characteristic index; and summing products of the standard scores of the characteristic indexes and the weights of the characteristic indexes to obtain the compressive strength score of the operator.
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