Intelligent evaluation method, device and equipment for psychological safety quality of electric power practitioner based on electroencephalogram information and storage medium

Abstract

The invention provides an intelligent evaluation method, device and equipment for psychological safety quality of an electric power practitioner based on electroencephalogram information and a storage medium. Relates to the technical field of electroencephalogram signals. The method comprises the steps that electroencephalogram information of an electric power practitioner in different working scenes is obtained, effective electroencephalogram information is extracted and then cleaned, and target electroencephalogram information is obtained; marking the information of the electric power practitioner in the target electroencephalogram information; based on a set psychological safety quality quantification formula of the electric power practitioner in different working scenes, calculating a 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; and modeling by using the quantitative data set of the psychological safety quality, and training to obtain an intelligent evaluation model of the psychological safety quality of the electric power practitioner. According to the invention, intelligent objective evaluation of the psychological safety quality of the power practitioner in various working scenes can be realized, and the working risk of the power practitioner is reduced.

Description

Technical Field

[0001] The present application relates to the field of electroencephalogram (EEG) signal technology, and in particular to an intelligent evaluation method, device, equipment, and storage medium for the psychological safety quality of power practitioners based on EEG information. Background Art

[0002] With the acceleration of social development, the demand for power industry professionals has increased significantly. However, the power industry is a high-risk field, and practitioners need to work in high-risk environments. Their psychological safety quality directly affects their operational safety and work efficiency. Therefore, assessing the psychological safety quality of power industry practitioners is crucial to reduce accidents and improve safety.

[0003] Currently, traditional evaluations of the psychological safety qualities of power industry practitioners rely primarily on questionnaires or observations, which are difficult to objectively reflect their psychological state. Furthermore, relying solely on manual psychological counselors to conduct psychological safety quality assessments means the number of counselors cannot meet the growing demand for power industry practitioners. The psychological characteristics of different power industry practitioners vary greatly, making it difficult for manual methods to provide personalized assessments of their psychological safety qualities. The existence of these problems has resulted in the current power system's inadequate ability to assess the psychological safety qualities of power industry practitioners, resulting in the continued existence of production risks caused by insufficient psychological safety qualities of power industry practitioners. Therefore, an intelligent evaluation method and system for the psychological safety qualities of power industry practitioners is urgently needed to replace traditional manual methods, improve the power system's objective ability to assess practitioners' psychological safety literacy, and reduce workplace risks. Summary of the Invention

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

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

[0006] Obtain EEG information of power workers in different working scenarios;

[0007] Based on the tag information, effective EEG information of the power practitioners in different work scenarios is extracted 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 changes in the psychological state of the power practitioners during work;

[0008] Cleaning the effective EEG information of the power practitioners in different work scenarios to obtain target EEG information of the power practitioners in different work scenarios;

[0009] Marking the information of the power practitioners in the target EEG information of the power practitioners in different work scenes;

[0010] Based on the quantitative formula of psychological safety quality of power practitioners in different working scenarios, the quantitative scores of psychological safety quality of power practitioners are calculated according to the information of power practitioners, and the quantitative scores of psychological safety quality of power practitioners are combined with the target EEG information to obtain the quantitative data set of psychological safety quality of power practitioners in different working scenarios, which is expressed as in, represents the target EEG information of the rth power worker in work scenario m, represents the quantitative score of psychological safety quality of the rth power practitioner in work scenario m;

[0011] The quantitative data set of psychological safety quality of power practitioners in different work scenarios is used to build a model, and an intelligent assessment model of psychological safety quality of power practitioners is obtained after training; the input of the intelligent assessment model of 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 scenario.

[0012] Furthermore, based on the tag information, effective EEG information of the power practitioners in different working scenarios is extracted from the EEG information of the power practitioners in different working scenarios, including:

[0013] Acquire marking information, where the marking information includes at least one time period mark, and the time period mark includes a start time and an end time;

[0014] Based on the start and end times, effective EEG information of power workers in different working scenarios is extracted. Among them S m (t1:t2) represents the effective EEG information of the power worker in the working scene m, t1 represents the start time, t2 represents the end time, and They respectively represent the EEG signals of the first EEG channel, the second EEG channel and the nth EEG channel of the power worker in work scenario m.

[0015] Furthermore, the effective EEG information of the power practitioners in different working scenes is cleaned, and the target EEG information F of the power practitioners in different working scenes is obtained. s Indicated as F s =Kalman[S m (t1:t2)], where Kalman represents Kalman filtering processing.

[0016] Furthermore, the information of the power practitioners includes name, age, years of experience, skill level SKL and psychological characteristics at work PC; the quantitative formula of the psychological safety quality of power practitioners in different work scenarios is expressed as:

[0017] Socr r =φ(Aag,Year,SKL,PC)

[0018] Where Socr r represents the quantitative score of psychological safety quality obtained by the rth power practitioner, and φ represents the quantitative standard of psychological safety quality score based on artificial experience.

[0019] Furthermore, the quantitative data set of psychological safety quality of power practitioners in different work scenarios is used to build a model, and an intelligent assessment model of psychological safety quality of power practitioners is obtained after training, including:

[0020] The convolutional neural network is used to extract the features of the target EEG information of the rth power worker in the work scene m, and the EEG information features of the nth channel of the power worker r are obtained through the convolution operation under the work scene m.

[0021] The EEG information features of each channel of the power practitioner r are fused to obtain the multi-channel EEG features of the power practitioner r, thus achieving a comprehensive representation of the EEG information of the power practitioner;

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

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

[0024] Furthermore, the convolutional neural network is used to extract the features of the target EEG information of the rth power practitioner in the work scene m, and the EEG information features of the nth channel of the power practitioner r extracted by the convolution operation in the work scene m are obtained. The calculation process is expressed as:

[0025]

[0026] Where, f CNN represents the convolution operation, Represents the EEG information of the nth channel of power worker r in work scenario m.

[0027] Furthermore, the EEG information features of each channel of the power worker r are fused to obtain the multi-channel EEG features of the power worker r. The calculation expression for the comprehensive representation of the EEG information of the power worker is:

[0028]

[0029] Where, represents the multi-channel EEG features of power worker r in work scene m, represents the feature concatenation mapping, and They represent the EEG information features of the first and second channels of power worker r extracted by convolution operation in work scene m respectively;

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

[0031]

[0032] Where, represents the psychological safety quality score of power practitioner r in work scenario m, and Tanh(·) represents the regression function in the predictor.

[0033] In a second aspect, the present application provides an intelligent evaluation device for the psychological safety quality of power practitioners based on EEG information, the device including a controller configured to:

[0034] A data acquisition module is configured to acquire EEG information of power practitioners in different working scenarios;

[0035] a data labeling module configured to extract effective EEG information of the power worker in different work scenarios from the EEG information of the power worker in different work scenarios based on the labeling information; wherein the effective EEG information of the power worker in different work scenarios is EEG data reflecting changes in the psychological state of the power worker during work;

[0036] a data cleaning module configured to clean the effective EEG information of the power practitioners in different work scenarios to obtain target EEG information of the power practitioners in different work scenarios;

[0037] a data annotation module configured to annotate information of the power practitioner in the target EEG information of the power practitioner in different work scenarios;

[0038] The quantitative scoring module is configured to calculate the psychological safety quality quantitative score of power practitioners based on the quantitative formula of psychological safety quality of power practitioners in different working scenarios, and combine the psychological safety quality quantitative score of power practitioners with the target EEG information to obtain the quantitative data set of psychological safety quality of power practitioners in different working scenarios, which is expressed as in, represents the target EEG information of the rth power worker in work scenario m, represents the quantitative score of psychological safety quality of the rth power practitioner in work scenario m;

[0039] The machine learning module 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 the power practitioners, and the output is the psychological safety quality score of the power practitioners in the set work scenario.

[0040] In a third aspect, an embodiment of the present application provides an electronic device comprising: at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information as described in the first aspect and various possible designs of the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, an intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information as described in the first aspect and various possible designs of the first aspect is implemented.

[0042] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information as described in the first aspect and various possible designs of the first aspect.

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

[0044] This application can realize intelligent and objective evaluation of the psychological safety quality of power practitioners in various work scenarios through the collection of EEG information of power practitioners, marking of effective EEG information, cleaning of EEG data information, annotation of EEG information of power practitioners, quantitative formula of psychological safety quality of power practitioners in specific work scenarios based on prior knowledge, intelligent evaluation model of psychological safety quality of power practitioners based on deep learning, and psychological safety quality evaluation of power practitioners based on intelligent evaluation model of psychological safety quality, thereby reducing the work risks of power practitioners. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0046] Figure 1 A flowchart of an intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information provided in an embodiment of the present application;

[0047] Figure 2 A structural diagram of the intelligent assessment model for psychological safety quality of power practitioners provided in an embodiment of the present application;

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

[0049] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0051] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of information such as financial data or user data involved 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 the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0053] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0054] The present application embodiment provides an intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information. Figure 1 As shown, it is a flow chart of an intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information provided in an embodiment of the present application. The intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information includes the following steps S10-S50.

[0055] S10: Obtain EEG information of power workers in different working scenarios.

[0056] In this embodiment, EEG signals of power workers under the stimulation of work scenes are collected using EEG acquisition equipment. The EEG signals collected according to the number of channels of the acquisition equipment are expressed as follows: Where m represents the working scenario, n represents the number of EEG device channels used, and S m represents the EEG information of power workers in work scene m, and They respectively represent the EEG signals of the first EEG channel, the second EEG channel and the nth EEG channel of the power worker in the work scene m.

[0057] S20: Based on the label 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 the EEG data reflecting the changes in the psychological state of the power practitioners during the work process.

[0058] In this embodiment, the tag information can be obtained by labeling the collected EEG signals based on the experience and knowledge of professional psychological counselors, and marking a time interval of data that can reflect the changes in the psychological state of power practitioners during the work process from the original signal. For example, the tag information includes at least one time period tag, and the time period tag includes a start time and an end time; based on the start time and the end time, the effective EEG information of power practitioners in different work scenarios is extracted. Among them S m (t1:t2) represents the effective EEG information of the power worker in the working scene m, t1 represents the start time, t2 represents the end time, and They respectively represent the EEG signals of the first EEG channel, the second EEG channel and the nth EEG channel of the power worker in work scenario m.

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

[0060] In this embodiment, the effective EEG information marked in step 2 is processed using the Kalman filter algorithm to remove the influence of artifacts such as electrooculogram and drift on the EEG information, and the expression of high-quality EEG information is obtained as follows: F s It represents the target EEG information of power practitioners in different working scenarios, that is, the high-quality EEG signal after removing artifacts. The number of signal channels is n, and Kalman represents Kalman filtering processing.

[0061] S40: Marking the information of the power practitioner in the target EEG information of the power practitioner in different work scenes.

[0062] In this embodiment, statistics can be collected on the power practitioners to whom the EEG data obtained in step 30 belong, including the practitioner's name, age, years of service, skill level SKL, psychological characteristics PC at work, etc., and these information can be compared with F s Maintain the corresponding relationship.

[0063] S50: Based on the set quantitative formula for the psychological safety quality of power practitioners in different work scenarios, the quantitative score of the psychological safety quality of power practitioners is calculated according to the information of the power practitioners, and the quantitative score of the psychological safety quality of power practitioners is combined with the target EEG information to obtain the quantitative data set of the psychological safety quality of power practitioners in different work scenarios, which is expressed as in, represents the target EEG information of the rth power worker in work scenario m, It represents the quantitative score of psychological safety quality of the r-th power practitioner in work scenario m.

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

[0065] The psychological safety quality of power practitioners is quantified based on the relevant information collected in step S40 using technical means such as power occupation evaluation standards, power industry expert knowledge and experience, and psychological expert evaluation. A quantitative formula for the psychological safety quality of power practitioners in a specific work scenario is formulated, which is expressed as Among them, Socr r It represents the quantitative score of psychological safety quality obtained by the r-th power practitioner, and its quantitative value range is set in [1-100].

[0066] S60: Modeling is performed using the quantitative data set of the psychological safety quality of power practitioners in the different work scenarios, and an intelligent assessment model of the psychological safety quality of power practitioners is obtained after training; the input of the intelligent assessment model of the psychological safety quality 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 scenario.

[0067] like Figure 2 The figure shows the structure of the intelligent assessment model of psychological safety quality of power practitioners provided by the embodiment of the present application. First, the convolutional neural network is used to extract the features of multi-channel EEG signals. The expression is: in It represents the EEG information feature of the nth channel of the power worker r extracted by convolution operation under the working scene m. Secondly, the extracted multi-channel features are fused to achieve a comprehensive representation of the EEG information of the power worker, which is expressed as in Indicates the working scene m Chinese power industry practitioners r After the fusion of multi-channel EEG features, φ represents the feature splicing map. 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 an intelligent assessment model for the psychological safety quality of power practitioners based on deep learning.

[0068] The obtained intelligent assessment model for the psychological safety quality of power practitioners can be used to implement psychological safety quality assessments for power practitioners. For example, EEG information of power practitioners in a specific work scenario is collected and processed through steps S10-S30. The resulting data is input into the intelligent assessment model for the psychological safety quality of power practitioners obtained in step S60. This intelligent assessment of the psychological safety quality of power practitioners is then performed, and a psychological safety quality score for the power practitioners in the specific work scenario is output, thereby achieving an intelligent evaluation of the psychological safety quality of power practitioners. In a specific application, for example, a scoring threshold is set in a specific work scenario. When the psychological safety quality score for the work scenario output by the intelligent assessment model for the psychological safety quality of power practitioners fails to meet the scoring threshold, it is determined that the power practitioner is temporarily unable to work in this work scenario. Only when the scoring threshold is met can the power practitioner work in this work scenario. The scoring threshold can be determined in advance by collecting EEG information of multiple power practitioners who can work normally in this work scenario, and after processing in steps S10-S30, using the intelligent assessment model for the psychological safety quality of power practitioners to output the scoring value, and taking the average of the scoring values of the above multiple power practitioners as the scoring threshold.

[0069] The present application also provides an intelligent evaluation device for the psychological safety quality of power practitioners based on EEG information, such as Figure 3 As shown, the intelligent evaluation device for psychological safety quality of power practitioners based on EEG information includes:

[0070] The data acquisition module 301 is configured to acquire EEG information of power practitioners in different working scenarios;

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

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

[0073] The data annotation module 304 is configured to annotate the target EEG information of the power practitioners in different working scenarios with the information of the power practitioners;

[0074] The quantitative scoring module 305 is configured to calculate the psychological safety quality quantitative score of the power practitioners based on the quantitative formula of the psychological safety quality of the power practitioners in different work scenarios according to the information of the power practitioners, and combine the psychological safety quality quantitative score of the power practitioners with the target EEG information to obtain the quantitative data set of the psychological safety quality of the power practitioners in different work scenarios, which is expressed as in, Indicates the r Power workers at work m The target EEG information under Indicates the r Power workers at work m Quantitative score of psychological safety quality under

[0075] The machine learning module 306 is configured to use the quantitative data set of the psychological safety quality of power practitioners in the 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 the power practitioners, and the output is the psychological safety quality score of the power practitioners in the set work scenario.

[0076] An embodiment of the present application provides an electronic device, which may include a processor and a memory, wherein the processor and the memory can communicate with each other; illustratively, the processor and the memory communicate with each other via a communication bus.

[0077] The processor executes the computer-executable instructions stored in the memory, so that the processor implements the solutions 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. System buses can be categorized as address buses, data buses, and control buses. Transceivers enable communication between the database access device and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) or non-volatile memory.

[0079] The electronic device provided in the embodiment of the present application may be the terminal device of the above embodiment.

[0080] An embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the technical solution of the above-mentioned embodiment of the intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information.

[0081] An embodiment of the present 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 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-mentioned embodiment.

[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 example, the division of modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0083] Modules described as separate components may or may not be physically separate, and 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 elements. Some or all of these modules may be selected to implement the solution of this embodiment based on actual needs.

[0084] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each module may exist physically separately, or two or more modules may be integrated into a single unit. The above-mentioned modules may be implemented in the form of hardware or hardware plus software functional units.

[0085] The above-mentioned integrated module implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some steps of the methods of various embodiments of the present application.

[0086] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASICs). A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0087] The memory may include a high-speed RAM memory, and may also include non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk.

[0088] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, and control buses.

[0089] The storage medium may be implemented by any type of volatile or non-volatile memory 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 may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0090] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic control unit or a main control device.

[0091] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with 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. 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 the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent evaluation method for the psychological safety quality of power practitioners based on EEG information, characterized by: The method comprises: Obtain EEG information of power workers in different working scenarios; Based on the tag information, effective EEG information of the power practitioners in different work scenarios is extracted 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 changes in the psychological state of the power practitioners during work; Cleaning the effective EEG information of the power practitioners in different work scenarios to obtain target EEG information of the power practitioners in different work scenarios; Marking the information of the power practitioners in the target EEG information of the power practitioners in different work scenes; Based on the quantitative formula of psychological safety quality of power practitioners in different working scenarios, the quantitative scores of psychological safety quality of power practitioners are calculated according to the information of power practitioners, and the quantitative scores of psychological safety quality of power practitioners are combined with the target EEG information to obtain the quantitative data set of psychological safety quality of power practitioners in different working scenarios, which is expressed as in, represents the target EEG information of the rth power worker in work scenario m, represents the quantitative score of psychological safety quality of the rth power practitioner in work scenario m; The quantitative data set of psychological safety quality of power practitioners in different work scenarios is used to build a model, and an intelligent assessment model of psychological safety quality of power practitioners is obtained after training; the input of the intelligent assessment model of 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 scenario.

2. The intelligent evaluation method for psychological safety quality of power practitioners based on EEG information according to claim 1 is characterized in that: Based on the tag information, effective EEG information of the power practitioners in different working scenarios is extracted from the EEG information of the power practitioners in different working scenarios, including: Acquire marking information, where the marking information includes at least one time period mark, and the time period mark includes a start time and an end time; Based on the start and end times, effective EEG information of power workers in different working scenarios is extracted. Among them S m (t1:t2) represents the effective EEG information of the power worker in the working scene m, t1 represents the start time, t2 represents the end time, and They respectively represent the EEG signals of the first EEG channel, the second EEG channel and the nth EEG channel of the power worker in work scenario m.

3. The intelligent evaluation method for psychological safety quality of power practitioners based on EEG information according to claim 2 is characterized in that: The effective EEG information of the power practitioners in different working scenes is cleaned to obtain the target EEG information F of the power practitioners in different working scenes. s Indicated as F s =Kalman[S m (t1:t2)], where Kalman represents Kalman filtering processing.

4. The intelligent evaluation method for psychological safety quality of power practitioners based on EEG information according to claim 1 is characterized in that: The information of the power practitioners includes name, age, years of experience, skill level SKL and psychological characteristics at work PC; the quantitative formula of the psychological safety quality of power practitioners in different work scenarios is expressed as: Where Socr r represents the quantitative score of psychological safety quality obtained by the rth power practitioner, It represents the quantitative standard of psychological safety quality score based on human experience.

5. The intelligent evaluation method for psychological safety quality of power practitioners based on EEG information according to claim 1 is characterized in that: The model is built using the quantitative data set of psychological safety quality of power practitioners in different work scenarios. After training, an intelligent assessment model of psychological safety quality of power practitioners is obtained, which includes: The convolutional neural network is used to extract the features of the target EEG information of the rth power worker in the work scene m, and the EEG information features of the nth channel of the power worker r are obtained through the convolution operation under the work scene m. The EEG information features of each channel of the power practitioner r are fused to obtain the multi-channel EEG features of the power practitioner r, thus achieving a comprehensive representation of the EEG information of the power practitioner; Input the multi-channel EEG features of power worker r into the predictor, and output the psychological safety quality score of power worker r in work scenario m; The above process is trained to obtain an intelligent assessment model for the psychological safety quality of power practitioners.

6. The intelligent evaluation method for psychological safety quality of power practitioners based on EEG information according to claim 5 is characterized in that: The convolutional neural network is used to extract the target EEG information of the rth power practitioner in the work scene m, and the EEG information feature of the nth channel of the power practitioner r is obtained by the convolution operation under the work scene m. The calculation process is expressed as: Where, f CNN represents the convolution operation, Represents the EEG information of the nth channel of power worker r in work scenario m.

7. The intelligent evaluation method for psychological safety quality of power practitioners based on EEG information according to claim 5 is characterized in that: The EEG information features of each channel of the power worker r are integrated to obtain the multi-channel EEG features of the power worker r. The calculation expression for the comprehensive representation of the EEG information of the power worker is: Where, represents the multi-channel EEG features of power worker r in work scene m, φ represents the feature concatenation mapping, and They represent the EEG information features of the first and second channels of power worker r extracted by convolution operation in work scene m respectively; The multi-channel EEG features of power worker r are input into the predictor, and the calculation expression of the output psychological safety quality score of power worker r in work scenario m is: Where, represents the psychological safety quality score of power practitioner r in work scenario m, and Tanh(·) represents the regression function in the predictor.

8. An intelligent evaluation device for the psychological safety quality of power practitioners based on EEG information, characterized by: The device comprises: A data acquisition module is configured to acquire EEG information of power practitioners in different working scenarios; a data labeling module configured to extract effective EEG information of the power worker in different work scenarios from the EEG information of the power worker in different work scenarios based on the labeling information; wherein the effective EEG information of the power worker in different work scenarios is EEG data reflecting changes in the psychological state of the power worker during work; a data cleaning module configured to clean the effective EEG information of the power practitioners in different work scenarios to obtain target EEG information of the power practitioners in different work scenarios; a data annotation module configured to annotate information of the power practitioner in the target EEG information of the power practitioner in different work scenarios; The quantitative scoring module is configured to calculate the psychological safety quality quantitative score of power practitioners based on the quantitative formula of psychological safety quality of power practitioners in different working scenarios, and combine the psychological safety quality quantitative score of power practitioners with the target EEG information to obtain the quantitative data set of psychological safety quality of power practitioners in different working scenarios, which is expressed as in, Indicates the r Power workers at work m The target EEG information under Indicates the r Power workers at work m Quantitative score of psychological safety quality under The machine learning module 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 the power practitioners, and the output is the psychological safety quality score of the power practitioners in the set work scenario.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable 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 to 7.

10. 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 to 7.

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