Operation and maintenance skill evaluation method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of artificial intelligence, and relates to an operation and maintenance skill evaluation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining skill practice data and training track data of a target operation and maintenance person; the trained skill evaluation model is adopted, the skill mastering state of the target operation and maintenance personnel is obtained through analysis according to the skill practice data, and skill training preferences of the target operation and maintenance personnel are obtained through analysis according to the training track data; generating a skill portrait of the target operation and maintenance personnel according to the skill mastering state and the skill training preference; and distributing a target skill improvement item to the target operation and maintenance personnel according to the skill portrait. In addition, the invention also relates to a block chain technology, and data such as skill practice data and training track data can be stored in a block chain. According to the method, the skill mastering condition of the operation and maintenance personnel can be accurately described and evaluated, so that the operation and maintenance resources are reasonably and efficiently allocated.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular, to an operation and maintenance skill evaluation method, device, computer device, and storage medium. Background Art

[0002] With the accelerating digital transformation of the financial industry, the business operations of financial enterprises increasingly rely on efficient and stable Internet technology (IT) systems. However, the rapid progress of information technology and the increasing complexity of IT systems have led to a trend of high specialization and diversification in the skills required by IT operation and maintenance personnel in the financial industry. Against this background, visualizing the skills of operation and maintenance personnel and providing financial enterprise management and individuals with a clear understanding of individual skill levels has become a key requirement. This requirement not only concerns how to reasonably allocate work tasks based on the current skill situation to improve work efficiency but also involves how to design effective improvement plans based on the existing skill foundation to ensure the continuous growth and development of IT operation and maintenance talents. However, currently, there is a lack of a systematic mechanism in the financial industry to accurately describe and evaluate the skill mastery of operation and maintenance personnel, which directly affects the accuracy and efficiency of enterprises in aspects such as personnel allocation, skill training planning, project task assignment, and rank promotion decisions, and further leads to problems such as unreasonable resource allocation and low operation and maintenance efficiency. Summary of the Invention

[0003] The purpose of the embodiments of this application is to propose an operation and maintenance skill evaluation method, device, computer device, and storage medium, which can accurately describe and evaluate the skill mastery of operation and maintenance personnel, so as to reasonably and efficiently allocate operation and maintenance resources.

[0004] To solve the above technical problems, the embodiments of this application provide an operation and maintenance skill evaluation method, which adopts the following technical solutions:

[0005] Obtain the skill practice data and training track data of the target operation and maintenance personnel;

[0006] Use the trained skill evaluation model to analyze the skill mastery status of the target operation and maintenance personnel based on the skill practice data, and analyze the skill training preferences of the target operation and maintenance personnel based on the training track data;

[0007] Generate a skill portrait of the target operation and maintenance personnel based on the skill mastery status and the skill training preferences;

[0008] Allocate a target skill improvement project for the target operation and maintenance personnel according to the skill portrait.

[0009] To solve the above technical problems, the embodiments of this application also provide an operation and maintenance skill evaluation device, which adopts the following technical solutions:

[0010] An acquisition module for acquiring the skill practice data and training trajectory data of a target operation and maintenance personnel;

[0011] An analysis module for using the trained skill evaluation model to analyze the skill mastery status of the target operation and maintenance personnel based on the skill practice data, and analyze the skill training preferences of the target operation and maintenance personnel based on the training trajectory data;

[0012] A generation module for generating a skill portrait of the target operation and maintenance personnel based on the skill mastery status and the skill training preferences;

[0013] An allocation module for allocating a target skill improvement project to the target operation and maintenance personnel according to the skill portrait.

[0014] To solve the above technical problems, an embodiment of the present application further provides a computer device, which adopts the following technical solutions: the computer device includes a memory and a processor, the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of the operation and maintenance skill evaluation method described in any one of the above.

[0015] To solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions: the computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, it implements the steps of the operation and maintenance skill evaluation method described in any one of the above.

[0016] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0017] The solution of the embodiment of this application can accurately evaluate the skill mastery status of the target operation and maintenance personnel by obtaining their skill practice data and analyzing it using the trained skill evaluation model; by obtaining the training trajectory data of the target operation and maintenance personnel and analyzing it using the trained skill evaluation model, it can identify the skill training preferences of the target operation and maintenance personnel; by combining the training trajectory data and the actual skill practice data for targeted evaluation, it helps to more comprehensively and accurately reflect the true skill mastery level of the target operation and maintenance personnel; by generating a skill portrait of the target operation and maintenance personnel based on the skill mastery status and skill training preferences, and allocating target skill improvement projects for the target operation and maintenance personnel according to the skill portrait, it can comprehensively display the current skill level and training preferences of the target operation and maintenance personnel through the skill portrait, and accurately match the target skill improvement projects through the skill portrait. The project allocation method based on the skill portrait not only considers the current skill level of the target operation and maintenance personnel, but also combines training preferences and development needs, thus ensuring the pertinence and effectiveness of the skill improvement projects. Through reasonable and efficient allocation of skill improvement projects, it ensures that the target operation and maintenance personnel can quickly improve their skill levels, thereby improving work efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the solutions in this application, the following will briefly introduce the drawings required for the description of the embodiments of this application. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 is an exemplary system architecture diagram to which this application can be applied;

[0020] Figure 2 is a flowchart of an embodiment of the operation and maintenance skill evaluation method of this application;

[0021] Figure 3 is a structural diagram of an embodiment of the operation and maintenance skill evaluation device of this application;

[0022] Figure 4 is a structural diagram of an embodiment of the computer device of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0024] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the application. The phrase does not necessarily refer to the same embodiment at every occurrence in the specification, nor are they mutually exclusive independent or alternative embodiments from other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] To enable those skilled in the art of this technology to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings.

[0026] As Figure 1 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 computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing 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 fiber optic cables, etc.

[0027] Users 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 may be installed on the terminal device 101, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social platform software, etc.

[0028] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop 1011, tablet 1012, or 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) player, a laptop computer, a desktop computer, and the like.

[0029] The server 103 can be a server that provides various services, such as a background server that supports the pages displayed on the terminal device 101.

[0030] It should be noted that the operation and maintenance skill assessment method provided by the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the operation and maintenance skill assessment device is generally set in the server / terminal device.

[0031] It should be understood that Figure 1 the numbers of the terminal devices, networks, and servers in

[0032] Continue to refer to Figure 2 , which shows a flowchart of an embodiment of the question-and-answer method according to the present application. The operation and maintenance skill assessment method includes the following steps:

[0033] Step S201, obtain the skill practice data and training trajectory data of the target operation and maintenance personnel;

[0034] In this embodiment, the electronic device (such as Figure 1 the server / terminal device shown) on which the operation and maintenance skill assessment method runs can obtain the skill practice data and training trajectory data of the target operation and maintenance personnel through a wired connection or a wireless connection. It should be noted that the above wireless connection methods can include, but are not limited to, 2G / 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future-developed wireless connection methods.

[0035] Specifically, obtain the skill practice data and training track data of the target operation and maintenance personnel. Among them, the skill practice data refers to the relevant data of the operation and maintenance personnel practicing various operation and maintenance skill points, which may include but are not limited to the details of test questions related to the practice of operation and maintenance skill points, training scores, training duration, training progress, training habits, training difficulty, training fields, etc.; the training track data refers to the relevant data of the operation and maintenance personnel undergoing training for various operation and maintenance skill points, which may include but are not limited to the number of training sessions, training content, online training duration, and training check-in status related to the training of operation and maintenance skill points. The operation and maintenance skill points may include but are not limited to network configuration skills, troubleshooting skills, network security maintenance skills, database management skills, system installation skills, performance monitoring skills, etc. For example, for the operation and maintenance skill point "network configuration skills", the skill practice data of the target operation and maintenance personnel for this network configuration skill may be: the details of the test questions are network configuration training questions, the training score is 90 points, the training duration is 2 hours, the training progress is 90%, etc.; the training track data of the target operation and maintenance personnel for this network configuration skill may be: the number of training sessions is 3 times, the training content is theoretical learning, the online training duration is 2 hours, the training check-in status is full attendance, etc.

[0036] In some optional implementation manners, after obtaining the skill training data and training track data of the target operation and maintenance personnel, the following steps may further be included:

[0037] Preprocess the skill practice data and the training track data, remove the noise data, duplicate data, and abnormal data in the skill practice data and the training track data, and obtain the preprocessed skill practice data and the preprocessed training track data;

[0038] Specifically, preprocess the obtained skill practice data and training track data, identify the noise data, duplicate data, and abnormal data in the skill practice data and the training track data, and remove the identified noise data, duplicate data, and abnormal data to obtain the preprocessed skill practice data and the preprocessed training track data.

[0039] Determine whether the preprocessed skill practice data and the preprocessed training track data carry label information;

[0040] Specifically, traverse each preprocessed skill practice data and preprocessed training track data to determine whether each preprocessed skill practice data and preprocessed training track data carries label information. For example, the label information of the preprocessed skill practice data may be the marked training score, training duration, training progress, etc.; the label information of the preprocessed training track data may be the marked number of training sessions, training content theme, online training duration, etc.

[0041] If the preprocessed skill practice data carries label information, extract feature data from the label information of the preprocessed skill practice data to obtain the first skill practice feature;

[0042] If the preprocessed training trajectory data carries label information, extract feature data from the label information of the preprocessed training trajectory data to obtain the first training trajectory feature.

[0043] Specifically, if the preprocessed skill practice data carries label information, extract feature data from the label information of the preprocessed skill practice data to obtain the first skill practice feature. If the preprocessed training trajectory data carries label information, extract feature data from the label information of the preprocessed training trajectory data to obtain the first training trajectory feature. For example, if the label information carried by the preprocessed skill practice data is the training score, extract the training score and its score from the preprocessed skill practice data as the first skill practice feature; if the label information carried by the preprocessed training trajectory data is the number of trainings, extract the number of trainings and its specific number from the preprocessed training trajectory data as the first training trajectory feature.

[0044] The embodiments of the present application can significantly reduce the time cost of manual intervention and improve the data processing efficiency through an automated data preprocessing process. For labeled data, directly extract feature data from the labels, which simplifies the feature extraction process and reduces the difficulty of feature engineering.

[0045] In some alternative implementation manners, after determining whether the preprocessed skill practice data and the preprocessed training trajectory data carry label information, the following steps may further be included:

[0046] If the preprocessed skill practice data does not carry label information, perform semantic recognition and analysis on the preprocessed skill practice data to obtain the second skill practice feature;

[0047] If the preprocessed training trajectory data does not carry label information, perform semantic recognition and analysis on the preprocessed training trajectory data to obtain the second training trajectory feature.

[0048] Specifically, if the preprocessed skill practice data does not carry label information, semantic recognition and analysis are performed on the preprocessed skill practice data to obtain second skill practice features; if the preprocessed training trajectory data does not carry label information, semantic recognition and analysis are performed on the preprocessed training trajectory data to obtain second training trajectory features. For example, it can be to perform word segmentation, stop word removal, stemming, etc. on the preprocessed skill practice data or preprocessed training trajectory data to obtain processed text data, use language analysis techniques to extract text features from the processed text data, and perform semantic analysis on the extracted text features to obtain a semantic analysis result. Features related to skill practice are extracted from the semantic analysis result as second skill practice features; or, features related to the training trajectory are extracted from the semantic analysis result as second training trajectory features.

[0049] In the embodiments of the present application, by performing semantic recognition and analysis on the skill practice data and / or training trajectory data without labels, useful second skill practice features and / or second training trajectory features can be extracted, and unstructured data (such as text information) that was originally difficult to directly utilize can be transformed into structured feature data, enhancing data understanding and usability.

[0050] Step S202, using the trained skill evaluation model, analyze the skill mastery status of the target operation and maintenance personnel based on the skill practice data, and analyze the skill training preferences of the target operation and maintenance personnel based on the training trajectory data;

[0051] In this embodiment, the skill evaluation model is a trained machine learning or deep learning model. This model can learn and understand the internal laws and features of the data based on the input skill practice data and training trajectory data, and identify the skill mastery status and skill training preferences of the target operation and maintenance personnel. The skill mastery status refers to the degree of mastery of each skill point by the target operation and maintenance personnel, which can include status classifications such as full mastery, partial mastery, and complete absence. The skill training preference refers to the personal preferences and needs of the target operation and maintenance personnel in terms of training, which can include interested skill directions, preferred training times, favorite training methods, etc.

[0052] Specifically, obtain the trained skill evaluation model, input the skill practice data and training trajectory data into the trained skill evaluation model, and use the trained skill evaluation model to analyze according to the input skill practice data to obtain the skill mastery status of the target operation and maintenance personnel. The skill mastery status can be a fully mastered status, a partially mastered status, a completely missing status, etc.; use the trained skill evaluation model to analyze according to the input training trajectory data to obtain the skill training preference of the target operation and maintenance personnel. The skill training preference can be the skill direction of interest, the preferred training time, the preferred training method, etc.

[0053] In some alternative implementation manners, after preprocessing the skill practice data and the training trajectory data to obtain skill practice features and training trajectory features, the above-mentioned use of the trained skill evaluation model to analyze the skill mastery status of the target operation and maintenance personnel according to the skill practice data and analyze the skill training preference of the target operation and maintenance personnel according to the training trajectory data is specifically as follows: Obtain the trained skill evaluation model, input the skill practice features and training trajectory features into the trained skill evaluation model, and use the trained skill evaluation model to analyze according to the input skill practice features to obtain the skill mastery status of the target operation and maintenance personnel. The skill mastery status can be a fully mastered status, a partially mastered status, a completely missing status, etc.; use the trained skill evaluation model to analyze according to the input training trajectory features to obtain the skill training preference of the target operation and maintenance personnel. The skill training preference can be the skill direction of interest, the preferred training time, the preferred training method, etc.

[0054] In some alternative implementation manners, the above-mentioned analysis of the skill mastery status of the target operation and maintenance personnel according to the skill practice data may include the following steps:

[0055] Obtain the first skill point type to which the skill practice data belongs;

[0056] Specifically, use the trained skill evaluation model to obtain the first skill point type to which each skill practice data belongs. The first skill point type may include, but is not limited to, network configuration skills, troubleshooting skills, network security maintenance skills, database management skills, system installation skills, performance monitoring skills, etc.

[0057] Divide the skill practice data according to the first skill point type to obtain the practice data sets corresponding to each first skill point type;

[0058] Specifically, according to the first skill point type to which the skill practice data belongs, divide each skill practice data into the practice data set corresponding to the first skill point type. Among them, each practice data set corresponds to a first skill point type.

[0059] Calculate the proficiency values of the target operation and maintenance personnel for each first skill point type according to the training data sets corresponding to the respective first skill point types;

[0060] Specifically, perform data statistics according to the training data sets corresponding to the respective first skill point types. For example, quantify the data in the training data set and add up the quantified values one by one to obtain the proficiency values of the target operation and maintenance personnel for each first skill point type.

[0061] Determine the skill mastery status of the target operation and maintenance personnel for each first skill point type according to the proficiency values and preset thresholds.

[0062] Specifically, set one or more preset thresholds for each first skill point type. The preset threshold is used to measure the mastery degree of the target operation and maintenance personnel for this skill point. The preset threshold may include the correct rate of question answering, the success rate of operation, the progress of training completion, etc. Compare the proficiency values of each first skill point type with the corresponding preset thresholds to determine the skill mastery status of the target operation and maintenance personnel for each first skill point type.

[0063] In the embodiments of the present application, by obtaining the skill point types to which the skill training data belongs and partitioning the skill training data according to the skill point types, fine partitioning of the operation and maintenance skill training data is realized. Each skill point type corresponds to a data set, which facilitates subsequent analysis and calculation work, and also helps to reduce data redundancy and errors. By calculating the proficiency values of the target operation and maintenance personnel for each skill point type according to the training data sets corresponding to the respective skill point types, quantitative evaluation of the skill level of the target operation and maintenance personnel is realized; by determining the skill mastery status of the target operation and maintenance personnel for each skill point type according to the proficiency values and preset thresholds, it helps to quickly determine the mastery of the target operation and maintenance personnel on each skill point.

[0064] In some optional implementation manners, the above-mentioned obtaining the skill training preferences of the target operation and maintenance personnel by analyzing the training trajectory data may include the following steps:

[0065] Obtain the second skill point types to which the training trajectory data belongs;

[0066] Specifically, use the trained skill evaluation model to obtain the second skill point types to which the respective training trajectory data belongs. The second skill point types may include, but are not limited to, network configuration skills, troubleshooting skills, network security maintenance skills, database management skills, system installation skills, performance monitoring skills, etc.

[0067] Partition the training trajectory data according to the second skill point type to obtain the training data sets corresponding to each second skill point type;

[0068] Specifically, according to the second skill point type to which the training trajectory data belongs, partition each training trajectory data into the training data set corresponding to this second skill point type. Among them, each training data set corresponds to a second skill point type.

[0069] According to the training data sets corresponding to each second skill training type, calculate the skill preference values of the target operation and maintenance personnel for each second skill point type;

[0070] Specifically, conduct data statistics according to the training data sets corresponding to each second skill point type, such as quantifying the data in the training data set, and adding up the quantified values one by one to obtain the skill preference values of the target operation and maintenance personnel for each second skill point type.

[0071] Sort each second skill point type according to the skill preference values to obtain the skill training preference list of the target operation and maintenance personnel.

[0072] Specifically, sort each second skill point type in descending order of the calculated skill preference values to obtain the skill training preference list of the target operation and maintenance personnel. This skill training preference list reflects the preference degree of the target operation and maintenance personnel for each second skill point type.

[0073] In the embodiment of the present application, by obtaining the skill point type to which the training trajectory data belongs, after partitioning the training trajectory data according to the skill point type, the training data sets corresponding to each skill point type are obtained, realizing the precise classification of operation and maintenance skill training data. By calculating the skill preference values of the target operation and maintenance personnel for each skill point type according to the training data sets corresponding to each skill training type, the quantitative evaluation of the skill preference of the target operation and maintenance personnel is realized; by sorting each skill point type according to the skill preference values, the skill training preference list of the target operation and maintenance personnel is obtained, which can clearly show the preference degree of the target operation and maintenance personnel for each skill point.

[0074] Step S203, generate a skill portrait of the target operation and maintenance personnel according to the skill mastery status and the skill training preference;

[0075] In this embodiment, the skill portrait is a visualization tool used to describe the skill status of the target operation and maintenance personnel. The skill portrait presents the skill mastery status and skill training preference of the target operation and maintenance personnel in a graphical way, and can better show the skill status and development needs of the target operation and maintenance personnel.

[0076] Specifically, obtain a preset portrait structure, standardize the skill mastery status and the skill training preferences according to the data requirements of the portrait structure, and generate a technical portrait of the target operation and maintenance personnel based on the standardized data and the portrait structure.

[0077] In some alternative implementation manners, generating the skill portrait of the target operation and maintenance personnel according to the skill mastery status and the skill training preferences may include the following steps:

[0078] Align the data of the skill mastery status and the skill training preferences according to the same skill point type, and use the same skill point type as the portrait base point.

[0079] Specifically, extract a first skill point type from the skill mastery status, extract a second skill point type from the skill training preferences, compare the first skill point type with the second skill point type to determine the same skill point type. Align the data of the skill mastery status and the skill training preferences according to the same skill point type, obtain the skill mastery status and the skill training preferences included in each same skill point type, and use each same skill point type as the portrait base point of the skill portrait.

[0080] Map the skill mastery status and the skill training preferences to the portrait base point according to the same skill point type to obtain the mapped portrait base point data.

[0081] Specifically, map the skill mastery status and the skill training preferences according to the same skill point type, that is, map the skill mastery status (such as full mastery status, partial mastery status, complete absence status) and the skill training preferences (such as skill preference value) of the target operation and maintenance personnel on each skill point type to the corresponding portrait base point to obtain the mapped portrait base point data.

[0082] Obtain the weight value of the same skill point type, and perform weighted processing on the portrait base point data according to the weight value to obtain the weighted portrait base point data.

[0083] Specifically, obtain the weight values of each same skill point type set in advance, and the weight value reflects the importance and priority of different skill point types in the operation and maintenance work. Perform weighted processing on the mapped portrait base point data according to the weight values of each same skill point type to obtain the weighted portrait base point data.

[0084] Obtain a preset portrait structure, and perform portrait integration according to the portrait structure and the weighted portrait base point data to generate the skill portrait of the target operation and maintenance personnel.

[0085] Specifically, obtain a preset portrait structure, and perform portrait integration based on this portrait structure and the weighted portrait base point data. During the integration process, fill the weighted portrait base point data into the corresponding positions of the portrait structure to generate the skill portrait of the target operation and maintenance personnel.

[0086] In the embodiment of the present application, by extracting the skill point types from the skill mastery status and skill training preferences as the portrait base points, and mapping the skill mastery status and skill training preferences to the portrait base points according to the skill point types, it helps to ensure that the portrait can comprehensively and accurately reflect the skill characteristics of the target operation and maintenance personnel. Based on the characteristics and requirements of the operation and maintenance work, reasonable weight values are assigned to each skill point type, and the mapped portrait base point data is weighted to achieve a quantitative evaluation of the importance of the operation and maintenance personnel's skills, enabling the portrait to more accurately reflect the skill priorities and development directions of the target operation and maintenance personnel. By generating the skill portrait in a graphical manner, it intuitively displays the skill status and development needs of the target operation and maintenance personnel. This intuitiveness enables managers and target operation and maintenance personnel to easily understand the information conveyed by the portrait.

[0087] Step S204, assign a target skill improvement project to the target operation and maintenance personnel according to the skill portrait.

[0088] In this embodiment, the skill improvement project refers to a training or learning plan designed to help operation and maintenance personnel improve specific skill points. Obtain the skill point requirements of a preset number of skill improvement projects, and match the generated skill portrait with the skill point requirements of each skill improvement project to obtain the target skill improvement project of the target operation and maintenance personnel.

[0089] In some optional implementation manners, the above-mentioned assigning a target skill improvement project to the target operation and maintenance personnel according to the skill portrait may include the following steps:

[0090] Obtain the skill point requirements of each predefined skill improvement project;

[0091] Specifically, obtain the skill point requirements of each predefined skill improvement project, and the skill point requirements may include the skill point types that the project aims to improve, the prerequisite skills required, the recommended training duration, etc.

[0092] Calculate the matching degree values between the skill portrait and the skill point requirements of each skill improvement project;

[0093] Specifically, compare the skill portrait of the target operation and maintenance personnel with the skill point requirements of each skill improvement project, compare the mastery degree of each skill point in the skill portrait with the matching degree of the skill point requirements, and compare the preference degree of each skill point in the skill portrait with the matching degree of the skill point requirements, and calculate the corresponding matching degree values.

[0094] Obtain the skill improvement project with the highest matching degree value as the target skill improvement project for the target operation and maintenance personnel.

[0095] Specifically, sort the calculated matching degree values of each skill improvement project from high to low, and obtain the skill improvement project with the highest matching degree value as the target skill improvement project for the target operation and maintenance personnel.

[0096] In the embodiment of the present application, by obtaining the skill point requirements of each predefined skill improvement project and comparing them with the skill portrait of the target operation and maintenance personnel, calculating the matching degree value between the skill portrait and the skill point requirements of each skill improvement project, and performing project allocation based on the matching degree value, it helps to make the best choice among multiple skill improvement projects, ensuring that limited training resources can be efficiently and reasonably allocated to the operation and maintenance personnel who most need improvement.

[0097] Through the above solution, in the embodiment of the present application, specifically by obtaining the skill practice data of the target operation and maintenance personnel and analyzing it using the trained skill evaluation model, the skill mastery status of the target operation and maintenance personnel can be accurately evaluated; by obtaining the training track data of the target operation and maintenance personnel and analyzing it using the trained skill evaluation model, the skill training preferences of the target operation and maintenance personnel can be identified; by combining the training track data and the actual skill practice data for targeted evaluation, it helps to more comprehensively and accurately reflect the true skill mastery level of the target operation and maintenance personnel; by generating the skill portrait of the target operation and maintenance personnel based on the skill mastery status and skill training preferences, and allocating the target skill improvement project according to the skill portrait, the current skill level and training preferences of the target operation and maintenance personnel can be comprehensively displayed through the skill portrait, and the target skill improvement project can be accurately matched through the skill portrait. The project allocation method based on the skill portrait not only considers the current skill level of the target operation and maintenance personnel, but also combines the training preferences and development needs, thus ensuring the pertinence and effectiveness of the skill improvement project. Through reasonable and efficient allocation of skill improvement projects, it is ensured that the target operation and maintenance personnel can quickly improve their skill levels, thereby improving work efficiency and quality.

[0098] Exemplarily, in the financial service scenario, the IT operation and maintenance team is responsible for ensuring the stable operation and efficient service of the financial system. With the increasing business complexity and accelerating technological iteration, the skills that operation and maintenance personnel need to master are becoming increasingly diverse. In this example, through an operation and maintenance skills assessment system, the skills mastery of operation and maintenance personnel is accurately described and evaluated, providing strong support for financial enterprises to allocate operation and maintenance resources reasonably and efficiently. First, the operation and maintenance skills assessment system obtains the skills practice data and training track data of the target operation and maintenance personnel. The skills practice data of the target operation and maintenance personnel includes, but is not limited to, their operation records in the simulation environment, the process of troubleshooting and repair, system configuration and optimization practices, etc. These data can be collected through the IT operation and maintenance management system of financial enterprises or a dedicated skills practice platform. At the same time, the training track data records the training courses attended by operation and maintenance personnel, the browsing records of online learning resources, the situation of skills certification exams, etc., and these data can be obtained through the enterprise internal training management system or a third-party online learning platform.

[0099] Then, the operation and maintenance skills assessment system adopts a skills assessment model based on machine learning. This model is trained and optimized based on a large amount of operation and maintenance skills data and can accurately identify the skills mastery status of operation and maintenance personnel. Specifically, the model will analyze indicators such as the proficiency and error rate of the target operation and maintenance personnel in each skills area according to the skills practice data, so as to obtain the skills mastery status. At the same time, the model will also analyze the learning preferences, learning speed, learning achievements, etc. of the target operation and maintenance personnel according to the training track data to obtain the skills training preferences. After obtaining the skills mastery status and skills training preferences, the operation and maintenance skills assessment system further generates a skills portrait of the target operation and maintenance personnel. The skills portrait is a comprehensive skills assessment report that combines multiple dimensions such as the skills mastery of operation and maintenance personnel, learning preferences, and development potential, providing financial enterprise management with a clear understanding of the skills level of operation and maintenance personnel. After that, according to the skills portrait of the target operation and maintenance personnel, suitable skills improvement projects are allocated to the target operation and maintenance personnel. For example, for operation and maintenance personnel who perform excellently in a specific skills area, more advanced skills improvement projects can be allocated to further improve their professional level; for operation and maintenance personnel who have weaknesses in a certain skills area, targeted training courses or practice projects can be allocated to help them quickly make up for the deficiencies. Through reasonable allocation of skills improvement projects, financial enterprises can ensure the efficient utilization of operation and maintenance resources and promote the continuous growth and development of operation and maintenance personnel.

[0100] Through the operation and maintenance skills assessment system of this example, financial enterprises can accurately describe and evaluate the skills of operation and maintenance personnel, providing strong support for the reasonable and efficient allocation of operation and maintenance resources. At the same time, this system also helps financial enterprises optimize personnel allocation, improve operation and maintenance efficiency, and reduce operating costs. In the future, with the continuous deepening of the digital transformation of the financial industry, the application of operation and maintenance skills assessment methods will become more and more extensive, providing strong guarantee for the digital transformation of financial enterprises.

[0101] It should be emphasized that to further ensure the privacy and security of the above-mentioned skill practice data, training track data and other information, as well as other relevant data, the above-mentioned skill practice data, training track data and other information, as well as other relevant data can also be stored in a node of a blockchain.

[0102] The blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a string of data blocks generated by using cryptographic methods. Each data block contains information about a batch of network transactions, which is used to verify the validity (anti-counterfeiting) of the information and generate the next block. The blockchain can include the blockchain underlying platform, the platform product service layer, and the application service layer, etc.

[0103] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. These 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 methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), etc., or a Random Access Memory (RAM), etc.

[0104] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments. Their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0105] For further reference Figure 3 , as to the aboveFigure 2 For the implementation of the method shown, this application provides an embodiment of an operation and maintenance skill assessment device, and this device embodiment corresponds to Figure 2 the method embodiment shown, and this device can be specifically applied to various electronic devices.

[0106] As Figure 3 shown, the operation and maintenance skill assessment device 400 described in this embodiment includes: an acquisition module 401, an analysis module 402, a generation module 403, and an allocation module 404. Among them:

[0107] The acquisition module 401 is configured to acquire the skill practice data and training track data of the target operation and maintenance personnel;

[0108] The analysis module 402 is configured to use the trained skill assessment model to analyze the skill mastery status of the target operation and maintenance personnel according to the skill practice data, and analyze the skill training preferences of the target operation and maintenance personnel according to the training track data;

[0109] The generation module 403 is configured to generate a skill portrait of the target operation and maintenance personnel according to the skill mastery status and the skill training preferences;

[0110] The allocation module 404 is configured to allocate a target skill improvement project for the target operation and maintenance personnel according to the skill portrait.

[0111] In this embodiment, the acquisition module 401 is configured to acquire the skill practice data and training track data of the target operation and maintenance personnel. Among them, the skill practice data refers to the relevant data of the operation and maintenance personnel's practice of various operation and maintenance skill points, which may include but are not limited to the details of test questions related to the practice of operation and maintenance skill points, training scores, training durations, training progress, training habits, training difficulties, and training fields, etc.; the training track data refers to the relevant data of the operation and maintenance personnel's training of various operation and maintenance skill points, which may include but are not limited to the number of training times, training content, training online duration, and training sign-in status related to the training of operation and maintenance skill points, etc. The operation and maintenance skill points may include but are not limited to network configuration skills, troubleshooting skills, network security maintenance skills, database management skills, system installation skills, performance monitoring skills, etc. For example, for the operation and maintenance skill point "network configuration skill", the skill practice data of the target operation and maintenance personnel for this network configuration skill may be: the details of the test question are network configuration training test questions, the training score is 90 points, the training duration is 2 hours, the training progress is 90%, etc.; the training track data of the target operation and maintenance personnel for this network configuration skill may be: the number of training times is 3 times, the training content is theoretical learning, the training online duration is 2 hours, and the training sign-in status is full attendance, etc.

[0112] Specifically, the skill evaluation model is a trained machine learning or deep learning model. This model can learn and understand the internal laws and characteristics of the data based on the input skill practice data and training trajectory data, and identify the skill mastery status and skill training preferences of the target operation and maintenance personnel. The skill mastery status refers to the degree of mastery of each skill point by the target operation and maintenance personnel, which can include status classifications such as complete mastery, partial mastery, and complete absence. The skill training preference refers to the personal preferences and needs of the target operation and maintenance personnel in terms of training, which can include interested skill directions, preferred training times, favorite training methods, etc.

[0113] Specifically, the analysis module 402 is used to obtain the trained skill evaluation model, input the skill practice data and training trajectory data into the trained skill evaluation model, and use the trained skill evaluation model to analyze according to the input skill practice data to obtain the skill mastery status of the target operation and maintenance personnel. The skill mastery status can be a complete mastery status, a partial mastery status, a complete absence status, etc.; use the trained skill evaluation model to analyze according to the input training trajectory data to obtain the skill training preference of the target operation and maintenance personnel. The skill training preference can be an interested skill direction, a preferred training time, a favorite training method, etc.

[0114] Specifically, the skill portrait is a visualization tool used to describe the skill status of the target operation and maintenance personnel. By presenting the skill mastery status and skill training preferences of the target operation and maintenance personnel in a graphical way, the skill portrait can better display the skill status and development needs of the target operation and maintenance personnel.

[0115] Specifically, the generation module 403 is used to obtain the preset portrait structure, perform standardization processing on the skill mastery status and the skill training preference according to the data requirements of the portrait structure, and generate the technical portrait of the target operation and maintenance personnel based on the standardized data and the portrait structure.

[0116] Specifically, the skill improvement project refers to a training or learning plan designed to help operation and maintenance personnel improve specific skill points. The allocation module 404 is used to obtain the skill point requirements of a preset number of skill improvement projects, match the generated skill portrait with the skill point requirements of each skill improvement project, and obtain the target skill improvement project of the target operation and maintenance personnel.

[0117] The operation and maintenance skill assessment device 400 according to the embodiments of the present application can accurately evaluate the skill mastery status of the target operation and maintenance personnel by obtaining the skill practice data of the target operation and maintenance personnel and analyzing it using the trained skill assessment model; by obtaining the training track data of the target operation and maintenance personnel and analyzing it using the trained skill assessment model, it can identify the skill training preferences of the target operation and maintenance personnel; by combining the training track data and the actual skill practice data for targeted assessment, it helps to more comprehensively and accurately reflect the true skill mastery level of the target operation and maintenance personnel; by generating a skill portrait of the target operation and maintenance personnel based on the skill mastery status and skill training preferences, and allocating a target skill improvement project for the target operation and maintenance personnel according to the skill portrait, it can comprehensively display the current skill level and training preferences of the target operation and maintenance personnel through the skill portrait, and accurately match the target skill improvement project through the skill portrait. The project allocation method based on the skill portrait not only considers the current skill level of the target operation and maintenance personnel, but also combines the training preferences and development needs, thus ensuring the pertinence and effectiveness of the skill improvement project. Through reasonable and efficient allocation of skill improvement projects, it is ensured that the target operation and maintenance personnel can quickly improve their skill levels, thereby improving work efficiency and quality.

[0118] In some optional implementation manners of this embodiment, the operation and maintenance skill assessment device 400 may further include a preprocessing module, a label determination module, a first extraction module, and a second extraction module. Among them:

[0119] The preprocessing module is configured to preprocess the skill practice data and the training track data, remove the noise data, duplicate data, and abnormal data in the skill practice data and the training track data, and obtain the preprocessed skill practice data and the preprocessed training track data;

[0120] The label determination module is configured to determine whether the preprocessed skill practice data and the preprocessed training track data carry label information;

[0121] The first extraction module is configured to, if the preprocessed skill practice data carries label information, extract feature data from the label information of the preprocessed skill practice data to obtain the first skill practice feature;

[0122] The second extraction module is configured to, if the preprocessed training track data carries label information, extract feature data from the label information of the preprocessed training track data to obtain the first training track feature.

[0123] Specifically, a preprocessing module is configured to preprocess the obtained skill practice data and training trajectory data, identify noise data, duplicate data, and abnormal data in the skill practice data and training trajectory data, and remove the identified noise data, duplicate data, and abnormal data to obtain preprocessed skill practice data and preprocessed training trajectory data.

[0124] Specifically, a label determination module is configured to traverse each piece of preprocessed skill practice data and preprocessed training trajectory data to determine whether each piece of preprocessed skill practice data and preprocessed training trajectory data carries label information. For example, the label information of the preprocessed skill practice data can be the marked training results, training duration, training progress, etc.; the label information of the preprocessed training trajectory data can be the marked number of trainings, training content theme, training online duration, etc.

[0125] Specifically, a first extraction module is configured to, if the preprocessed skill practice data carries label information, extract feature data from the label information of the preprocessed skill practice data to obtain a first skill practice feature. A second extraction module is configured to, if the preprocessed training trajectory data carries label information, extract feature data from the label information of the preprocessed training trajectory data to obtain a first training trajectory feature. For example, if the label information carried by the preprocessed skill practice data is the training result, the training result and its score are extracted from the preprocessed skill practice data as the first skill practice feature; if the label information carried by the preprocessed training trajectory data is the number of trainings, the number of trainings and its specific number are extracted from the preprocessed training trajectory data as the first training trajectory feature.

[0126] The operation and maintenance skill evaluation device 400 according to the embodiment of the present application can significantly reduce the time cost of manual intervention and improve the data processing efficiency through an automated data preprocessing process. For labeled data, feature data is directly extracted from the label, which simplifies the feature extraction process and reduces the difficulty of feature engineering.

[0127] In some alternative implementation manners of this embodiment, the operation and maintenance skill evaluation device 400 may further include a third extraction module and a fourth extraction module. Among them:

[0128] The third extraction module is configured to, if the preprocessed skill practice data does not carry label information, perform semantic recognition and analysis on the preprocessed skill practice data to obtain a second skill practice feature;

[0129] The fourth extraction module is configured to, if the preprocessed training trajectory data does not carry label information, perform semantic recognition and analysis on the preprocessed training trajectory data to obtain a second training trajectory feature.

[0130] Specifically, the third extraction module is configured to perform semantic recognition and analysis on the preprocessed skill practice data to obtain second skill practice features if the preprocessed skill practice data does not carry label information. The fourth extraction module is configured to perform semantic recognition and analysis on the preprocessed training trajectory data to obtain second training trajectory features if the preprocessed training trajectory data does not carry label information. For example, it can be to perform word segmentation, stop word removal, stemming, etc. on the preprocessed skill practice data or preprocessed training trajectory data to obtain processed text data, use language analysis technology to extract text features from the processed text data, and perform semantic analysis on the extracted text features to obtain a semantic analysis result. Extract features related to skill practice from the semantic analysis result as the second skill practice features; or, extract features related to the training trajectory from the semantic analysis result as the second training trajectory features.

[0131] The operation and maintenance skill evaluation device 400 according to the embodiment of the present application can perform semantic recognition and analysis on the skill practice data and / or training trajectory data without labels to extract useful second skill practice features and / or second training trajectory features, and can convert unstructured data (such as text information) that is originally difficult to directly utilize into structured feature data, enhancing data understanding and usability.

[0132] In some optional implementation manners of this embodiment, the analysis module 402 may include a first acquisition sub-module, a first division sub-module, a first calculation sub-module, and a state determination sub-module, where:

[0133] The first acquisition sub-module is configured to acquire the first skill point type to which the skill practice data belongs;

[0134] The first division sub-module is configured to divide the skill practice data according to the first skill point type to obtain practice data sets corresponding to each first skill point type;

[0135] The first calculation sub-module is configured to calculate the proficiency level values of the target operation and maintenance personnel for each first skill point type according to the practice data sets corresponding to each first skill point type;

[0136] The state determination sub-module is configured to determine the skill mastery state of the target operation and maintenance personnel for each first skill point type according to the proficiency level values and preset thresholds.

[0137] Specifically, the first acquisition sub-module is used to obtain the first skill point type to which each skill practice data belongs by using the trained skill evaluation model. The first skill point type may include, but is not limited to, network configuration skills, troubleshooting skills, network security maintenance skills, database management skills, system installation skills, performance monitoring skills, etc.

[0138] Specifically, the first division sub-module is used to divide each skill practice data into the practice data set corresponding to the first skill point type according to the first skill point type to which the skill practice data belongs. Each practice data set corresponds to a first skill point type.

[0139] Specifically, the first calculation sub-module is used to perform data statistics according to the practice data sets corresponding to each first skill point type. For example, quantify the data in the practice data set and add the quantified values one by one to obtain the proficiency value of the target operation and maintenance personnel for each first skill point type.

[0140] Specifically, the status determination sub-module is used to preset one or more preset thresholds for each first skill point type. The preset threshold is used to measure the mastery degree of the target operation and maintenance personnel for this skill point. The preset threshold may include the correct rate of answering questions, the success rate of operations, the training completion progress, etc. Compare the proficiency values of each first skill point type with the corresponding preset thresholds to determine the skill mastery status of the target operation and maintenance personnel for each first skill point type.

[0141] The analysis module 402 of the embodiment of the present application realizes the fine division of the operation and maintenance skill practice data by obtaining the skill point type to which the skill practice data belongs and dividing the skill practice data according to the skill point type. Each skill point type corresponds to a data set, which facilitates subsequent analysis and calculation work, and also helps to reduce data redundancy and errors. By calculating the proficiency values of the target operation and maintenance personnel for each skill point type according to the practice data sets corresponding to each skill point type, the quantitative evaluation of the skill level of the target operation and maintenance personnel is realized; by determining the skill mastery status of the target operation and maintenance personnel for each skill point type according to the proficiency value and the preset threshold, it helps to quickly determine the mastery of the target operation and maintenance personnel on each skill point.

[0142] In some optional implementation manners of this embodiment, the analysis module 402 may include a second acquisition sub-module, a second division sub-module, a second calculation sub-module, and a preference ranking sub-module. Among them:

[0143] The second acquisition sub-module is used to obtain the second skill point type to which the training trajectory data belongs;

[0144] The second sub - division module is used to divide the training trajectory data according to the second skill point type to obtain training data sets corresponding to each second skill point type;

[0145] The second calculation sub - module is used to calculate the skill preference values of the target operation and maintenance personnel for each second skill point type according to the training data sets corresponding to each second skill training type;

[0146] The preference sorting sub - module is used to sort each second skill point type according to the skill preference values to obtain the skill training preference list of the target operation and maintenance personnel.

[0147] Specifically, the second acquisition sub - module is used to adopt the trained skill evaluation model to obtain the second skill point type to which each training trajectory data belongs. The second skill point type may include, but is not limited to, network configuration skills, troubleshooting skills, network security maintenance skills, database management skills, system installation skills, performance monitoring skills, etc.

[0148] Specifically, the second sub - division module is used to divide each training trajectory data into the training data set corresponding to the second skill point type according to the second skill point type to which the training trajectory data belongs. Each training data set corresponds to a second skill point type.

[0149] Specifically, the second calculation sub - module is used to perform data statistics according to the training data sets corresponding to each second skill point type. For example, quantify the data in the training data set and add up the quantified values one by one to obtain the skill preference values of the target operation and maintenance personnel for each second skill point type.

[0150] Specifically, the preference sorting sub - module is used to sort each second skill point type in descending order according to the calculated skill preference values to obtain the skill training preference list of the target operation and maintenance personnel. The skill training preference list reflects the preference degree of the target operation and maintenance personnel for each second skill point type.

[0151] The analysis module 402 of the embodiment of the present application realizes the accurate classification of operation and maintenance skill training data by obtaining the skill point type to which the training trajectory data belongs and dividing the training trajectory data according to the skill point type to obtain training data sets corresponding to each skill point type. By calculating the skill preference values of the target operation and maintenance personnel for each skill point type according to the training data sets corresponding to each skill training type, the quantitative evaluation of the skill preference of the target operation and maintenance personnel is realized; by sorting each skill point type according to the skill preference values, the skill training preference list of the target operation and maintenance personnel is obtained, which can clearly show the preference degree of the target operation and maintenance personnel on each skill point.

[0152] In some alternative implementation manners of this embodiment, the generation module 403 may include a base point determination sub-module, a base point mapping sub-module, a weighting processing sub-module, and a portrait generation sub-module. Among them:

[0153] The base point determination sub-module is configured to align the skill mastery status and the skill training preference according to the same skill point type, and use the same skill point type as the portrait base point;

[0154] The base point mapping sub-module is configured to map the skill mastery status and the skill training preference to the portrait base point according to the same skill point type, and obtain the mapped portrait base point data;

[0155] The weighting processing sub-module is configured to obtain the weight value of the same skill point type, and perform weighting processing on the portrait base point data according to the weight value to obtain the weighted portrait base point data;

[0156] The portrait generation sub-module is configured to obtain a preset portrait structure, and perform portrait integration according to the portrait structure and the weighted portrait base point data to generate the skill portrait of the target operation and maintenance personnel.

[0157] Specifically, the base point determination sub-module is configured to extract a first skill point type from the skill mastery status, extract a second skill point type from the skill training preference, compare the first skill point type with the second skill point type to determine the same skill point type. Align the skill mastery status and the skill training preference according to the same skill point type to obtain the skill mastery status and the skill training preference included in each same skill point type, and use each same skill point type as the portrait base point of the skill portrait.

[0158] Specifically, the base point mapping sub-module is configured to map the skill mastery status and the skill training preference according to the same skill point type, that is, map the skill mastery status (such as full mastery status, partial mastery status, full absence status) and the skill training preference (such as skill preference value) of the target operation and maintenance personnel on each skill point type to the corresponding portrait base point to obtain the mapped portrait base point data.

[0159] Specifically, the weighting processing sub-module is configured to obtain the weight values of each preset same skill point type, and the weight value reflects the importance and priority of different skill point types in the operation and maintenance work. Perform weighting processing on the mapped portrait base point data according to the weight values of each same skill point type to obtain the weighted portrait base point data.

[0160] Specifically, the image generation sub-module is used to obtain a preset image structure, integrate the image based on the image structure and the weighted image base point data, and during the integration process, fill the weighted image base point data into the corresponding positions of the image structure to generate the skill image of the target operation and maintenance personnel.

[0161] The generation module 403 of the embodiment of the present application extracts the skill point types from the skill mastery status and skill training preferences as the image base points, and maps the skill mastery status and skill training preferences to the image base points according to the skill point types, which helps to ensure that the image can comprehensively and accurately reflect the skill characteristics of the target operation and maintenance personnel. Based on the characteristics and requirements of the operation and maintenance work, reasonable weight values are assigned to each skill point type, and the weighted processing is performed on the mapped image base point data, realizing the quantitative evaluation of the importance of the operation and maintenance personnel's skills, so that the image can more accurately reflect the skill priorities and development directions of the target operation and maintenance personnel. By generating the skill image in a graphical way, the skill status and development needs of the target operation and maintenance personnel are intuitively displayed. This intuitiveness enables managers and target operation and maintenance personnel to easily understand the information conveyed by the image.

[0162] In some optional implementation manners of this embodiment, the allocation module 404 may include a requirement acquisition sub-module, a matching calculation sub-module, and a project determination sub-module. Among them:

[0163] The requirement acquisition sub-module is used to obtain the skill point requirements of each predefined skill improvement project;

[0164] The matching calculation sub-module is used to calculate the matching degree value between the skill image and the skill point requirements of each skill improvement project;

[0165] The project determination sub-module is used to obtain the skill improvement project with the highest matching degree value as the target skill improvement project of the target operation and maintenance personnel.

[0166] Specifically, the requirement acquisition sub-module is used to obtain the skill point requirements of each predefined skill improvement project, and the skill point requirements may include the skill point types that the project aims to improve, the prerequisite skills required, the recommended training duration, etc.

[0167] Specifically, the matching calculation sub-module is used to compare the skill image of the target operation and maintenance personnel with the skill point requirements of each skill improvement project, compare the mastery degree of each skill point in the skill image with the matching degree of the skill point requirements, and compare the preference degree of each skill point in the skill image with the matching degree of the skill point requirements, and calculate the corresponding matching degree value.

[0168] Specifically, the project determination sub-module is used to sort the calculated matching degree values of each skill improvement project from high to low, and obtain the skill improvement project with the highest matching degree value as the target skill improvement project of the target operation and maintenance personnel.

[0169] The allocation module 404 of the embodiment of the present application calculates the matching degree value between the skill portrait and the skill point requirements of each skill improvement project by obtaining the skill point requirements of each predefined skill improvement project and comparing them with the skill portrait of the target operation and maintenance personnel, and performs project allocation based on the matching degree value, which helps to make the optimal choice among multiple skill improvement projects and ensures that limited training resources can be efficiently and reasonably allocated to the operation and maintenance personnel who most need improvement.

[0170] To solve the above technical problems, the embodiment of the present application also provides a computer device. For details, please refer to Figure 4 , Figure 4 which is the basic structural block diagram of the computer device in this embodiment.

[0171] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are communicatively connected to each other through a system bus. It should be noted that only the computer device 6 with a memory 61, a processor 62, and a network interface 63 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of the present technology 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 (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0172] The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad or a voice control device and other means.

[0173] The memory 61 includes at least one type of readable storage medium, which includes flash memory, hard disk, multimedia card, card-type memory (such as 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, magnetic disk, optical disc, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc., equipped on the computer device 6. Of course, the memory 61 may also include both the internal storage unit and the external storage device of the computer device 6. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions of the operation and maintenance skill assessment method. In addition, the memory 61 may also be used to temporarily store various data that have been output or will be output.

[0174] In some embodiments, the processor 62 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to run the computer-readable instructions stored in the memory 61 or process data, such as running the computer-readable instructions of the operation and maintenance skill assessment method.

[0175] The network interface 63 may include a wireless network interface or a wired network interface, and the network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0176] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium storing computer-readable instructions, and the computer-readable instructions can be executed by at least one processor, so that the at least one processor executes the steps of the operation and maintenance skill assessment method as described above.

[0177] The computer device, computer-readable storage medium, and computer-readable instructions provided by the embodiments of the present application can accurately evaluate the skill mastery status of the target operation and maintenance personnel by the processor executing to obtain the skill practice data of the target operation and maintenance personnel and analyzing it using the trained skill evaluation model; by obtaining the training trajectory data of the target operation and maintenance personnel and analyzing it using the trained skill evaluation model, the skill training preferences of the target operation and maintenance personnel can be identified; by combining the training trajectory data and the actual skill practice data for targeted evaluation, it helps to more comprehensively and accurately reflect the true skill mastery level of the target operation and maintenance personnel; by generating a skill portrait of the target operation and maintenance personnel based on the skill mastery status and skill training preferences, and allocating a target skill improvement project for the target operation and maintenance personnel according to the skill portrait, the current skill level and training preferences of the target operation and maintenance personnel can be comprehensively displayed through the skill portrait, and the target skill improvement project can be accurately matched through the skill portrait. The project allocation method based on the skill portrait not only considers the current skill level of the target operation and maintenance personnel, but also combines the training preferences and development needs, thus ensuring the pertinence and effectiveness of the skill improvement project. Through reasonable and efficient allocation of skill improvement projects, it is ensured that the target operation and maintenance personnel can quickly improve their skill levels, thereby improving work efficiency and quality.

[0178] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented 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, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0179] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The preferred embodiments of the present application are given in the drawings, but 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 disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure made by using the specification and drawings of the present application, directly or indirectly applied in other related technical fields, is equally within the scope of the patent protection of the present application.

[0180] In the embodiments of this application, the software tools or components that are not of our company are only introduced by way of example and do not represent actual use.

Claims

1. A method for evaluating operation and maintenance skills, characterized in that: The steps include: Obtain the skill practice data and training trajectory data of the target operation and maintenance personnel; Using the trained skill assessment model, the skill mastery status of the target operation and maintenance personnel is obtained according to the skill practice data analysis, and the skill training preference of the target operation and maintenance personnel is obtained according to the training trajectory data analysis; Generate a skill profile of the target operation and maintenance personnel according to the skill mastery status and the skill training preference; A target skill improvement project is allocated to the target operation and maintenance personnel according to the skill profile.

2. The operation and maintenance skill evaluation method according to claim 1, characterized in that: The step of obtaining the skill mastering status of the target operation and maintenance personnel according to the skill practice data analysis includes: Acquire a first skill point type to which the skill practice data belongs; Dividing the skill practice data according to the first skill point type to obtain a practice data set corresponding to each first skill point type; Calculating the target operation and maintenance personnel's proficiency value for each first skill point type according to the practice data set corresponding to each first skill point type; The skill mastery status of the target operation and maintenance personnel for each first skill point type is determined according to the mastery proficiency value and a preset threshold.

3. The operation and maintenance skill evaluation method according to claim 1, characterized in that: The step of obtaining the skill training preference of the target operation and maintenance personnel according to the training trajectory data analysis includes: Obtaining a second skill point type to which the training trajectory data belongs; Dividing the training trajectory data according to the second skill point type to obtain a training data set corresponding to each second skill point type; Calculating the skill preference value of the target operation and maintenance personnel for each second skill point type according to the training data set corresponding to each second skill training type; The second skill point types are sorted according to the skill preference values ​​to obtain a skill training preference list of the target operation and maintenance personnel.

4. The operation and maintenance skill evaluation method according to claim 1, characterized in that: The step of generating the skill profile of the target operation and maintenance personnel according to the skill mastering status and the skill training preference includes: Aligning the skill mastering status and the skill training preference according to the same skill point type, and using the same skill point type as a profile base point; Mapping the skill mastering status and the skill training preference to the portrait base point according to the same skill point type to obtain mapped portrait base point data; Obtaining weight values ​​of the same skill point type, and performing weighted processing on the portrait base point data according to the weight values ​​to obtain weighted portrait base point data; A preset portrait structure is obtained, and portrait integration is performed according to the portrait structure and the weighted portrait base point data to generate a skill portrait of the target operation and maintenance personnel.

5. The operation and maintenance skill evaluation method according to claim 1, characterized in that: The step of allocating a target skill improvement project to the target operation and maintenance personnel according to the skill profile includes: Get the predefined skill point requirements for each skill improvement project; Calculate the matching value between the skill profile and the skill point requirements of each skill improvement project; The skill improvement project with the highest matching value is obtained as the target skill improvement project for the target operation and maintenance personnel.

6. The operation and maintenance skill evaluation method according to claim 1, characterized in that: After the step of obtaining the skill training data and training trajectory data of the target operation and maintenance personnel, the method further includes: Preprocessing the skill practice data and the training trajectory data to remove noise data, duplicate data and abnormal data in the skill practice data and the training trajectory data to obtain preprocessed skill practice data and preprocessed training trajectory data; Determining whether the preprocessed skill practice data and the preprocessed training trajectory data have label information; If the preprocessed skill practice data carries label information, extracting feature data from the label information of the preprocessed skill practice data to obtain a first skill practice feature; If the preprocessed training trajectory data carries label information, feature data is extracted from the label information of the preprocessed training trajectory data to obtain a first training trajectory feature.

7. The operation and maintenance skill evaluation method according to claim 6, characterized in that: After the step of determining whether the preprocessed skill practice data and the preprocessed training trajectory data have label information, the method further includes: If the preprocessed skill practice data does not carry label information, performing semantic recognition analysis on the preprocessed skill practice data to obtain a second skill practice feature; If the preprocessed training trajectory data does not carry label information, semantic recognition analysis is performed on the preprocessed training trajectory data to obtain a second training trajectory feature.

8. An operation and maintenance skill evaluation device, characterized in that: The device comprises: An acquisition module is used to obtain the skill practice data and training trajectory data of the target operation and maintenance personnel; An analysis module is used to use the trained skill assessment model to obtain the skill mastery status of the target operation and maintenance personnel according to the skill practice data analysis, and to obtain the skill training preference of the target operation and maintenance personnel according to the training trajectory data analysis; A generating module, used for generating a skill profile of the target operation and maintenance personnel according to the skill mastering status and the skill training preference; An allocation module is used to allocate target skill improvement projects to the target operation and maintenance personnel based on the skill profile.

9. A computer device, characterized in that: It includes 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 operation and maintenance skill 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, and when the computer-readable instructions are executed by a processor, the steps of the operation and maintenance skill assessment method as described in any one of claims 1 to 7 are implemented.

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