Electrical power practitioner psychological safety quality intelligent evaluation method and device based on electroencephalogram information, equipment and storage medium

Abstract

The application provides a power practitioner psychological safety quality intelligent evaluation method and device based on electroencephalogram information, equipment and a storage medium. It relates to the technical field of electroencephalogram signals. The method comprises the following steps: obtaining electroencephalogram information of power practitioners in different working scenarios, extracting effective electroencephalogram information, and cleaning to obtain target electroencephalogram information; marking the information of the power practitioner in the target electroencephalogram information; based on the set psychological safety quality quantification formula of the power practitioner in different working scenarios, calculating the psychological safety quality quantification score, and combining the psychological safety quality quantification score with the target electroencephalogram information to obtain a psychological safety quality quantification data set; modeling by using the psychological safety quality quantification data set, and obtaining a power practitioner psychological safety quality intelligent evaluation model through training. The application can realize intelligent and objective measurement and evaluation of the psychological safety quality of power practitioners in various working scenarios, and reduce the working risk of power practitioners.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electroencephalogram signals, and particularly relates to an intelligent evaluation method, device and equipment for psychological safety quality of power practitioners based on electroencephalogram information and a storage medium. BACKGROUND

[0002] With the acceleration of social development process, the demand for power practitioners increases greatly, but the power industry belongs to a high-risk field, and practitioners need to work in a high-risk environment, and the psychological safety quality directly affects the operation safety and work efficiency. Therefore, it is crucial to evaluate the psychological safety quality of power practitioners to reduce accidents and improve safety.

[0003] At present, the evaluation of the psychological safety quality of traditional power practitioners mainly depends on questionnaires or observation, which is difficult to objectively reflect the psychological state. In addition, only relying on artificial psychological consultants to evaluate the psychological safety quality cannot meet the increasing demand of power practitioners, and the psychological characteristics of different power practitioners are greatly different, so it is difficult for artificial methods to provide personalized evaluation of the psychological safety quality of power practitioners. The existence of the above problems causes the current power system to lack the ability to evaluate the psychological safety quality of power practitioners, resulting in the production risk caused by the insufficient psychological safety quality of power practitioners. Therefore, an intelligent evaluation method and system for the psychological safety quality of power practitioners are urgently needed to replace traditional artificial methods, improve the objective ability of the power system to evaluate the psychological safety quality of practitioners, and reduce the risk of work. SUMMARY

[0004] The present application provides an intelligent evaluation method, device, equipment and storage medium for the psychological safety quality of power practitioners based on electroencephalogram information, to realize the intelligent and objective evaluation of the psychological safety quality of power practitioners and reduce the risk of work.

[0005] In a first aspect, the present application provides an intelligent evaluation method for the psychological safety quality of power practitioners based on electroencephalogram information, comprising:

[0006] obtaining electroencephalogram information of power practitioners in different working scenarios;

[0007] extracting effective electroencephalogram information of power practitioners in different working scenarios from the electroencephalogram information of power practitioners in different working scenarios based on the marking information; wherein the effective electroencephalogram information of power practitioners in different working scenarios is electroencephalogram data reflecting the change of the psychological state of power practitioners in the working process;

[0008] cleaning the effective electroencephalogram information of power practitioners in different working scenarios to obtain target electroencephalogram information of power practitioners in different working scenarios;

[0009] The information of the electric power practitioner is labeled in the target brain electrical information of the electric power practitioner in different work scenes;

[0010] Based on the psychological safety quality quantization formula of the electric power practitioner in different work scenes, the psychological safety quality quantization score of the electric power practitioner is calculated according to the information of the electric power practitioner, and the psychological safety quality quantization score of the electric power practitioner is combined with the target brain electrical information to obtain the psychological safety quality quantization data set of the electric power practitioner in different work scenes, which is represented as Wherein, Fm(r) represents the target brain electrical information of the rth electric power practitioner in the work scene m, Fm(r) represents the psychological safety quality quantization score of the rth electric power practitioner in the work scene m;

[0011] The psychological safety quality intelligent evaluation model of the electric power practitioner is obtained by modeling and training the psychological safety quality quantization data set of the electric power practitioner in different work scenes. The input of the psychological safety quality intelligent evaluation model of the electric power practitioner is the target brain electrical information of the electric power practitioner, and the output is the psychological safety quality score of the electric power practitioner in the set work scene.

[0012] Further, based on the label information, the effective brain electrical information of the electric power practitioner in different work scenes is extracted from the brain electrical information of the electric power practitioner in different work scenes, including:

[0013] The label information is obtained, and the label information includes at least one time period label, and the time period label includes a start time and an end time;

[0014] Based on the start time and the end time, the effective brain electrical information of the electric power practitioner in different work scenes is extracted, Wherein S m (t1:t2) represents the effective brain electrical information of the electric power practitioner in the work scene m, t1 represents the start time, t2 represents the end time, And Fm(r) represents the brain electrical signal of the first brain electrical channel, the second brain electrical channel and the nth brain electrical channel of the electric power practitioner in the work scene m.

[0015] Further, the effective brain electrical information of the electric power practitioner in different work scenes is cleaned to obtain the target brain electrical information F s of the electric power practitioner in different work scenes, which is represented as F s = Kalman[S m (t1:t2)], wherein Kalman represents Kalman filtering processing.

[0016] Furthermore, the information of the power industry practitioners includes name, age, years of service, skill level (SKL), and psychological characteristics (PC) at work; the quantitative formula for the psychological safety quality of power industry practitioners under different work scenarios is expressed as follows:

[0017] Socr r =φ(Aag,Year,SKL,PC)

[0018] In the formula, Socr r Let φ represent the quantitative score of psychological safety awareness obtained by the r-th power industry worker, and let φ represent the quantitative standard of psychological safety awareness score based on human experience.

[0019] Furthermore, a model is created using quantitative data sets of psychological safety awareness among power industry workers in different work scenarios. After training, an intelligent assessment model for the psychological safety awareness of power industry workers is obtained, including:

[0020] The convolutional neural network is used to extract the target EEG information of the r-th power industry worker in the work scene m, and the features of the n-th channel EEG information of the power industry worker r are extracted by convolution operation in the work scene m.

[0021] By fusing the EEG information features of various channels of power practitioner r, multi-channel EEG features of power practitioner r are obtained, thereby achieving a comprehensive representation of the EEG information of power practitioner r.

[0022] Input the multi-channel EEG features of power industry worker r into the predictor and output the psychological safety quality score of power industry worker r in the work scenario m.

[0023] The above process was trained to obtain an intelligent assessment model for the psychological safety awareness of power industry practitioners.

[0024] Furthermore, a convolutional neural network is used to extract features from the target EEG information of the r-th power industry worker in work scenario m, resulting in the N-th channel EEG information features of the power industry worker r extracted through convolution operations in work scenario m. The calculation process is expressed as follows:

[0025]

[0026] In the formula, f CNN This represents the convolution operation. This represents the EEG information of the nth channel of an electrical worker r in work scenario m.

[0027] Furthermore, by fusing the EEG information features of the various channels of the power industry worker r, we obtain the multi-channel EEG features of the power industry worker r. The computational expression for achieving a comprehensive representation of the power industry worker's EEG information is as follows:

[0028]

[0029] In the formula, This represents the multichannel EEG characteristics of an electrical worker, r, in work scenario m. This represents a feature concatenation mapping. and These represent the first and second channel EEG information features of an electrician r extracted through convolution operations in work scenario m;

[0030] The multi-channel EEG features of power industry worker r are input into the predictor, and the calculated expression of the psychological safety quality score of power industry worker r in work scenario m is as follows:

[0031]

[0032] In the formula, Let r represent the psychological safety quality score of power industry worker r in work scenario m, and Tanh(·) represent the regression function in the predictor.

[0033] Secondly, this application provides an intelligent evaluation device for the psychological safety awareness of power industry practitioners based on electroencephalogram (EEG) information. The device includes a controller, which is configured to:

[0034] The data acquisition module is configured to acquire electroencephalogram (EEG) information of power industry workers in different work scenarios.

[0035] The data tagging module is configured to extract effective EEG information of the power industry practitioners in different work scenarios based on the tagging information; wherein, the effective EEG information of the power industry practitioners in different work scenarios is EEG data reflecting the changes in the psychological state of the power industry practitioners during the work process;

[0036] The data cleaning module is configured to clean the effective EEG information of the power industry practitioners in different work scenarios to obtain the target EEG information of the power industry practitioners in different work scenarios.

[0037] The data annotation module is configured to annotate the target EEG information of the power practitioners in different work scenarios with the information of the power practitioners.

[0038] The quantitative scoring module is configured to calculate a quantitative score for the psychological safety competence of power industry workers based on a predefined formula for different work scenarios. This score is then combined with target EEG information to obtain a quantitative data set of psychological safety competence for power industry workers in different work scenarios, represented as follows: in, This represents the target EEG information of the r-th power industry worker in work scenario m. This represents the quantitative score of the psychological safety awareness of the r-th power industry worker in work scenario m.

[0039] The machine learning module is configured to use a quantitative data set of the psychological safety qualities of power practitioners in different work scenarios to build a model, and after training, obtain an intelligent assessment model of the psychological safety qualities of power practitioners. The input of the intelligent assessment model of the psychological safety qualities of power practitioners is the target EEG information of the power practitioners, and the output is the psychological safety quality score of the power practitioners in the set work scenarios.

[0040] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the intelligent evaluation method for the psychological safety quality of power practitioners based on electroencephalogram (EEG) information as described in the first aspect and various possible designs of the first aspect.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the intelligent evaluation method for the psychological safety qualities of power industry practitioners based on electroencephalogram (EEG) information as described in the first aspect and various possible designs of the first aspect.

[0042] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the intelligent evaluation method for the psychological safety qualities of power industry practitioners based on electroencephalogram (EEG) information as described in the first aspect and various possible designs of the first aspect.

[0043] The intelligent evaluation method, device, equipment, and storage medium for the psychological safety awareness of power industry practitioners based on electroencephalogram (EEG) information provided in this application have at least the following beneficial effects:

[0044] This application enables intelligent and objective evaluation of the psychological safety qualities of power industry workers in various work scenarios through the collection of EEG information from power industry workers, the labeling of effective EEG information, the cleaning of EEG data, the annotation of EEG information from power industry workers, the quantification formula of psychological safety qualities of power industry workers in specific work scenarios based on prior knowledge, the intelligent assessment model of psychological safety qualities of power industry workers based on deep learning, and the evaluation of psychological safety qualities of power industry workers based on the intelligent assessment model of psychological safety qualities. This reduces the work risks of power industry workers. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] Figure 1 A flowchart illustrating an intelligent evaluation method for the psychological safety awareness of power industry practitioners based on electroencephalogram (EEG) information, provided as an embodiment of this application;

[0047] Figure 2 A structural block diagram of the intelligent assessment model for the psychological safety literacy of power industry practitioners provided in the embodiments of this application;

[0048] Figure 3 This is a structural diagram of a brain-like control network provided in an embodiment of this application.

[0049] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0051] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0052] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0053] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0054] This application provides an intelligent evaluation method for the psychological safety awareness of power industry practitioners based on electroencephalogram (EEG) information. For example...Figure 1 The diagram shown is a flowchart of an intelligent evaluation method for the psychological safety quality of power industry practitioners based on electroencephalogram (EEG) information, provided in an embodiment of this application. The intelligent evaluation method for the psychological safety quality of power industry practitioners based on EEG information includes the following steps S10-S50.

[0055] S10: Acquire EEG information of power industry practitioners in different work scenarios.

[0056] In this embodiment, an EEG acquisition device is used to collect the EEG signals of power industry workers under work-related stimuli. The collected EEG signals are represented according to the different numbers of channels in the acquisition device. Where m represents the work scenario, n represents the number of EEG channels used, and S m This represents the electroencephalogram (EEG) information of an electrical worker in the workplace, m. and These represent the EEG signals of the first, second, and nth EEG channels of an electrician in work scenario m, respectively.

[0057] S20: Based on the labeled information, extract the effective EEG information of the power practitioners in different work scenarios from the EEG information of the power practitioners in different work scenarios; wherein, the effective EEG information of the power practitioners in different work scenarios is EEG data reflecting the changes in the psychological state of the power practitioners during the work process.

[0058] In this embodiment, the labeling information can be obtained by annotating the collected EEG signals based on the experience and knowledge of professional psychological counselors, marking data from the raw signals within a time interval that reflects changes in the psychological state of power industry workers during their work process. For example, the labeling information includes at least one time period marker, which includes a start time and an end time; based on the start time and end time, effective EEG information of power industry workers in different work scenarios is extracted. Where S m (t1:t2) represents the effective EEG information of an electricity worker in work scenario m, where t1 represents the start time and t2 represents the end time. and These represent the EEG signals of the first, second, and nth EEG channels of an electrician in work scenario m, respectively.

[0059] S30: Clean the effective EEG information of the power industry practitioners in different work scenarios to obtain the target EEG information of the power industry practitioners in different work scenarios.

[0060] In this embodiment, the Kalman filter algorithm is used to process the valid EEG information marked in step two to remove the influence of artifacts such as electrooculography (EOG) and drift on the EEG information, and the expression of high-quality EEG information is obtained as follows: F s This represents the target EEG information of power industry practitioners in different work scenarios, that is, high-quality EEG signals after artifact removal, with n signal channels, and Kalman indicating Kalman filtering.

[0061] S40: Mark the information of the power industry practitioners in the target EEG information of the power industry practitioners in different work scenarios.

[0062] In this embodiment, the information of the electrical industry practitioners to whom the EEG data obtained in step 30 belongs can be statistically analyzed. This includes information such as practitioner's name, age, years of service, skill level (SKL), and psychological characteristics (PC) during work. This information is then compared with F... s Maintain the corresponding relationship.

[0063] S50: Based on the established quantitative formula for the psychological safety literacy of power industry practitioners under different work scenarios, a quantitative score for the psychological safety literacy of power industry practitioners is calculated according to their information. This quantitative score is then combined with target EEG information to obtain a set of quantitative data on the psychological safety literacy of power industry practitioners under different work scenarios, represented as follows: in, This represents the target EEG information of the r-th power industry worker in work scenario m. This represents the quantitative score of the psychological safety awareness of the r-th power industry worker in work scenario m.

[0064] In this embodiment, the method for determining the quantitative formula for the psychological safety quality of power practitioners under different work scenarios is as follows:

[0065] Using technical means such as power industry occupational evaluation standards, the knowledge and experience of power industry experts, and psychological expert assessments, the psychological safety qualities of power practitioners are quantified based on the relevant information collected in step S40. A quantitative formula for the psychological safety qualities of power practitioners in specific work scenarios is formulated and expressed as follows: Socr r This represents the psychological safety quality score obtained by the r-th power industry worker, and its quantitative value range is set in [1-100].

[0066] S60: Model the data set of quantitative information on the psychological safety qualities of power practitioners in different work scenarios, and obtain an intelligent assessment model of the psychological safety qualities of power practitioners after training; the input of the intelligent assessment model of the psychological safety qualities of power practitioners is the target EEG information of power practitioners, and the output is the psychological safety quality score of power practitioners in the set work scenarios.

[0067] like Figure 2 The diagram shown is a structural block diagram of the intelligent assessment model for the psychological safety literacy of power industry practitioners provided in this application embodiment. It includes the quantitative data set of the psychological safety literacy of power industry practitioners under work scenario m obtained in step S50. Modeling is performed. First, a convolutional neural network is used to extract features from multi-channel EEG signals. The expression is as follows: in This represents the EEG information features of the nth channel of an electricity worker r extracted through convolution operations under work scenario m; secondly, the extracted multi-channel features are fused to achieve a comprehensive representation of the EEG information of the electricity worker, expressed as follows: in This refers to a work scenario. m Chinese power practitioners r After fusing multi-channel EEG features, φ represents the feature splicing mapping. Then, the fused features... The expression in the input predictor is in Tanh ( · ) represents the regression function in the predictor. Finally, the above process is trained to obtain a deep learning-based intelligent assessment model for the psychological safety awareness of power industry practitioners.

[0068] The acquired intelligent assessment model for the psychological safety qualities of power industry workers can be used to evaluate their psychological safety qualities. For example, by collecting EEG information from power industry workers in a specific work scenario and processing it through steps S10-S30, the data is input into the intelligent assessment model for the psychological safety qualities of power industry workers acquired in step S60. This model intelligently assesses the psychological safety qualities of power industry workers and outputs a psychological safety quality score for each worker in that specific work scenario, thus achieving intelligent evaluation of their psychological safety qualities. In practical applications, for example, a scoring threshold can be set for a specific work scenario. If the psychological safety quality score output by the intelligent assessment model for that work scenario fails to reach the threshold, it is determined that the power industry worker is temporarily unable to work in that scenario. Only when the scoring threshold is reached can the power industry worker work in that scenario. The scoring threshold can be pre-collected by collecting EEG information from multiple power industry practitioners who are able to work normally in this work scenario. After processing through steps S10-S30, the scoring value is output by the intelligent assessment model of the psychological safety quality of power industry practitioners. The average of the scoring values ​​of the above multiple power industry practitioners is taken as the scoring threshold.

[0069] This application also provides an intelligent evaluation device for the psychological safety awareness of power industry practitioners based on electroencephalogram (EEG) information, such as... Figure 3 As shown, the intelligent evaluation device for the psychological safety awareness of power industry practitioners based on EEG information includes:

[0070] The data acquisition module 301 is configured to acquire electroencephalogram (EEG) information of power industry practitioners in different work scenarios;

[0071] The data tagging module 302 is configured to extract effective EEG information of the power industry practitioners in different work scenarios based on the tagging information; wherein, the effective EEG information of the power industry practitioners in different work scenarios is EEG data reflecting the changes in the psychological state of the power industry practitioners during the work process;

[0072] The data cleaning module 303 is configured to clean the effective EEG information of the power practitioners in different work scenarios to obtain the target EEG information of the power practitioners in different work scenarios.

[0073] Data annotation module 304 is configured to annotate the information of the power practitioners in the target EEG information of the power practitioners in different work scenarios;

[0074] The quantitative scoring module 305 is configured to calculate a quantitative score for the psychological safety qualities of power industry workers based on a pre-defined formula for different work scenarios. This score is then combined with target EEG information to obtain a set of quantitative data on the psychological safety qualities of power industry workers in different work scenarios, represented as follows: in, Indicates the first r A power industry professional in a work setting m The target EEG information, Indicates the first r A power industry professional in a work setting m The quantitative score of psychological safety awareness;

[0075] Machine learning module 306 is configured to use the quantitative data set of the psychological safety quality of power practitioners in different work scenarios to build a model, and obtain an intelligent assessment model of the psychological safety quality of power practitioners after training; the input of the intelligent assessment model of the psychological safety quality of power practitioners is the target EEG information of power practitioners, and the output is the psychological safety quality score of power practitioners in the set work scenarios.

[0076] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0077] The processor executes computer execution instructions stored in memory, causing the processor to perform the schemes in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0078] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0079] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0080] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the intelligent evaluation method for the psychological safety quality of power practitioners based on electroencephalogram (EEG) information described in the above embodiments.

[0081] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information in the above embodiments.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0083] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0084] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0085] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0086] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0087] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0088] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0089] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0090] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0091] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent evaluation of the psychological safety awareness of power industry practitioners based on electroencephalogram (EEG) information, characterized in that, The method includes: Acquire electroencephalogram (EEG) information of power industry workers in different work scenarios; Based on the labeled information, effective EEG information of the power industry practitioners in different work scenarios is extracted from their EEG information; wherein, the effective EEG information of the power industry practitioners in different work scenarios is EEG data reflecting the changes in the psychological state of the power industry practitioners during the work process; The effective EEG information of the power industry practitioners in different work scenarios is cleaned to obtain the target EEG information of the power industry practitioners in different work scenarios; The information of the power industry practitioners is marked in the target EEG information of the power industry practitioners in different work scenarios; Based on the established formula for quantifying the psychological safety awareness of power industry workers under different work scenarios, a quantitative score for their psychological safety awareness is calculated using their information. This quantitative score is then combined with target EEG information to obtain a set of quantitative data on the psychological safety awareness of power industry workers under different work scenarios, denoted as... ,in, Indicates the first A power industry professional in a work setting The target EEG information, Indicates the first A power industry professional in a work setting The quantitative score of psychological safety awareness; A model is built using a quantitative dataset of psychological safety qualities of power industry workers in different work scenarios. After training, an intelligent assessment model of psychological safety qualities of power industry workers is obtained. The input of the intelligent assessment model of psychological safety qualities of power industry workers is the target EEG information of power industry workers, and the output is the psychological safety quality score of power industry workers in the set work scenarios. The information of the power industry practitioners includes their name and age. Age Years of service Year Skill Level SKL and psychological characteristics at work PC The quantitative formula for the psychological safety quality of power practitioners under different work scenarios is expressed as follows: In the formula, Indicates the first The quantitative scores of psychological safety awareness obtained by power industry practitioners This represents a quantitative standard for psychological safety literacy scores based on human experience. A model was created using a quantitative dataset of psychological safety awareness among power industry workers in different work scenarios. After training, an intelligent assessment model for the psychological safety awareness of power industry workers was obtained, including: Using convolutional neural networks to study the first A power industry professional in a work setting Feature extraction is performed on the target EEG information to obtain the results in the work scenario. The following is a list of power industry practitioners extracted through convolution operations. No. Individual channel EEG information characteristics; Power industry practitioners By fusing the EEG information features from various channels, we can obtain data from power industry professionals. Multi-channel EEG characteristics enable comprehensive characterization of EEG information of power industry practitioners; Power industry practitioners In the multi-channel EEG feature input predictor, the output of power industry practitioners Psychological safety awareness assessment in workplace scenario m; The above process was trained to obtain an intelligent assessment model for the psychological safety awareness of power industry practitioners; Using convolutional neural networks to study the first A power industry professional in a work setting Feature extraction is performed on the target EEG information to obtain the results in the work scenario. The following is a list of power industry practitioners extracted through convolution operations. No. EEG information characteristics of each channel The calculation process is expressed as follows: In the formula, f CNN This represents the convolution operation. In the context of work Power industry practitioners No. EEG information from multiple channels; Power industry practitioners By fusing the EEG information features from various channels, we can obtain data from power industry professionals. The calculation expression for the multi-channel EEG features, which enables a comprehensive representation of the EEG information of power industry practitioners, is as follows: In the formula, In the context of work Chinese power practitioners Multichannel EEG characteristics. This represents a feature concatenation mapping. and These respectively represent the work scenario The following is a list of power industry practitioners extracted through convolution operations. The characteristics of EEG information from the first and second channels; Power industry practitioners In the multi-channel EEG feature input predictor, the output of power industry practitioners In the work environment m The formula for calculating the psychological safety quality score is as follows: In the formula, Indicates power industry practitioners In the work environment m The psychological safety quality score is as follows. This represents the regression function in the predictor.

2. The intelligent evaluation method for the psychological safety quality of power industry practitioners based on electroencephalogram (EEG) information according to claim 1, characterized in that, Based on the labeled information, effective EEG information of the power industry practitioners in different work scenarios is extracted, including: Obtain marker information, the marker information including at least one time period marker, the time period marker including a start time and an end time; Based on the aforementioned start and end times, effective EEG information of power industry practitioners in different work scenarios was extracted. ,in This indicates the work environment of power industry workers. m Effective EEG information in Indicates the start time. Indicates the end time. , and These represent the work scenarios of power industry workers. The brain signals of the first, second, and nth brain channels.

3. The intelligent evaluation method for the psychological safety quality of power industry practitioners based on electroencephalogram (EEG) information according to claim 2, characterized in that, The effective EEG information of the power industry practitioners in different work scenarios is cleaned to obtain the target EEG information of the power industry practitioners in different work scenarios. F s Represented as ,in Kalman This indicates Kalman filtering.

4. An intelligent evaluation device for the psychological safety awareness of power industry practitioners based on electroencephalogram (EEG) information, used to implement the method as described in any one of claims 1-3, characterized in that, The device includes: The data acquisition module is configured to acquire electroencephalogram (EEG) information of power industry workers in different work scenarios. The data tagging module is configured to extract effective EEG information of the power industry practitioners in different work scenarios based on the tagging information; wherein, the effective EEG information of the power industry practitioners in different work scenarios is EEG data reflecting the changes in the psychological state of the power industry practitioners during the work process; The data cleaning module is configured to clean the effective EEG information of the power industry practitioners in different work scenarios to obtain the target EEG information of the power industry practitioners in different work scenarios. The data annotation module is configured to annotate the target EEG information of the power practitioners in different work scenarios with the information of the power practitioners. The quantitative scoring module is configured to calculate a quantitative score for the psychological safety competence of power industry workers based on a predefined formula for different work scenarios. This score is then combined with target EEG information to obtain a quantitative data set of psychological safety competence for power industry workers in different work scenarios, represented as follows: ,in, Indicates the first A power industry professional in a work setting The target EEG information, Indicates the first A power industry professional in a work setting The quantitative score of psychological safety awareness; The machine learning module is configured to use a quantitative data set of the psychological safety qualities of power practitioners in different work scenarios to build a model, and after training, obtain an intelligent assessment model of the psychological safety qualities of power practitioners. The input of the intelligent assessment model of the psychological safety qualities of power practitioners is the target EEG information of the power practitioners, and the output is the psychological safety quality score of the power practitioners in the set work scenarios.

5. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information as described in any one of claims 1-3.

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