Intelligent vocational ability assessment method and device, equipment and storage medium

Abstract

The invention discloses an intelligent occupational ability assessment method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining user basic information of a to-be-evaluated user, and generating a user basic information tag based on the user basic information; based on a pre-trained occupational interest evaluation model and a workplace character evaluation model, obtaining an occupational adaptation type label, a workplace character type label and a psychological state type label; importing the user basic information tag, the occupational adaptation type tag, the workplace character type tag and the psychological state type tag into an occupational adaptation degree model to obtain an occupational ability adaptation degree tag and a psychological state adaptation degree tag; and importing the vocational ability adaptation degree tag and the psychological state adaptation degree tag into a vocational recommendation degree model to obtain a vocational recommendation result of the to-be-evaluated user. The invention further relates to the technical field of block chains, and the user information is stored on a block chain network. According to the method and the device, personalized evaluation labels and recommendation results can be dynamically generated.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence technology and is applicable to the financial field. Specifically, it relates to an intelligent professional competency assessment method, device, equipment and storage medium. Background Art

[0002] Professional competency assessment is particularly important in today's highly competitive workplace. With the continuous development of the industry and changes in market demand, companies need to find outstanding talents suitable for their own development in order to improve the overall efficiency and competitiveness of the team. However, traditional recruitment methods often fail to accurately identify the potential and suitability of candidates, which leads to frequent mismatches between people and positions, which not only increases the recruitment costs of companies, but also affects employees' job satisfaction and career development. Therefore, professional competency assessment came into being, aiming to fully understand the candidates' career interests, personality traits and psychological states through scientific testing and analysis. Through these assessments, companies can more effectively match talents with job requirements and achieve a more efficient recruitment process.

[0003] Most existing methods of career interest assessment rely on standardized questionnaire questions and cumulative scoring mechanisms. Although they can reveal an individual's career inclinations to a certain extent, this method is difficult to capture the uniqueness and complexity of each respondent due to the fixed question settings. It also ignores the diversity and dynamic changes of individuals in terms of career abilities, deep-seated interests, and workplace psychological states, resulting in assessment results that are often one-sided and lack personalized guidance value. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose an intelligent career ability assessment method, device, computer equipment and storage medium to solve the technical problems that existing career interest assessments have fixed questions and lack of personalization, and are unable to fully reflect the individual's complex career abilities, interests and workplace psychological states, resulting in assessment results that are often one-sided and lack personalized guidance value.

[0005] In order to solve the above technical problems, the present application embodiment provides an intelligent professional ability assessment method, which adopts the following technical solutions:

[0006] An intelligent professional ability assessment method, comprising:

[0007] Obtain the basic information of the user to be evaluated, and generate a basic information tag based on the basic information of the user;

[0008] Import the user's basic information into the pre-trained career interest assessment model to obtain the career adaptation type label;

[0009] Import the user's basic information into the pre-trained workplace personality assessment model to obtain workplace personality type labels and psychological state type labels;

[0010] Import the user's basic information label, career adaptation type label, workplace personality type label, and psychological state type label into the pre-trained career adaptation model to obtain the career ability adaptation label and psychological state adaptation label;

[0011] The occupational ability suitability labels and psychological state suitability labels are imported into the pre-trained occupational recommendation model to obtain the occupational recommendation results for the user to be evaluated.

[0012] Furthermore, the steps of obtaining basic user information of the user to be evaluated and generating a basic user information tag based on the basic user information specifically include:

[0013] Receive the professional competency assessment instruction and obtain the basic user information of the user to be assessed from the preset database;

[0014] Perform data classification on the user's basic information to obtain the basic information classification result;

[0015] Generate user basic information labels based on the basic information classification results.

[0016] In order to solve the above technical problems, the embodiment of the present application also provides an intelligent professional ability assessment device, which adopts the following technical solution:

[0017] An intelligent professional ability assessment device, comprising:

[0018] A user information processing module is used to obtain the basic information of the user to be evaluated and generate a basic information tag based on the basic information of the user;

[0019] The career interest assessment module is used to import the user's basic information into the pre-trained career interest assessment model to obtain the career adaptation type label;

[0020] The workplace personality assessment module is used to import the user's basic information into the pre-trained workplace personality assessment model to obtain workplace personality type labels and psychological state type labels;

[0021] The career suitability module is used to import the user's basic information label, career suitability type label, workplace personality type label and psychological state type label into the pre-trained career suitability model to obtain the career ability suitability label and psychological state suitability label;

[0022] The career recommendation module is used to import the career ability suitability labels and psychological state suitability labels into the pre-trained career recommendation model to obtain the career recommendation results for the user to be evaluated.

[0023] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0024] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of any of the above-mentioned intelligent professional competence assessment methods when executing the computer-readable instructions.

[0025] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0026] A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of any of the above-described intelligent professional competence assessment methods.

[0027] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0028] The present application discloses an intelligent professional ability assessment method, device, equipment and storage medium, which belongs to the field of artificial intelligence technology. The present application introduces multi-dimensional labels such as basic information labels, professional interest labels, workplace personality labels and psychological state labels of users, and uses pre-trained professional interest assessment models, workplace personality assessment models and professional adaptability models for layer-by-layer analysis, thereby realizing a multi-dimensional comprehensive assessment of the user's professional ability. Compared with the existing professional interest assessment, the scheme of the present application breaks the limitations of the traditional fixed assessment questions and lack of personalization. By analyzing the individual characteristics of users in various aspects, personalized assessment labels and recommendation results can be dynamically generated, effectively making up for the shortcomings of the one-sidedness of the existing assessment results. The present application can not only more comprehensively reflect the user's professional interests and workplace psychological state, but also provide personalized career recommendations based on the adaptability of professional ability and psychological state, so that the assessment results are more instructive and have practical reference significance, which helps users to more scientifically choose the career development direction that meets their own characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 An exemplary system architecture diagram is shown in which the present application can be applied;

[0031] Figure 2 A flow chart showing an embodiment of an intelligent professional competence assessment method according to the present application is shown;

[0032] Figure 3 A schematic diagram of the structure of an embodiment of an intelligent professional ability assessment device according to the present application is shown;

[0033] Figure 4 A schematic structural diagram of an embodiment of a computer device according to the present application is shown. DETAILED DESCRIPTION

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0035] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0037] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0038] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0039] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0040] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0041] It should be noted that the intelligent professional ability assessment method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the intelligent professional ability assessment device is generally set in the server / terminal device.

[0042] It should be understood that Figure 1 The numbers of terminal devices, networks and servers in the embodiment are merely illustrative. The above system may have any number of terminal devices, networks and servers according to implementation requirements.

[0043] Continue to refer Figure 2 , showing a flow chart of an embodiment of a method for intelligent professional ability assessment according to the present application. The intelligent professional ability assessment method comprises the following steps:

[0044] S201, obtaining basic user information of a user to be evaluated, and generating a basic user information tag based on the basic user information.

[0045] Specifically, step S201 mainly constructs the personalized input of the evaluation by collecting the basic data of the user, and then generates the basic information label of the user with identification function. The basic information of the user may include the user's age, gender, educational background, work experience, professional skills, hobbies, growth experience, etc. In the process of obtaining this data, the system will automatically classify and encode it according to the characteristics of different users, and convert it into labeled data so that the model can better process and understand the user information.

[0046] The labeling process usually involves structuring and simplifying the original information, and extracting important features from a large amount of user information through coding and labeling, such as "technical talent" and "strong communication skills". This labeling process can provide basic data for interest assessment, personality analysis, etc., thereby achieving accurate assessment of individual characteristics. This process not only helps to filter out noise data and make the model more efficient, but also provides key trait markers for subsequent career recommendations, ensuring the personalization and accuracy of the assessment.

[0047] S202, importing the user's basic information into a pre-trained career interest assessment model to obtain a career adaptation type label.

[0048] Specifically, the goal of step S202 is to convert the user's basic information label into a career adaptation type label through the career interest assessment model. The career interest assessment model has been trained with a large number of samples and has the ability to associate and match user characteristics with career types. After entering the user's basic information, the career interest assessment model will analyze the user's interest tendencies and career preferences. The career adaptation type labels output based on the interest matching model will indicate the user's potential interests in different career fields. For example, the model generates labels such as "suitable for technology research and development" or "good at data analysis" by analyzing the user's technical background and innovation tendency. This label can reflect the user's degree of adaptation in various professions and help determine whether their interest areas are in line with specific career directions. The career interest assessment model has adaptive capabilities and can make personalized judgments based on the characteristics of different users, making the career interest assessment results more objective and scientific, avoiding the disadvantages of traditional assessments that lack personalization due to fixed questions.

[0049] The career adaptation type label refers to the label representing the user's career interest and adaptability generated after analyzing the user's basic information through the career interest assessment model. The label reflects the user's potential interest and matching degree in different career fields, and helps predict the user's possible performance in a specific career. For example, for users with strong logical thinking and attention to details, the model may generate labels such as "suitable for data analysis" or "good at financial risk control"; while for users with extroverted personalities and outstanding communication skills, the label may show "suitable for marketing" or "have sales potential."

[0050] For example, when recruiting insurance sales personnel, a pre-trained career interest assessment model can be used to help screen insurance sales candidates. For example, the candidate's basic information (such as age, education, past sales experience, communication skills, etc.) is imported into the career interest assessment model, which will combine these data to analyze the candidate's interests and personality to identify whether he or she is suitable for insurance sales. The model will give labels of "high fit", "medium fit" or "low fit" based on the candidate's communication skills, emotional control, customer service awareness and other characteristics. In this way, recruiters can more efficiently evaluate the match between candidates and insurance sales positions, and give priority to interviewing candidates who are assessed as "high fit" by the model, thereby improving recruitment efficiency and the subsequent job stability of employees.

[0051] S203, importing the user's basic information into a pre-trained workplace personality assessment model to obtain a workplace personality type label and a psychological state type label.

[0052] Specifically, step S203 is to obtain workplace personality type labels and psychological state type labels based on user basic information through the workplace personality assessment model. The workplace personality assessment model is pre-trained and can identify and classify the personality traits of users, such as extroversion or introversion, stability or volatility, innovation or execution, etc. In addition, the model can also judge its current psychological state, such as anxiety, pressure, enthusiasm, etc., by analyzing user information. The core of this link is to refine the personality characteristics of users through workplace personality labels, and clarify the performance characteristics of users in the workplace, such as whether they are good at teamwork, whether they have strong leadership or innovation. At the same time, the psychological state label reflects the possible emotional state and psychological endurance of users in the workplace, and provides emotional judgment basis for career recommendations. Through this multi-dimensional label generation method, the system can understand the personality characteristics and emotional tendencies of users at a deeper level, enhance the analysis accuracy of the adaptation model, and thus lay the foundation for the evaluation of career adaptation.

[0053] Workplace personality type labels and psychological state type labels are two types of labels obtained by analyzing the user's personality traits and psychological state, which are used to more carefully reflect the user's behavioral tendencies and emotional states in the work environment. Workplace personality type labels describe the user's personality traits in the workplace, such as "extroverted", "cooperative" or "leadership", which can show whether the user is suitable for teamwork, management or independent work. Psychological state type labels reflect the user's current emotions and psychological tolerance, such as "stable", "high-pressure tolerance" or "anxiety tendency", which are used to assess whether the user can adapt to a high-intensity workplace environment. For example, a "cooperative" and "high-pressure tolerance" user may be suitable for a team management role, while an "independent" and "stable" user may be more suitable for a research position.

[0054] For example, in the above embodiment, the pre-trained workplace personality assessment model can be used to help the recruiter understand the candidate's workplace personality and psychological state more comprehensively. For example, the candidate's basic information (such as age, educational background, past sales experience, self-description, etc.) is imported into the model, and the model can analyze the candidate's workplace personality type (such as "social", "executive", "stress-resistant", etc.) and psychological state type (such as "positive", "stable", "easy to be anxious", etc.). If a candidate is rated as "social, stress-resistant, and psychologically stable", it means that he or she may have strong communication and stress-resistant abilities, and is suitable for the challenging environment of insurance sales. In this way, recruiters can screen out candidates with greater potential and better psychological qualities, thereby improving the quality of recruitment and the fit of employees' positions.

[0055] S204, importing the user's basic information label, career adaptation type label, workplace personality type label and psychological state type label into the pre-trained career adaptation model to obtain a career ability adaptation label and a psychological state adaptation label.

[0056] Specifically, the focus of step S204 is to input the multi-dimensional user tags into the career adaptability model, so as to generate the career ability adaptability tags and the psychological state adaptability tags. After learning a large number of training samples, the career adaptability model can analyze the ability adaptability and psychological matching of users in specific career situations. The input tag information covers the user's basic attributes, career interests, personality traits and psychological state. The model will comprehensively judge whether the user has the ability required for a specific career and whether he is psychologically adapted to the career based on these data. For example, the system may conclude that a user is suitable for a high-intensity, high-pressure career, while other users are more suitable for working in a stable environment. The generation of career ability adaptability tags and psychological state adaptability tags enables the system to match the user's ability characteristics with the needs of different careers. Through this comprehensive evaluation, the career recommendation results can more accurately reflect the user's actual adaptability.

[0057] The occupational ability suitability label and the psychological state suitability label are labels used to evaluate the user's actual ability match and psychological state adaptability in a certain occupation. The occupational ability suitability label measures the degree of conformity of the user's skills, knowledge, etc. with the needs of a specific occupation. For example, "highly adaptable to data analysis" or "suitable for creative design", this label indicates the user's competence in these occupational fields. The psychological state suitability label evaluates the user's psychological endurance and emotional adaptability in the occupational environment, such as "suitable for high-pressure environment" or "more suitable for stable environment". For example, a user who is "highly adaptable to sales" and "suitable for high-pressure environment" is suitable for challenging sales jobs, while a user who is "suitable for technical research and development" and "suitable for stable environment" may be more suitable for long-term research positions.

[0058] For another example, in the above embodiment, the recruiter can further refine the evaluation of the candidate through the career suitability model. First, the candidate's basic information labels (such as age, education, sales experience), career suitability type labels (such as "high suitability"), workplace personality type labels (such as "social type" and "stress-resistant type"), and psychological state type labels (such as "stable") are input into the pre-trained career suitability model. The model will comprehensively analyze these labels and give the candidate's "career ability suitability" and "psychological state suitability" labels for the insurance sales position. For example, the model evaluates the candidate's career ability suitability as "high" and the psychological state suitability as "relatively high", indicating that the candidate not only has the communication and stress resistance required for the position, but also adapts to the pressure and challenges of sales work in terms of psychological state. Based on this, the recruiter can give priority to candidates with high suitability to improve the success rate of recruitment and the job stability of employees.

[0059] S205, importing the occupational ability suitability label and the psychological state suitability label into the pre-trained occupation recommendation model to obtain the occupation recommendation result of the user to be evaluated.

[0060] Specifically, step S205 is to generate the final career recommendation result using the career recommendation model. This step combines the analysis results of the previous labels and provides targeted career choice suggestions. After receiving the career ability adaptation and psychological state adaptation labels, the career recommendation model can make intelligent recommendations based on the comprehensive characteristics of the user. The model will match the career path suitable for the user's characteristics through existing data and rules, and form a number of candidate careers or career directions for reference. For example, for users who are psychologically stable and adapt to stress, the model may recommend more challenging career positions, while for users who prefer creativity and freedom, it will recommend career choices in the direction of design, innovation, etc. The career recommendation results can provide dynamic and personalized career guidance based on the actual situation of the user. This not only helps users find career directions that meet their own characteristics, but also increases the credibility and practicality of the recommendation results, and realizes comprehensive and personalized career matching guidance.

[0061] Furthermore, the steps of obtaining basic user information of the user to be evaluated and generating a basic user information tag based on the basic user information specifically include:

[0062] Receive the professional competency assessment instruction and obtain the basic user information of the user to be assessed from the preset database;

[0063] Perform data classification on the user's basic information to obtain the basic information classification result;

[0064] Generate user basic information labels based on the basic information classification results.

[0065] In this embodiment, by receiving the professional competency assessment instruction, the system first retrieves the basic information of the user to be assessed from the preset database, such as age, education, skills, and work experience, etc. Then, the basic information is systematically classified and sorted, and the user characteristics are subdivided into specific categories, such as "technical" or "management" labels.

[0066] Through the above steps, accurate classification and labeling of user basic information are achieved, which improves the structure and standardization of data.

[0067] Furthermore, the career interest assessment model is built based on a deep neural network. Before the step of importing the user's basic information into the pre-trained career interest assessment model to obtain the career adaptation type label, it also includes:

[0068] Acquire first training data, wherein the first training data includes first historical user information and matching historical occupation type labels;

[0069] Importing the first training data into a career interest assessment model, wherein the career interest assessment model includes a first input layer, a hidden layer, and an output layer;

[0070] Receiving first training data through a first input layer, and converting first historical user information into a feature vector recognizable by the model;

[0071] The feature vector is nonlinearly transformed through the hidden layer to extract the deep features of the first historical user information, and the feature relationship between the deep features and the preset occupation type label is learned;

[0072] Through the output layer, output the occupation adaptation type prediction label corresponding to the first historical user information based on the feature relationship;

[0073] Based on the occupational adaptation type prediction labels and historical occupational type labels, the occupational interest assessment model is iteratively trained until the model is fitted to obtain a pre-trained occupational interest assessment model.

[0074] In this embodiment, in order to build a career interest assessment model with high accuracy, the system first obtains a large amount of first training data, covering rich historical user information and corresponding career type labels. The system imports these data into the career interest assessment model so that the model can understand and map the relationship between user characteristics and career interests through deep learning. The career interest assessment model is constructed based on a deep neural network structure, specifically including an input layer, multiple hidden layers and an output layer. First, the input layer receives the first historical user information and converts it into a numerical feature vector. Next, the hidden layer performs multi-layer nonlinear transformation on these feature vectors to gradually extract the deep-level features of the user information, such as specific career tendencies or interest dimensions. These hidden layers refine and enhance the relationship between the input information and career interests through progressive calculations, and then construct a high-dimensional feature expression. In the output layer, the model will generate a career adaptation type prediction label based on the extracted features, and compare it with the historical career type label in the training data to calculate the model error. Through repeated iterative training, the model gradually optimizes and tunes the parameters so that the predicted label fits the historical label better until the preset accuracy is reached, and finally a deeply trained career interest assessment model is obtained.

[0075] Through the above steps, the knowledge of historical data can be deeply learned into the model, so that the prediction results are more in line with the real user characteristics and the pre-training of the career interest assessment model is completed.

[0076] Furthermore, the workplace personality assessment model is built based on the pre-trained BERT model. Before the step of importing the user's basic information into the pre-trained workplace personality assessment model to obtain the workplace personality type label and the psychological state type label, it also includes:

[0077] Acquire second training data, wherein the second training data includes second historical user information and matching workplace behavior descriptions;

[0078] Importing the second training data into the workplace personality assessment model, wherein the workplace personality assessment model includes a second input layer, a first fully connected layer, and a second fully connected layer;

[0079] Performing feature encoding on the second historical user information through a second input layer to obtain a first embedding vector;

[0080] The workplace behavior description is segmented through the second input layer, and the obtained text segmentation is feature encoded to obtain the second embedding vector;

[0081] Concatenate the first embedding vector and the second embedding vector to obtain a concatenated embedding vector;

[0082] Import the concatenated embedding vector into the first fully connected layer and the second fully connected layer respectively, obtain the probability distribution of workplace personality type output by the first fully connected layer, and obtain the probability distribution of psychological state type output by the second fully connected layer;

[0083] The workplace personality assessment model is made to learn the probability distribution of workplace personality types and the probability distribution of psychological state types respectively, and a pre-trained workplace personality assessment model is obtained.

[0084] In this embodiment, in order to establish an accurate workplace personality assessment model, the system first obtains the second training data, which includes a large amount of historical user information and matching workplace behavior descriptions. These data help the model understand the relationship between behavioral performance and personality traits in the workplace. The workplace personality assessment model is based on the pre-trained BERT model, and mainly extracts and learns the input information through deep learning technology. The input data is divided into two parts: the second historical user information and the workplace behavior description. First, the second input layer performs feature encoding on the second historical user information and converts it into a first embedding vector; at the same time, the workplace behavior description is processed by word segmentation to obtain a second embedding vector. Then, these two embedding vectors are spliced ​​into a more comprehensive spliced ​​embedding vector as the input of the model. Then, the spliced ​​embedding vector is sent to two fully connected layers to calculate the probability distribution of workplace personality type and psychological state type respectively. These two fully connected layers optimize the prediction results of workplace personality and psychological state respectively. After multiple iterations and optimizations, the model can accurately learn the complex relationship between workplace personality characteristics and psychological state. Through this training process, the workplace personality assessment model is gradually improved and will eventually be able to output more accurate workplace personality type and psychological state type labels.

[0085] Through the above steps, a comprehensive analysis of workplace personality and psychological state is achieved. By combining historical user data and workplace behavior, the accuracy and personalization of the assessment are improved, making the workplace personality assessment more in line with the user's actual situation.

[0086] Furthermore, the pre-trained career suitability model is built based on the first clustering model, and the user basic information label, career suitability type label, workplace personality type label and psychological state type label are imported into the pre-trained career suitability model to obtain the career ability suitability label and the psychological state suitability label, which specifically includes:

[0087] Use the pre-trained career suitability model to cluster the user's basic information labels, career suitability type labels, workplace personality type labels, and psychological state type labels to obtain type label clusters;

[0088] Calculate the distances from the cluster center of the type label cluster to the preset professional ability label and psychological state label respectively;

[0089] The occupational ability label and psychological state label adapted to the user are determined according to the distance, and the occupational ability adaptation degree label and psychological state adaptation degree label are obtained.

[0090] In this embodiment, the pre-trained career suitability model performs cluster analysis on the input multiple label data through a clustering model to help identify the relationship between the user and the specific career suitability. Specifically, the system performs a comprehensive clustering operation on the user's basic information label, career suitability type label, workplace personality type label, and psychological state type label to generate clusters of different types of labels, which reflect the similarities of different user groups in terms of career suitability. Then, the system calculates the distance between the center point of each cluster and the preset career ability label and psychological state label to determine which career label meets the characteristics of the user group. In this way, the system can accurately identify the career ability and psychological state labels that match the user based on the similarity between the clusters, thereby generating career ability suitability labels and psychological state suitability labels. This process allows the evaluation of career suitability to not only take into account the user's interests and personality, but also combines cluster analysis, further improving the personalization and accuracy of the recommendation results.

[0091] Through the above steps, the accuracy of career suitability assessment is effectively improved, and more personalized career recommendations can be provided based on the user's label clustering characteristics.

[0092] Further, the steps of determining the occupational ability label and psychological state label adapted to the user according to the distance, and obtaining the occupational ability adaptability label and psychological state adaptability label specifically include:

[0093] Obtain the distance from the cluster center point to each preset professional ability label to obtain a first distance;

[0094] Obtain the distance from the cluster center to each preset psychological state label to obtain the second distance;

[0095] Obtain the occupational ability labels whose first distance to the cluster center point is less than or equal to the preset distance threshold, and obtain the occupational ability adaptation degree label;

[0096] The psychological state labels whose second distance to the cluster center point is less than or equal to the preset distance threshold are obtained to obtain the psychological state fitness label.

[0097] In this embodiment, in order to accurately determine the occupational ability and psychological state that best matches the user, the system evaluates the adaptability of the label by calculating the distance from the center point of the cluster to each preset occupational ability label and psychological state label. First, the system calculates the distance from the center point of the cluster to all preset occupational ability labels to obtain a first distance value; then, the distance from the center point of the cluster to all preset psychological state labels is calculated to obtain a second distance value, which is used to measure the similarity between the user's characteristics and the occupational ability and psychological state labels. Next, by setting a preset distance threshold, the system screens out the occupational ability labels and psychological state labels that meet the conditions: only when the first distance is less than or equal to the preset threshold, the system will consider the occupational ability label to be adapted to the user; similarly, only when the second distance is less than or equal to the threshold, the psychological state label will be considered to be adapted to the user. In this way, the system can accurately identify the best matching label with the user in terms of occupational ability and psychological state, thereby obtaining occupational ability adaptability labels and psychological state adaptability labels.

[0098] Through the above steps, the system can effectively screen out the most appropriate professional ability and psychological state labels based on the calculation method of distance and threshold, thereby improving the accuracy and personalization of recommendations.

[0099] Furthermore, the pre-trained career suitability model is built based on the second clustering model, and the career ability suitability label and the psychological state suitability label are imported into the pre-trained career recommendation model to obtain the career recommendation result of the user to be evaluated, which specifically includes:

[0100] The occupational ability suitability labels are clustered using the pre-trained occupational recommendation model to obtain a first suitability label cluster;

[0101] The pre-trained career recommendation model is used to cluster the psychological state fitness labels to obtain the second fitness label clustering cluster;

[0102] In the preset career recommendation table, the first fitness label clustering cluster and the second fitness label clustering cluster are matched and mapped respectively to obtain the career recommendation result of the user to be evaluated.

[0103] In this embodiment, the pre-trained career recommendation model is trained based on another clustering model, aiming to map the user's career ability fitness label and psychological state fitness label to the most suitable career recommendation result. First, the system uses the pre-trained career recommendation model to cluster the career ability fitness label to generate a first fitness label cluster cluster, which analyzes the career ability fitness characteristics of different users and assigns the user to the group that best matches their ability. Then, the system also uses the model to cluster the psychological state fitness label to generate a second fitness label cluster cluster to ensure that the most suitable group is found according to the user's psychological state characteristics. Next, according to the preset career recommendation table, the system matches and maps the first fitness label cluster cluster and the second fitness label cluster cluster with each career recommendation result in the table, thereby obtaining the career recommendation result of the user to be evaluated. This process provides each user with personalized and accurate career recommendations by combining the user's ability and psychological state, ensuring that the recommendation results are more in line with the user's actual situation.

[0104] Through the above steps, the user's ability and psychological state labels are effectively and accurately matched with the career recommendation results, which improves the personalization and accuracy of the recommendation system and provides users with more valuable career advice.

[0105] In the above embodiments, the present application discloses an intelligent professional ability assessment method, which belongs to the field of artificial intelligence technology. The present application introduces multi-dimensional labels such as basic information labels, professional interest labels, workplace personality labels and psychological state labels of users, and uses pre-trained professional interest assessment models, workplace personality assessment models and professional adaptability models for layer-by-layer analysis, thereby realizing a multi-dimensional comprehensive assessment of the user's professional ability. Compared with the existing professional interest assessment, the scheme of the present application breaks the limitations of the traditional fixed assessment questions and lack of personalization. By analyzing the individual characteristics of users in various aspects, personalized assessment labels and recommendation results can be dynamically generated, effectively making up for the shortcomings of the one-sidedness of the existing assessment results. The present application can not only more comprehensively reflect the user's professional interests and workplace psychological state, but also provide personalized career recommendations based on the adaptability of professional ability and psychological state, so that the assessment results are more instructive and have practical reference significance, which helps users to more scientifically choose the career development direction that meets their own characteristics.

[0106] In this embodiment, the electronic device (eg, Figure 1The server shown in the figure) can receive instructions or obtain data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0107] It should be emphasized that in order to further ensure the privacy and security of the above user information, the above user information can also be stored in a blockchain node.

[0108] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0109] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0110] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0112] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0113] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of an intelligent professional ability assessment device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0114] like Figure 3 As shown, the intelligent professional ability assessment device 300 described in this embodiment includes:

[0115] The user information processing module 301 is used to obtain the basic information of the user to be evaluated and generate a basic information tag based on the basic information of the user;

[0116] A career interest assessment module 302 is used to import basic user information into a pre-trained career interest assessment model to obtain a career adaptation type label;

[0117] The workplace personality assessment module 303 is used to import the user's basic information into the pre-trained workplace personality assessment model to obtain a workplace personality type label and a psychological state type label;

[0118] The career suitability module 304 is used to import the user's basic information label, career suitability type label, workplace personality type label and psychological state type label into the pre-trained career suitability model to obtain the career ability suitability label and psychological state suitability label;

[0119] The career recommendation module 305 is used to import the career ability suitability label and the psychological state suitability label into the pre-trained career recommendation model to obtain the career recommendation result of the user to be evaluated.

[0120] Furthermore, the user information processing module 301 is specifically used for:

[0121] Receive a professional competency assessment instruction, and obtain basic user information of the user to be assessed from a preset database; perform data classification on the user's basic information to obtain a basic information classification result; and generate a user basic information label based on the basic information classification result.

[0122] Furthermore, the career interest assessment model is built based on a deep neural network. The intelligent career ability assessment device 300 also includes a career interest assessment model training module, which is specifically used for:

[0123] Acquire first training data, wherein the first training data includes first historical user information and matching historical occupation type labels;

[0124] The first training data is imported into the career interest assessment model, wherein the career interest assessment model includes a first input layer, a hidden layer and an output layer; the first training data is received through the first input layer, and the first historical user information is converted into a feature vector recognizable by the model; the feature vector is nonlinearly transformed through the hidden layer to extract the deep features of the first historical user information, and the feature relationship between the deep features and the preset career type label is learned; through the output layer, the career adaptation type prediction label corresponding to the first historical user information is output based on the feature relationship; based on the career adaptation type prediction label and the historical career type label, the career interest assessment model is iteratively trained until the model is fitted to obtain a pre-trained career interest assessment model.

[0125] Furthermore, the workplace personality assessment model is built based on the pre-trained BERT model. The intelligent professional ability assessment device 300 also includes a workplace personality assessment model training module, which is specifically used for:

[0126] Obtain second training data, wherein the second training data includes second historical user information and matching workplace behavior descriptions; import the second training data into a workplace personality assessment model, wherein the workplace personality assessment model includes a second input layer, a first fully connected layer, and a second fully connected layer; feature encode the second historical user information through the second input layer to obtain a first embedding vector; perform word segmentation processing on the workplace behavior description through the second input layer, and feature encode the obtained text segmentation to obtain a second embedding vector; concatenate the first embedding vector and the second embedding vector to obtain a concatenated embedding vector; import the concatenated embedding vector into the first fully connected layer and the second fully connected layer respectively, obtain the workplace personality type probability distribution output by the first fully connected layer, and obtain the psychological state type probability distribution output by the second fully connected layer; enable the workplace personality assessment model to learn the workplace personality type probability distribution and the psychological state type probability distribution respectively, and obtain a pre-trained workplace personality assessment model.

[0127] Furthermore, the pre-trained career suitability model is built based on the first clustering model, and the career suitability module 304 is specifically used for:

[0128] Use the pre-trained career suitability model to cluster the user's basic information labels, career suitability type labels, workplace personality type labels, and psychological state type labels to obtain type label clustering clusters; calculate the distances from the cluster center points of the type label clustering clusters to the preset career ability labels and psychological state labels respectively; determine the career ability labels and psychological state labels that are suitable for the user based on the distances, and obtain the career ability suitability labels and psychological state suitability labels.

[0129] Furthermore, the career suitability module 304 is further used to:

[0130] Obtain the distance from the cluster center to each preset professional ability label to obtain the first distance; obtain the distance from the cluster center to each preset psychological state label to obtain the second distance; obtain the professional ability label whose first distance to the cluster center is less than or equal to the preset distance threshold to obtain the professional ability adaptation label; obtain the psychological state label whose second distance to the cluster center is less than or equal to the preset distance threshold to obtain the psychological state adaptation label.

[0131] Furthermore, the pre-trained career suitability model is built based on the second clustering model, and the career recommendation module 305 is specifically used for:

[0132] Use the pre-trained career recommendation model to cluster the career ability suitability labels to obtain the first suitability label clustering cluster; use the pre-trained career recommendation model to cluster the psychological state suitability labels to obtain the second suitability label clustering cluster; in the preset career recommendation table, match and map the first suitability label clustering cluster and the second suitability label clustering cluster respectively to obtain the career recommendation result for the user to be evaluated.

[0133] In the above embodiments, the present application discloses an intelligent professional ability assessment device, which belongs to the field of artificial intelligence technology. The present application introduces multi-dimensional labels such as basic information labels, professional interest labels, workplace personality labels and psychological state labels of users, and uses pre-trained professional interest assessment models, workplace personality assessment models and professional adaptability models for layer-by-layer analysis, thereby realizing a multi-dimensional comprehensive assessment of the user's professional ability. Compared with the existing professional interest assessment, the scheme of the present application breaks the limitations of the traditional fixed assessment questions and lack of personalization. By analyzing the individual characteristics of users in various aspects, personalized assessment labels and recommendation results can be dynamically generated, effectively making up for the shortcomings of the one-sidedness of the existing assessment results. The present application can not only more comprehensively reflect the user's professional interests and workplace psychological state, but also provide personalized career recommendations based on the adaptability of professional ability and psychological state, so that the assessment results are more instructive and have practical reference significance, which helps users to more scientifically choose the career development direction that meets their own characteristics.

[0134] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0135] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 4 with a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0136] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0137] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the intelligent professional ability assessment method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0138] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the intelligent professional competence assessment method.

[0139] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0140] In the above embodiments, the present application discloses a computer device, which belongs to the field of artificial intelligence technology. The present application introduces multi-dimensional labels such as basic information labels, professional interest labels, workplace personality labels and psychological state labels of users, and uses pre-trained professional interest assessment models, workplace personality assessment models and professional adaptability models for layer-by-layer analysis, thereby realizing a multi-dimensional comprehensive evaluation of the user's professional ability. Compared with the existing professional interest assessment, the scheme of the present application breaks the limitations of the traditional fixed assessment questions and lack of personalization. By analyzing the individual characteristics of users in various aspects, personalized assessment labels and recommendation results can be dynamically generated, effectively making up for the shortcomings of the one-sidedness of the existing assessment results. The present application can not only more comprehensively reflect the user's professional interests and workplace psychological state, but also provide personalized career recommendations based on the adaptability of professional ability and psychological state, so that the assessment results are more instructive and have practical reference significance, which helps users to more scientifically choose the career development direction that meets their own characteristics.

[0141] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the intelligent professional competency assessment method as described above.

[0142] In the above embodiments, the present application discloses a computer-readable storage medium, which belongs to the field of artificial intelligence technology. The present application introduces multi-dimensional labels such as basic information labels, professional interest labels, workplace personality labels and psychological state labels of users, and uses pre-trained professional interest assessment models, workplace personality assessment models and professional adaptability models for layer-by-layer analysis, thereby realizing a multi-dimensional comprehensive evaluation of the user's professional ability. Compared with the existing professional interest assessment, the scheme of the present application breaks the limitations of the traditional fixed assessment questions and lack of personalization. By analyzing the individual characteristics of users in various aspects, personalized assessment labels and recommendation results can be dynamically generated, effectively making up for the shortcomings of the one-sidedness of the existing assessment results. The present application can not only more comprehensively reflect the user's professional interests and workplace psychological state, but also provide personalized career recommendations based on the adaptability of professional ability and psychological state, so that the assessment results are more instructive and have practical reference significance, which helps users to more scientifically choose the career development direction that meets their own characteristics.

[0143] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0144] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0145] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. An intelligent professional ability assessment method, characterized in that: include: Obtaining basic user information of the user to be evaluated, and generating a basic user information tag based on the basic user information; Importing the user's basic information into a pre-trained career interest assessment model to obtain a career adaptation type label; Importing the user's basic information into a pre-trained workplace personality assessment model to obtain a workplace personality type label and a psychological state type label; Importing the user basic information label, the occupational adaptability type label, the workplace personality type label, and the psychological state type label into a pre-trained occupational adaptability model to obtain an occupational ability adaptability label and a psychological state adaptability label; The occupational ability suitability label and the psychological state suitability label are introduced into a pre-trained occupation recommendation model to obtain an occupation recommendation result for the user to be evaluated.

2. The intelligent professional ability assessment method according to claim 1, characterized in that: The step of obtaining basic user information of the user to be evaluated and generating a basic user information tag based on the basic user information specifically includes: Receiving a professional competency assessment instruction, and obtaining basic user information of the user to be assessed from a preset database; Performing data classification on the user basic information to obtain a basic information classification result; The user basic information tag is generated according to the basic information classification result.

3. The intelligent professional ability assessment method according to claim 1, characterized in that: The career interest assessment model is built based on a deep neural network. Before the step of importing the basic information of the user into the pre-trained career interest assessment model to obtain a career adaptation type label, it also includes: Acquire first training data, wherein the first training data includes first historical user information and matching historical occupation type labels; Importing the first training data into the career interest assessment model, wherein the career interest assessment model includes a first input layer, a hidden layer, and an output layer; receiving the first training data through the first input layer, and converting the first historical user information into a feature vector recognizable by a model; Performing a nonlinear transformation on the feature vector through the hidden layer to extract deep features of the first historical user information, and learning a feature relationship between the deep features and a preset occupation type label; Outputting, through the output layer, a predicted label of the occupational adaptation type corresponding to the first historical user information based on the feature relationship; Based on the occupational adaptation type prediction label and the historical occupational type label, the occupational interest assessment model is iteratively trained until the model is fitted to obtain the pre-trained occupational interest assessment model.

4. The intelligent professional ability assessment method according to claim 1, characterized in that: The workplace personality assessment model is built based on the pre-trained BERT model. Before the step of importing the basic information of the user into the pre-trained workplace personality assessment model to obtain the workplace personality type label and the psychological state type label, it also includes: Acquire second training data, wherein the second training data includes second historical user information and matching workplace behavior descriptions; Importing the second training data into the workplace personality assessment model, wherein the workplace personality assessment model includes a second input layer, a first fully connected layer, and a second fully connected layer; Performing feature encoding on the second historical user information through the second input layer to obtain a first embedding vector; The workplace behavior description is segmented through the second input layer, and feature encoding is performed on the obtained text segmentation to obtain a second embedding vector; Concatenating the first embedding vector and the second embedding vector to obtain a concatenated embedding vector; Importing the concatenated embedding vector into the first fully connected layer and the second fully connected layer respectively, obtaining the workplace personality type probability distribution output by the first fully connected layer, and obtaining the psychological state type probability distribution output by the second fully connected layer; The workplace personality assessment model is made to learn the workplace personality type probability distribution and the psychological state type probability distribution respectively to obtain the pre-trained workplace personality assessment model.

5. The intelligent professional ability assessment method according to claim 1, characterized in that: The pre-trained career suitability model is built based on the first clustering model, and the step of importing the user basic information label, the career suitability type label, the workplace personality type label, and the psychological state type label into the pre-trained career suitability model to obtain the career ability suitability label and the psychological state suitability label specifically includes: Using the pre-trained career suitability model, clustering the user basic information label, the career suitability type label, the workplace personality type label, and the psychological state type label to obtain a type label cluster; Calculating the distances from the cluster center of the type label cluster to the preset professional ability label and psychological state label respectively; The occupational ability label and the psychological state label adapted to the user are determined according to the distance, and the occupational ability adaptability label and the psychological state adaptability label are obtained.

6. The intelligent professional ability assessment method according to claim 5, characterized in that: The step of determining the occupational ability label and the psychological state label adapted to the user according to the distance, and obtaining the occupational ability adaptability label and the psychological state adaptability label specifically includes: Obtaining the distance from the cluster center point to each preset professional ability label to obtain a first distance; Obtaining the distance from the cluster center point to each preset psychological state label to obtain a second distance; Obtaining a professional ability label whose first distance to the cluster center point is less than or equal to a preset distance threshold, and obtaining the professional ability adaptation degree label; The psychological state label whose second distance to the cluster center point is less than or equal to the preset distance threshold is obtained to obtain the psychological state fitness label.

7. The intelligent professional ability assessment method according to claim 6, characterized in that: The pre-trained career suitability model is built based on the second clustering model, and the step of importing the career ability suitability label and the psychological state suitability label into the pre-trained career recommendation model to obtain the career recommendation result of the user to be evaluated specifically includes: Clustering the occupational capability suitability labels using the pre-trained occupational recommendation model to obtain a first suitability label cluster; Clustering the psychological state fitness labels using the pre-trained career recommendation model to obtain a second fitness label cluster; In the preset occupation recommendation table, the first fitness label clustering cluster and the second fitness label clustering cluster are matched and mapped respectively to obtain the occupation recommendation result of the user to be evaluated.

8. An intelligent professional ability assessment device, characterized in that: include: A user information processing module, used to obtain basic user information of the user to be evaluated, and generate a basic user information tag based on the basic user information; A career interest assessment module, used to import the user's basic information into a pre-trained career interest assessment model to obtain a career adaptation type label; A workplace personality assessment module, used to import the user's basic information into a pre-trained workplace personality assessment model to obtain a workplace personality type label and a psychological state type label; A career suitability module, used to import the user basic information label, the career suitability type label, the workplace personality type label and the psychological state type label into a pre-trained career suitability model to obtain a career ability suitability label and a psychological state suitability label; The career recommendation module is used to import the career ability adaptation label and the psychological state adaptation label into the pre-trained career recommendation model to obtain the career recommendation result of the user to be evaluated.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the intelligent professional ability assessment method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the intelligent professional competence assessment method according to any one of claims 1 to 7.

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