Cognitive function detection system, device and method for employment post-suitable training

Abstract

The embodiment of the invention discloses a cognitive function detection system, device and method for employment post-suitable training, and relates to the technical field of cognitive detection, and the method comprises a stimulation task matching module which is used for determining a plurality of corresponding current stimulation tasks in a stimulation task library through the industry demand information, which is obtained in advance, of the industry to which a practitioner belongs; the user data acquisition module is used for acquiring test reaction data of a test user in each current stimulation task and acquiring a physiological signal data set of the test user; the physiological psychological and cognitive function analysis module is used for determining a physiological psychological and cognitive function level corresponding to the test user according to the test reaction data and a preset neural network model, and determining an awakening state of the test user based on the physiological signal data set; and the training scheme generation module is used for generating a physiological psychological and cognitive function training scheme according to the corresponding physiological psychological and cognitive function level of the test user and the wake-up state of the test user.

Description

Technical Field

[0001] The present specification relates to the field of cognitive detection technology, and in particular to a cognitive function detection system, device and method for job suitability training. Background Art

[0002] In some specific industries, there are high requirements for practitioners' cognitive information processing ability, such as industries under complex electromagnetic radiation environments. Workers in this environment mainly refer to people who frequently contact and may be exposed to a certain dose of electromagnetic radiation in their daily work. The importance of changes in physiological and psychological indicators and cognitive information processing ability of workers working in complex electromagnetic radiation environments is self-evident. Complex electromagnetic radiation environments usually refer to environments where multiple electromagnetic signals are dense and overlapping in a limited time and space, which have a significant impact on the normal operation of information systems and electronic equipment. In complex electromagnetic radiation environments, workers need to process a large amount of information quickly and accurately to make correct decisions and operations, and are often accompanied by various emergencies, such as equipment failures and communication interruptions caused by electromagnetic interference, which also have a certain impact on the physiological signals of operators. Operators need to have a high degree of cognitive flexibility and adaptability to quickly adapt to environmental changes and take effective countermeasures. Therefore, for such industries, practitioners need to receive targeted job suitability training.

[0003] In the process of job training, it is necessary to formulate targeted training tasks for practitioners according to industry needs. In these specific industries, practitioners are required to have specific cognitive information processing capabilities, and need to be able to accurately identify and track targets under conditions of environmental interference, and complete corresponding records, etc. However, the cognitive ability level of practitioners is usually obtained through subjective evaluation by managers, and lacks scientific data support, resulting in inaccurate assessment of the cognitive ability level of practitioners, and it is impossible to carry out targeted training management work in these specific industries. Summary of the invention

[0004] One or more embodiments of the present specification provide a cognitive function detection system, device and method for job-suitability training for practitioners, which are used to solve the following technical problems: the cognitive ability level of practitioners is usually obtained through subjective evaluation by managers, and lacks scientific data support, resulting in inaccurate assessment of the cognitive ability level of practitioners and inability to carry out targeted training management work in specific industries.

[0005] One or more embodiments of this specification adopt the following technical solutions:

[0006] One or more embodiments of the present specification provide a cognitive function detection system for job suitability training for practitioners, the system comprising: a stimulation task matching module, used to match tasks in a pre-built stimulation task library through pre-acquired industry demand information of the industry to which the practitioner belongs, and determine a plurality of corresponding current stimulation tasks; a user data collection module, used to collect test reaction data of the test user in each of the current stimulation tasks, and to collect a set of physiological signal data of the test user; a physiological, psychological and cognitive function analysis module, used to determine the physiological, psychological and cognitive function level corresponding to the test user according to the test reaction data and a preset neural network model, and determine the awakening state of the test user based on the physiological signal data set, wherein the awakening state includes any one or more of effective awakening and insufficient awakening; a training program generation module, used to generate a corresponding physiological, psychological and cognitive function training program according to the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user.

[0007] Furthermore, each of the test reaction data includes eye movement reaction data and key press reaction data, and the physiological signal data set includes an initial physiological signal data subset before completing the current stimulation task and a stimulated physiological signal data subset after completing the current stimulation task; wherein, the initial physiological signal data subset includes initial heart rate data, initial heart rate variability data and initial skin resistance data, and the stimulated physiological signal data subset includes stimulated heart rate data, stimulated heart rate variability data and stimulated skin resistance data.

[0008] Furthermore, according to the test reaction data and the preset neural network model, the physiological, psychological and cognitive function levels corresponding to the test user are determined, specifically including: pre-acquiring multiple historical assessment data within the industry to which the practitioners belong, sorting multiple practitioners according to the historical assessment data, and determining the work performance levels of the multiple practitioners, wherein the work performance levels include advanced, intermediate and low levels; performing task tests on the multiple practitioners for the current stimulation task, and obtaining the reaction data corresponding to each of the practitioners to construct a data set; training the neural network model through the data set to obtain a specified neural network model that meets the preset requirements; inputting the test reaction data corresponding to each of the current stimulation tasks into the specified neural network model to output the physiological, psychological and cognitive function levels corresponding to each of the current stimulation tasks, wherein the physiological, psychological and cognitive function levels include advanced cognition, intermediate cognition and low cognition.

[0009] Furthermore, based on the physiological signal data set, the awakening state of the test user is determined, specifically including: determining a plurality of physiological signal evaluation parameters corresponding to the test user through the initial physiological signal data subset and the stimulated physiological signal data subset; obtaining a plurality of pre-set physiological signal evaluation parameter thresholds, and determining a standard-reaching state corresponding to each physiological signal based on each of the physiological signal evaluation parameters and the corresponding physiological signal evaluation parameter threshold, wherein the physiological signal evaluation parameter threshold is used to represent a reference physiological signal change value corresponding to effective awakening; determining the awakening state of the test user according to the standard-reaching state corresponding to each physiological signal and a preset logical combination relationship.

[0010] Furthermore, multiple physiological signal evaluation parameters corresponding to the test user are determined through the initial physiological signal data subset and the stimulated physiological signal data subset, specifically including: determining the heart rate evaluation parameters corresponding to the heart rate signal through the difference between the initial heart rate data and the stimulated heart rate data; determining the heart rate variability evaluation parameters corresponding to the heart rate variability signal based on the difference between the stimulated heart rate variability data and the initial heart rate variability data; determining the skin resistance evaluation parameters corresponding to the skin resistance signal based on the difference between the stimulated skin resistance data and the initial skin resistance data; and determining multiple physiological signal evaluation parameters corresponding to the test user based on the heart rate evaluation parameters, the heart rate variability evaluation parameters and the skin resistance evaluation parameters.

[0011] Furthermore, according to the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user, a corresponding physiological, psychological and cognitive function training program is generated, specifically including: obtaining the task evaluation level corresponding to each of the current stimulation tasks in the physiological, psychological and cognitive function level corresponding to the test user; when the awakening state of the test user is the effective awakening, determining at least one training ability-targeted item corresponding to the test user through the task evaluation level corresponding to each of the current stimulation tasks and the pre-acquired task detection elements corresponding to each of the current stimulation tasks, wherein the task detection elements include any one of the reaction speed element, the simultaneous anti-interference ability element and the attention conversion ability element; when the awakening state of the test user is the insufficient awakening, determining the alertness ability training item corresponding to the test user; generating the physiological, psychological and cognitive function training program corresponding to the test user through the at least one training ability-targeted item corresponding to the test user and the alertness ability training item corresponding to the test user.

[0012] Furthermore, by pre-acquired industry demand information of the industry to which the practitioner belongs, task matching is performed in a pre-constructed stimulation task library to determine a corresponding plurality of current stimulation tasks, specifically including: obtaining industry demand information of the industry to which the practitioner belongs, wherein the industry demand information includes a plurality of required physiological, psychological and cognitive functional abilities; obtaining the stimulation task library, wherein the stimulation task library includes a plurality of preset stimulation tasks and the detected physiological, psychological and cognitive functional abilities corresponding to each of the stimulation tasks; performing stimulation task matching according to the plurality of required physiological, psychological and cognitive functional abilities in the industry demand information and the detected physiological, psychological and cognitive functional abilities corresponding to each of the stimulation tasks in the stimulation task library to determine the stimulation task corresponding to each of the required physiological, psychological and cognitive functional abilities; setting task parameters for each of the stimulation tasks to determine a plurality of the current stimulation tasks.

[0013] Furthermore, the system also includes a stimulation task display module, which is used to display the multiple current stimulation tasks so that the test user can complete the multiple current stimulation tasks.

[0014] One or more embodiments of this specification provide a cognitive function detection device for job suitability training, including:

[0015] at least one processor; and,

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute:

[0018] One or more embodiments of the present specification provide a cognitive function detection method for job suitability training for practitioners, the method comprising: performing task matching in a pre-built stimulation task library through pre-acquired industry demand information of the industry to which the practitioner belongs, and determining a plurality of corresponding current stimulation tasks; collecting test reaction data of the test user in each of the current stimulation tasks, and collecting a set of physiological signal data of the test user; determining the physiological, psychological and cognitive function level corresponding to the test user according to the test reaction data and a preset neural network model, and determining the awakening state of the test user based on the physiological signal data set, wherein the awakening state includes any one or more of effective awakening and insufficient awakening; generating a corresponding physiological, psychological and cognitive function training program according to the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user.

[0019] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects: through the above technical solutions, through the stimulation task matching module, the system can accurately match the industry demand information of the industry to which the practitioners belong in the pre-built stimulation task library, and determine multiple stimulation tasks suitable for the current test user. The personalized matching ensures the pertinence and effectiveness of the evaluation; the user data acquisition module not only collects the test response data of the test user in each stimulation task, but also collects its physiological signal data set, providing a rich and multi-dimensional information basis for subsequent physiological, psychological and cognitive function analysis; the physiological, psychological and cognitive function analysis module uses the test response data and the preset neural network model to scientifically and accurately determine the physiological, psychological and cognitive function level of the test user. At the same time, combined with the physiological signal data set, it can further The system first evaluates the user's arousal state, thereby gaining a more comprehensive understanding of the user's cognitive state; the training program generation module generates corresponding physiological, psychological and cognitive function training programs based on the test user's physiological, psychological and cognitive function level and arousal state. The customized program can provide more targeted training content and methods based on the individual differences of users; through customized training programs, users can conduct targeted training in areas where their physiological, psychological and cognitive functions are weak or need to be improved, thereby more effectively improving their physiological, psychological and cognitive functions. At the same time, adjustment suggestions for the arousal state can also help improve the user's attention and participation in the training process; by matching the industry demand information of the industry to which the practitioners belong, the system can ensure that the stimulation tasks and training programs are closely linked to the user's actual work scenarios, thereby improving the user's adaptability and competitiveness at work. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0021] Figure 1 A schematic diagram of the structure of a cognitive function detection system for job suitability training provided in an embodiment of this specification;

[0022] Figure 2 A schematic diagram of the structure of a cognitive function detection device for job suitability training provided in an embodiment of this specification. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0024] The embodiment of this specification provides a method for detecting cognitive functions for job suitability training. It should be noted that the execution subject in the embodiment of this specification can be a server or any device with data processing capabilities. The method for detecting cognitive functions for job suitability training provided in the embodiment of this specification includes the following steps:

[0025] First, for the industry to which the practitioners belong for job-fitness training, the industry demand information of the industry to which the practitioners belong is obtained. The industry demand information here refers to the demand for the cognitive functional capabilities of the practitioners in the current industry. Through the pre-acquired industry demand information of the industry to which the practitioners belong, task matching is performed in the pre-built stimulus task library to determine the corresponding multiple current stimulus tasks.

[0026] Through the cognitive function detection system, the test user is subjected to physiological, psychological and cognitive function tests, and the test reaction data of the test user in each current stimulation task is collected, and the physiological signal data set of the test user is collected. It should be noted that the hardware part of the cognitive function detection system includes hardware devices such as a heart rate sensor, a skin resistance sensor, a display screen, a keypad, and an eye tracker. The test user collects a physiological signal data set through a heart rate sensor and a skin resistance sensor, and the current stimulation task is displayed on the display screen, and the test reaction data of the test user in each current stimulation task is collected through a keypad, an eye tracker, etc. According to the test reaction data and a preset neural network model, the physiological, psychological and cognitive function level corresponding to the test user is determined, and based on the physiological signal data set, the awakening state of the test user is determined, wherein the awakening state includes any one or more of effective awakening and insufficient awakening; according to the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user, a corresponding physiological, psychological and cognitive function training program is generated.

[0027] Figure 1 A schematic diagram of a cognitive function detection system for job suitability training provided in an embodiment of this specification is shown in FIG. Figure 1 As shown, the cognitive function detection system includes a stimulus task matching module, a user data collection module, a physiological and psychological and cognitive function analysis module and a training program generation module.

[0028] In one embodiment of the present specification, a stimulation task matching module is used to match tasks in a pre-built stimulation task library through pre-acquired industry demand information of the industry to which the practitioners belong, and determine the corresponding multiple current stimulation tasks. A user data acquisition module is used to collect the test reaction data of the test user in each of the current stimulation tasks, and to collect the physiological signal data set of the test user. A physiological, psychological and cognitive function analysis module is used to determine the physiological, psychological and cognitive function level corresponding to the test user based on the test reaction data and a preset neural network model, and determine the awakening state of the test user based on the physiological signal data set, wherein the awakening state includes any one or more of effective awakening and insufficient awakening. A training program generation module is used to generate a corresponding physiological, psychological and cognitive function training program based on the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user.

[0029] Through the above technical solution, through the stimulation task matching module, the system can accurately match the industry demand information of the industry to which the practitioners belong in the pre-built stimulation task library, determine multiple stimulation tasks suitable for the current test user, and personalize the matching to ensure the pertinence and effectiveness of the evaluation; the user data collection module not only collects the test user's test response data in each stimulation task, but also collects its physiological signal data set, providing a rich and multi-dimensional information basis for subsequent physiological, psychological and cognitive function analysis; the physiological, psychological and cognitive function analysis module uses the test response data and the preset neural network model to scientifically and accurately determine the physiological, psychological and cognitive function level of the test user. At the same time, combined with the physiological signal data set, it can further evaluate the user's arousal state, so as to be more comprehensive. The system can understand the user's cognitive state accurately; the training program generation module generates corresponding physiological, psychological and cognitive function training programs according to the physiological, psychological and cognitive function level and arousal state of the test user. The customized program can provide more targeted training content and methods according to the individual differences of users; through customized training programs, users can carry out targeted training in areas where their physiological, psychological and cognitive functions are weak or need to be improved, so as to more effectively improve their physiological, psychological and cognitive functions. At the same time, adjustment suggestions for the arousal state can also help improve the user's attention and participation in the training process; by matching the industry demand information of the industry to which the practitioners belong, the system can ensure that the stimulation tasks and training programs are closely linked to the user's actual work scenarios, thereby improving the user's adaptability and competitiveness at work.

[0030] In one embodiment of the present specification, when matching stimulation tasks, the stimulation task matching module first obtains the industry demand information of the industry to which the practitioner belongs, and the industry demand information here includes multiple physiological, psychological and cognitive functional abilities, which refers to the physiological, psychological and cognitive functional abilities that practitioners in the industry need to possess. The pre-constructed stimulation task library is obtained, and the stimulation task library includes multiple preset stimulation tasks and the physiological, psychological and cognitive functional abilities corresponding to each stimulation task; according to the multiple physiological, psychological and cognitive functional abilities in the industry demand information and the physiological, psychological and cognitive functional abilities corresponding to each stimulation task in the stimulation task library, the stimulation task matching is performed to determine the stimulation task corresponding to each physiological, psychological and cognitive functional ability; the task parameters are set for each stimulation task to determine multiple current stimulation tasks. It should be noted that the task parameters here can be the display parameters of the stimulation task on the display screen, as well as the task parameters that the user needs to complete.

[0031] In one embodiment of the present specification, the system further includes a stimulation task display module, which may be a display screen, for displaying the multiple current stimulation tasks so as to facilitate the test user to complete the multiple current stimulation tasks.

[0032] For example, suppose that when conducting a cognitive ability assessment, it is necessary to assess the practitioner's reaction speed, simultaneous interference resistance, and attention switching ability (sequential interference resistance). The corresponding three sets of stimulation tasks are as follows:

[0033] Group 1: On a black screen, a bright yellow target point suddenly appears. The position of the point is random, and the duration of appearance (flashing duration) is 0.53 seconds, 0.77 seconds and 1.2 seconds respectively. These three groups of time lengths correspond to the three difficulty levels of "advanced", "intermediate" and "beginner". The basis for determining the above three time lengths is based on 768 members of this industry population. According to the performance level of these industry population members in completing tasks within 6 months, they are divided into three levels of "good", "medium" and "general". The three levels correspond to the length of time it takes to find the target point in a special task. When the bright yellow target point appears on the screen, the test user is required to press the button immediately. The eye movement reaction time of the test user in this group is defined as Eye-RT1, and the button reaction time is defined as Hand-RT1.

[0034] Group 2: On a black screen, two face images appear as interference images. At the same time, after a random time length of 1-5 seconds, a bright yellow target point suddenly appears on the left or right side of the screen. When the bright yellow target point appears on the screen, the test user is required to press the corresponding button on the left or right side immediately. The eye movement reaction time of the test user in this group is defined as Eye-RT2, and the button reaction time is defined as Hand-RT2.

[0035] Group 3: On a black screen, a colorful, stimulating distracting image appears. At the same time, the image disappears. After a random time of 1-5 seconds, a bright yellow target point suddenly appears on the left or right side of the area outside the original image on the screen. When the bright yellow target point appears on the screen, the test user is required to press the corresponding button on the left or right side immediately. The eye movement reaction time of the test user in this group is defined as Eye-RT3, and the button reaction time is defined as Hand-RT3.

[0036] It can be seen from the above examples that each test response data includes eye movement response data and key press response data.

[0037] In addition to evaluating the practitioner's reaction speed, simultaneous anti-interference ability and attention switching ability (sequential anti-interference ability), the user's alertness needs to be tested. Three contact sensors are connected to the three fingers of the test user, and data is collected twice within 30 seconds before and after the stimulation task is completed. The collected heart rate data is defined as HR, the heart rate variability data is defined as HRV, and the skin resistance data is defined as SC. Before the stimulation task is completed, it is defined as B (Before), and after it is defined as A (After). Therefore, the physiological signal data set includes an initial physiological signal data subset before completing the current stimulation task and a stimulated physiological signal data subset after completing the current stimulation task; wherein, the initial physiological signal data subset includes initial heart rate data, initial heart rate variability data and initial skin resistance data, and the stimulated physiological signal data subset includes stimulated heart rate data, stimulated heart rate variability data and stimulated skin resistance data.

[0038] According to the test reaction data and the preset neural network model, the physiological, psychological and cognitive function level corresponding to the test user is determined, specifically including: pre-acquiring multiple historical assessment data within the industry to which the practitioner belongs, sorting multiple practitioners according to the historical assessment data, and determining the work performance levels of the multiple practitioners, wherein the work performance levels include advanced, intermediate and low levels; performing task tests on the multiple practitioners for the current stimulation task, and obtaining the reaction data corresponding to each of the practitioners to construct a data set; training the neural network model with the data set to obtain a specified neural network model that meets the preset requirements; inputting the test reaction data corresponding to each of the current stimulation tasks into the specified neural network model to output the physiological, psychological and cognitive function level corresponding to each of the current stimulation tasks, wherein the physiological, psychological and cognitive function levels include advanced cognition, intermediate cognition and low cognition.

[0039] In one embodiment of the present specification, data analysis is performed on the physiological and behavioral performance of the test user under the above three sets of paradigms, and the cognitive ability level of the test user under special tasks is automatically obtained. The input of the analysis algorithm is the eye movement reaction time Eye-RT and the key reaction time Hand-RT when the test user completes the three sets of stimulation tasks, and the output is three levels of "high", "medium" and "low". The above three sets of stimulation paradigms, the analysis algorithm corresponding to each stimulation paradigm has the same structure, and three sets of artificial intelligence BP neural networks are trained to achieve the above goals. The source of the data set for training the BP neural network and the specific training effect are described in detail below.

[0040] 768 members of the industry population were selected, and the performance level (work performance level) of these industry population members in completing tasks within 6 months was ranked, and three levels of "good", "medium" and "general" were divided. The 768 special personnel were required to complete the three groups of stimulation tasks in module one respectively, and complete the recording of Eye-RT and Hand-RT during the eye movement reaction and key reaction in this process. The data set is constructed as: D = {x = (Eye-RT, Hand-RT), y = [1,2,3]}, D is the data set, Eye-RT in the input x is the eye movement reaction data, Hand-RT is the key reaction data, and 1 in the output y indicates high level, 2 indicates medium level, and 3 indicates low level. The data collected in the above data set according to the three stimulation paradigms are divided into 3 groups, which are used as the training data sets corresponding to each group, and 10% of the data from each group of corresponding data sets are randomly selected as the validation set to train three groups of BP neural networks respectively. After repeated parameter adjustment, the accuracy of the three groups of BP neural networks is finally obtained, which are all higher than 92.75%, and are defined as BP1, BP2 and BP3 respectively. The test response data corresponding to each current stimulation task are respectively input into the specified neural network model to output the physiological, psychological and cognitive function level corresponding to each current stimulation task, wherein the physiological, psychological and cognitive function level includes high-level cognition, intermediate cognition and low-level cognition. The system automatically calculates and analyzes the eye movement reaction time Eye-RT and key reaction time Hand-RT data of the three groups of stimulation tasks to obtain the results of the three groups of stimulation tasks, for example, "first group: high", "second group: medium", "third group: medium".

[0041] Based on the physiological signal data set, the awakening state of the test user is determined, specifically including: determining multiple physiological signal evaluation parameters corresponding to the test user through the initial physiological signal data subset and the stimulated physiological signal data subset; obtaining multiple pre-set physiological signal evaluation parameter thresholds, and determining the standard-reaching state corresponding to each physiological signal based on each of the physiological signal evaluation parameters and the corresponding physiological signal evaluation parameter threshold, wherein the physiological signal evaluation parameter threshold is used to represent the reference physiological signal change value corresponding to effective awakening; determining the awakening state of the test user according to the standard-reaching state corresponding to each physiological signal and a preset logical combination relationship.

[0042] Through the initial physiological signal data subset and the stimulated physiological signal data subset, multiple physiological signal evaluation parameters corresponding to the test user are determined, specifically including: determining the heart rate evaluation parameters corresponding to the heart rate signal through the difference between the initial heart rate data and the stimulated heart rate data; determining the heart rate variability evaluation parameters corresponding to the heart rate variability signal based on the difference between the stimulated heart rate variability data and the initial heart rate variability data; determining the skin resistance evaluation parameters corresponding to the skin resistance signal based on the difference between the stimulated skin resistance data and the initial skin resistance data; and determining multiple physiological signal evaluation parameters corresponding to the test user based on the heart rate evaluation parameters, the heart rate variability evaluation parameters and the skin resistance evaluation parameters.

[0043] In one embodiment of the present specification, multiple users with good alertness in the industry are tested by experimental testing. The age of the experimental population here can be selected from users aged 16-30 years old. The heart rate data difference, heart rate variability data difference and skin resistance data difference of this population before and after the stimulation task are tested to obtain the heart rate data evaluation parameter threshold of 20, the heart rate variability data evaluation parameter threshold of 175 and the skin resistance data evaluation parameter threshold of 372. If Δ(HR-A—HR-B)≥20, it means that the heart rate data is in the standard state; Δ(HRV-B—HRV-A)≥175, it means that the heart rate variability is in the standard state; Δ(SC-B—SC-A)≥372, it means that the skin resistance data is in the standard state. The logical combination relationship between the three preset standard states is generally set as an and relationship, that is, the heart rate data meets the standard, the heart rate variability meets the standard, and the skin resistance data meets the standard, that is, Δ(HR-A—HR-B)≥20, and Δ(HRV-B—HRV-A)≥175, and Δ(SC-B—SC-A)≥372. If the above three conditions are met at the same time, the awakening state of the test user is determined to be "effective awakening". It should be noted that insufficient awakening indicates that the user's alertness is insufficient, which means that the user is not fully alert when he should be alert, which is manifested as insufficient awakening. This situation requires further training. Effective awakening means that the user's alertness is sufficient and he can be alert when he should be alert, without the need for special training.

[0044] Through the above technical scheme, by comparing the initial and post-stimulation physiological signal data, it is possible to objectively reflect the changes in the user's physiological reactions when accepting the stimulation task, thereby scientifically evaluating their awakening state; the preset physiological signal evaluation parameter threshold is used as a reference standard for effective awakening, ensuring the accuracy of the evaluation result; the threshold is usually set based on a large amount of experimental data and expert opinions, and can better reflect the range of changes in different physiological signals under effective awakening states; each person's physiological reactions are different, and by separately evaluating multiple physiological signals (such as heart rate, skin conductance, respiratory rate, etc.), the differences between individuals can be more comprehensively considered to avoid the deviation caused by a single indicator; based on the compliance status of multiple physiological signals and the preset logical combination relationship, the awakening state of the test user can be more accurately judged, such as effective awakening, insufficient awakening, etc., to provide customized suggestions for subsequent training or intervention measures; by real-time collection and processing of physiological signal data, real-time monitoring of the awakening state of the test user can be achieved, which is convenient for timely adjustment of the training plan. According to the changes in the user's physiological responses, the system can dynamically adjust the evaluation parameters and thresholds to adapt to the evaluation needs in different situations and improve the flexibility and adaptability of the evaluation. The evaluation results based on physiological signal data can provide data support for the formulation, adjustment and optimization of training programs to ensure the scientific nature and effectiveness of training measures. By accurately judging the awakening state of the test user, training resources can be allocated more effectively, avoiding unnecessary waste and improving training efficiency.

[0045] According to the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user, a corresponding physiological, psychological and cognitive function training program is generated, specifically including: obtaining the task evaluation level corresponding to each current stimulation task in the physiological, psychological and cognitive function level corresponding to the test user; when the awakening state of the test user is the effective awakening, determining at least one training ability-targeted item corresponding to the test user through the task evaluation level corresponding to each current stimulation task and the pre-acquired task detection element corresponding to each current stimulation task, wherein the task detection element includes any one of the reaction speed element, the simultaneous anti-interference ability element and the attention switching ability element; when the awakening state of the test user is the insufficient awakening, determining the alertness ability training item corresponding to the test user; generating the physiological, psychological and cognitive function training program corresponding to the test user through the at least one training ability-targeted item corresponding to the test user and the alertness ability training item corresponding to the test user.

[0046] In one embodiment of the present specification, the task evaluation level corresponding to each current stimulation task in the physiological, psychological and cognitive function level corresponding to the test user is obtained. When the awakening state of the test user is the effective awakening, it means that the user's alertness is sufficient and can be alert when the alertness is required, without special training. Through the task evaluation level corresponding to each current stimulation task and the task detection element corresponding to each current stimulation task obtained in advance, at least one training ability targeted item corresponding to the test user is determined, wherein the task detection element includes any one of the reaction speed element, the simultaneous anti-interference ability element and the attention conversion ability element. In other words, for the task evaluation level corresponding to each current stimulation task, when the task evaluation level corresponding to a certain current stimulation task is low, it means that the detection user is insufficient in the task detection element corresponding to this stimulation task and needs targeted training. In addition, according to demand, when the task evaluation level is medium, a targeted training program for corresponding ability improvement can be set.

[0047] According to cognitive neuroscience research, the degree of arousal is an external manifestation of alertness. People with high alertness will have a higher degree of arousal when encountering target stimulus signals, but people with low alertness will have insufficient arousal when encountering target stimulus signals. Therefore, insufficient arousal can be regarded as an indicator of insufficient alertness (external manifestation). Therefore, when the degree of arousal is insufficient, alertness should be trained in a targeted manner. In addition, it should be noted that in the case of insufficient arousal, the results of the three groups of stimulation tasks still have training reference value. The reason is that the whole process can be broken down into [stimulus appears] - [user arousal increases (alertness)] - [user identifies the target] - [user responds]. If two users, namely user A and user B, have the same final reaction time, but different degrees of arousal, for example, user A has a low degree of arousal and user B has a high degree of arousal, which means that user A is a person who "is not alert enough, but reacts quickly", and user B is a person who "is alert enough and reacts not slow". Then, if training is required, user A is training alertness and user B is training reaction speed. A physiological, psychological and cognitive function training program corresponding to the test user is generated through at least one training ability-targeted item corresponding to the test user and the alertness ability training item corresponding to the test user.

[0048] Through the above technical solution, by obtaining the task evaluation level of the test user in each current stimulation task, the performance level of the test user in different physiological, psychological and cognitive function dimensions can be accurately understood; when the test user is in an effective awakening state, according to the task evaluation level and task detection elements (such as reaction speed, simultaneous anti-interference ability, attention switching ability, etc.), the training ability targeted items that need to be focused on can be accurately determined. This personalized positioning helps to ensure the pertinence and effectiveness of the training content; according to the awakening state of the test user (effective awakening or insufficient awakening), the training plan is dynamically adjusted. For users with insufficient awakening, alertness training items are specially added to improve their concentration and reaction speed, laying a good foundation for subsequent physiological, psychological and cognitive function training; this dynamic adjustment mechanism based on the awakening state enables the training plan to be flexibly adjusted according to the real-time state of the user, so as to better meet the training needs of the user; through the training ability targeted items and alertness training items tailored for the test user, it can ensure that the training content is highly matched with their actual needs, thereby improving the training effect. On the basis of alertness training items, training for specific physiological, psychological and cognitive functions is gradually introduced and strengthened to help users gradually improve various cognitive skills and achieve comprehensive development.

[0049] The present specification also provides a cognitive function detection device for job suitability training, such as Figure 2 As shown, the device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: perform task matching in a pre-built stimulation task library through pre-acquired industry demand information of the industry to which the practitioner belongs, and determine a plurality of corresponding current stimulation tasks; collect the test reaction data of the test user in each of the current stimulation tasks, and collect a set of physiological signal data of the test user; determine the physiological, psychological and cognitive function level corresponding to the test user according to the test reaction data and a preset neural network model, and determine the awakening state of the test user based on the physiological signal data set, wherein the awakening state includes any one or more of effective awakening and insufficient awakening; generate a corresponding physiological, psychological and cognitive function training program according to the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user.

[0050] The embodiments of the present specification also provide a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to: determine a plurality of corresponding current stimulation tasks by matching tasks in a pre-built stimulation task library through pre-acquired industry demand information of the industry to which the practitioners belong; collect the test reaction data of the test user in each of the current stimulation tasks, and collect a set of physiological signal data of the test user; determine the physiological, psychological and cognitive function level corresponding to the test user according to the test reaction data and a preset neural network model, and determine the awakening state of the test user based on the physiological signal data set, wherein the awakening state includes any one or more of effective awakening and insufficient awakening; generate a corresponding physiological, psychological and cognitive function training program according to the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user.

[0051] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0052] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The devices and media provided in the embodiments of this specification correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0054] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0055] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0058] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0059] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0060] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0061] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0062] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A cognitive function detection system for job suitability training, characterized in that: The system comprises: A stimulation task matching module is used to match tasks in a pre-built stimulation task library through pre-acquired industry demand information of the industry to which the practitioners belong, and determine a plurality of corresponding current stimulation tasks; A user data collection module, used to collect the test response data of the test user in each of the current stimulation tasks, and to collect the physiological signal data set of the test user; A physiological, psychological and cognitive function analysis module, used to determine the physiological, psychological and cognitive function level of the test user according to the test response data and a preset neural network model, and determine the awakening state of the test user based on the physiological signal data set, wherein the awakening state includes any one or more of effective awakening and insufficient awakening; The training program generation module is used to generate a corresponding physiological, psychological and cognitive function training program according to the physiological, psychological and cognitive function level corresponding to the test user and the awakening state of the test user.

2. A cognitive function detection system for job suitability training according to claim 1, characterized in that: Each of the test reaction data includes eye movement reaction data and key press reaction data, and the physiological signal data set includes an initial physiological signal data subset before completing the current stimulation task and a stimulation physiological signal data subset after completing the current stimulation task; The initial physiological signal data subset includes initial heart rate data, initial heart rate variability data and initial skin resistance data, and the stimulated physiological signal data subset includes stimulated heart rate data, stimulated heart rate variability data and stimulated skin resistance data.

3. A cognitive function detection system for job suitability training according to claim 2, characterized in that: Determine the physiological, psychological and cognitive function level of the test user according to the test response data and the preset neural network model, specifically including: Pre-acquire a plurality of historical assessment data in the industry to which the practitioners belong, sort the plurality of practitioners according to the historical assessment data, and determine the work performance levels of the plurality of practitioners, wherein the work performance levels include senior, intermediate and low levels; Performing a task test of the current stimulation task on the plurality of practitioners, and obtaining response data corresponding to each of the practitioners to construct a data set; Training the neural network model using the data set to obtain a specified neural network model that meets preset requirements; The test response data corresponding to each of the current stimulation tasks are input into the designated neural network model to output the physiological, psychological and cognitive function levels corresponding to each of the current stimulation tasks, wherein the physiological, psychological and cognitive function levels include high-level cognition, intermediate cognition and low-level cognition.

4. A cognitive function detection system for job suitability training according to claim 2, characterized in that: Determining the wake-up state of the test user based on the physiological signal data set specifically includes: Determining a plurality of physiological signal evaluation parameters corresponding to the test user through the initial physiological signal data subset and the stimulated physiological signal data subset; Acquire a plurality of preset physiological signal evaluation parameter thresholds, and determine the compliance state corresponding to each physiological signal based on each physiological signal evaluation parameter and the corresponding physiological signal evaluation parameter threshold, wherein the physiological signal evaluation parameter threshold is used to represent the reference physiological signal change value corresponding to the effective awakening; The awakening state of the test user is determined according to the target-reaching state corresponding to each physiological signal and a preset logical combination relationship.

5. A cognitive function detection system for job suitability training according to claim 4, characterized in that: Determining a plurality of physiological signal evaluation parameters corresponding to the test user through the initial physiological signal data subset and the stimulated physiological signal data subset specifically includes: Determining a heart rate evaluation parameter corresponding to the heart rate signal by using a difference between the initial heart rate data and the stimulated heart rate data; Determining a heart rate variability assessment parameter corresponding to a heart rate variability signal according to a difference between the stimulated heart rate variability data and the initial heart rate variability data; Determining a skin resistance evaluation parameter corresponding to a skin resistance signal based on a difference between the stimulated skin resistance data and the initial skin resistance data; A plurality of physiological signal evaluation parameters corresponding to the test user are determined according to the heart rate evaluation parameter, the heart rate variability evaluation parameter and the skin resistance evaluation parameter.

6. A cognitive function detection system for job suitability training according to claim 1, characterized in that: According to the physiological, psychological and cognitive function level of the test user and the awakening state of the test user, a corresponding physiological, psychological and cognitive function training program is generated, specifically including: Obtaining a task evaluation level corresponding to each of the current stimulation tasks in the physiological, psychological and cognitive function levels corresponding to the test user; When the awakening state of the test user is the effective awakening, determining at least one training ability target item corresponding to the test user through the task evaluation level corresponding to each of the current stimulation tasks and the pre-acquired task detection elements corresponding to each of the current stimulation tasks, wherein the task detection elements include any one of a reaction speed element, a simultaneous anti-interference ability element, and an attention switching ability element; When the awakening state of the test user is insufficiently awakened, determining an alertness ability training item corresponding to the test user; A physiological, psychological and cognitive function training program corresponding to the test user is generated through at least one training ability-targeted item corresponding to the test user and the alertness ability training item corresponding to the test user.

7. A cognitive function detection system for job suitability training according to claim 1, characterized in that: By obtaining the industry demand information of the industry to which the practitioners belong in advance, task matching is performed in the pre-built stimulus task library to determine the corresponding multiple current stimulus tasks, including: Obtaining industry demand information of the industry to which the practitioner belongs, wherein the industry demand information includes multiple required physiological, psychological and cognitive functional abilities; Acquire the stimulation task library, wherein the stimulation task library includes a plurality of preset stimulation tasks and the physiological, psychological and cognitive functional abilities detected corresponding to each stimulation task; According to the multiple physiological, psychological and cognitive functional abilities required in the industry demand information and the physiological, psychological and cognitive functional abilities detected corresponding to each of the stimulation tasks in the stimulation task library, stimulation task matching is performed to determine the stimulation task corresponding to each of the physiological, psychological and cognitive functional abilities required; Task parameters are set for each of the stimulation tasks to determine a plurality of the current stimulation tasks.

8. A cognitive function detection system for job suitability training according to claim 1, characterized in that: The system further comprises a stimulation task display module, which is used to display the multiple current stimulation tasks so as to facilitate the test user to complete the multiple current stimulation tasks.

9. A cognitive function testing device for job suitability training, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute: By using the industry demand information of the industry to which the practitioners belong obtained in advance, task matching is performed in a pre-built stimulus task library to determine a plurality of corresponding current stimulus tasks; Collecting test response data of the test user in each of the current stimulation tasks, and collecting a set of physiological signal data of the test user; Determine the physiological, psychological and cognitive function level of the test user according to the test reaction data and the preset neural network model, and determine the awakening state of the test user based on the physiological signal data set, wherein the awakening state includes any one or more of effective awakening and insufficient awakening; A corresponding physiological, psychological and cognitive function training program is generated according to the physiological, psychological and cognitive function level of the test user and the awakening state of the test user.

10. A method for detecting cognitive functions for job suitability training, characterized in that: The method comprises: By using the industry demand information of the industry to which the practitioners belong obtained in advance, task matching is performed in a pre-built stimulus task library to determine a plurality of corresponding current stimulus tasks; Collecting test response data of the test user in each of the current stimulation tasks, and collecting a set of physiological signal data of the test user; Determine the physiological, psychological and cognitive function level of the test user according to the test reaction data and the preset neural network model, and determine the awakening state of the test user based on the physiological signal data set, wherein the awakening state includes any one or more of effective awakening and insufficient awakening; A corresponding physiological, psychological and cognitive function training program is generated according to the physiological, psychological and cognitive function level of the test user and the awakening state of the test user.

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