Automatic assessment method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the technical field of intelligent decision making, and relates to an automatic assessment method and device, computer equipment and a storage medium, and the method comprises the steps: reading a system database, and obtaining attribute data and performance data corresponding to an assessed identifier from the system database; performing feature extraction operation on the attribute data and the performance data according to a natural language processing technology to obtain an attribute feature vector and a performance feature vector; performing weight calculation operation on the attribute feature vector and the performance feature vector to obtain an attribute feature weight and a performance feature weight; and calculating the attribute feature vector, the performance feature vector, the attribute feature weight and the comprehensive score of the performance feature weight according to a weighted summation method to obtain an assessment score. According to the invention, the efficiency and accuracy of assessment are greatly improved; meanwhile, by introducing a natural language processing technology and a weight calculation method, the real situation of the examined object can be reflected more objectively, and deviation caused by human factors is reduced.

Description

Technical Field

[0001] The present application relates to the field of intelligent decision-making technology, and in particular to an automatic assessment method, device, computer equipment and storage medium. Background Art

[0002] Currently, employee evaluation is based on their daily activities, behaviors and performance.

[0003] However, the applicant found that the traditional evaluation method requires the assessor to verify the performance before making a comprehensive score. Such a score is subjective and cannot comprehensively and objectively evaluate the employee. In addition, the assessor must verify the employee's performance, which greatly increases the time and workload of the assessor in evaluating the employee's performance. It can be seen that the traditional evaluation method has the problem of low efficiency and accuracy. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide an automatic assessment method, apparatus, computer equipment and storage medium to solve the problems of low efficiency and accuracy of traditional assessment methods.

[0005] In order to solve the above technical problems, the present application embodiment provides an automatic assessment method, which adopts the following technical solution:

[0006] Receiving an assessment request carrying an assessment identifier sent by a user terminal;

[0007] Reading a system database, and obtaining attribute data and performance data corresponding to the assessed identifier in the system database;

[0008] Perform feature extraction operations on the attribute data and the performance data according to natural language processing technology to obtain an attribute feature vector and a performance feature vector;

[0009] Performing a weight calculation operation on the attribute feature vector and the performance feature vector to obtain an attribute feature weight and a performance feature weight;

[0010] Calculate the comprehensive score of the attribute feature vector, the performance feature vector, the attribute feature weight, and the performance feature weight according to the weighted sum method to obtain an assessment score;

[0011] The assessment score is output to the user terminal.

[0012] Furthermore, the step of reading the system database and obtaining the attribute data and performance data corresponding to the assessed identifier in the system database specifically includes the following steps:

[0013] Acquiring unstructured attribute data corresponding to the assessed identifier in the system database;

[0014] A text conversion operation is performed on the unstructured attribute data to obtain the attribute data.

[0015] Furthermore, after the step of reading the system database and acquiring the attribute data and performance data corresponding to the assessed identifier in the system database, the following step is also included:

[0016] Determine whether there are missing data in the attribute data and the performance data according to data exploration and statistical analysis methods;

[0017] If there are missing data in the attribute data and the performance data, a data interpolation operation is performed on the missing data according to a similarity K-nearest neighbor algorithm;

[0018] If there is no missing data in the attribute data and the performance data, the data interpolation operation is not performed.

[0019] Furthermore, the step of performing a weight calculation operation on the attribute feature vector and the performance feature vector to obtain the attribute feature weight and the performance feature weight specifically includes the following steps:

[0020] Determining the importance of the attribute feature vector and the performance feature vector according to the assessment index system;

[0021] The importance is calculated according to the hierarchical analysis method to obtain the attribute feature weight and the performance feature weight.

[0022] Furthermore, the step of calculating the comprehensive score of the attribute feature vector, the performance feature vector, the attribute feature weight and the performance feature weight according to the weighted sum method to obtain the assessment score specifically includes the following steps:

[0023] Calculating the score interval data of the attribute feature weight and the performance feature weight according to the weighted sum method;

[0024] Obtaining a machine learning prediction model corresponding to the score interval data;

[0025] The attribute feature vector and the performance feature vector are input into the machine learning prediction model to perform a score prediction operation to obtain the assessment score.

[0026] Furthermore, the step of outputting the assessment score to the user terminal specifically includes the following steps:

[0027] Displaying the attribute data and the scores of the performance data according to a bar graph;

[0028] Use line graphs to show the growth trend of the person being assessed during different assessment cycles;

[0029] The radar chart shows the balance of the examinee’s performance in different dimensions.

[0030] In order to solve the above technical problems, the embodiment of the present application also provides an automatic assessment device, which adopts the following technical solution:

[0031] A request receiving module, used for receiving an assessment request carrying an assessment identifier sent by a user terminal;

[0032] A data acquisition module, used for reading a system database, and acquiring attribute data and performance data corresponding to the assessed identifier in the system database;

[0033] A feature extraction module, used to perform feature extraction operations on the attribute data and the performance data according to natural language processing technology to obtain an attribute feature vector and a performance feature vector;

[0034] A weight calculation module, used to perform a weight calculation operation on the attribute feature vector and the performance feature vector to obtain an attribute feature weight and a performance feature weight;

[0035] A score calculation module, used to calculate the comprehensive score of the attribute feature vector, the performance feature vector, the attribute feature weight and the performance feature weight according to the weighted sum method to obtain an assessment score;

[0036] The score output module is used to output the assessment score to the user terminal.

[0037] Furthermore, the data acquisition module includes:

[0038] An unstructured attribute data acquisition submodule, used to acquire the unstructured attribute data corresponding to the assessed identifier in the system database;

[0039] The text conversion submodule is used to perform a text conversion operation on the unstructured attribute data to obtain the attribute data.

[0040] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0041] It includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the automatic assessment method as described above when executing the computer-readable instructions.

[0042] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0043] The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the automatic assessment method described above are implemented.

[0044] The present application provides an automatic assessment method, including: receiving an assessment request carrying an assessment identifier sent by a user terminal; reading a system database, obtaining attribute data and performance data corresponding to the assessment identifier in the system database; performing feature extraction operations on the attribute data and performance data according to natural language processing technology to obtain attribute feature vectors and performance feature vectors; performing weight calculation operations on the attribute feature vectors and the performance feature vectors to obtain attribute feature weights and performance feature weights; calculating the comprehensive scores of the attribute feature vectors, the performance feature vectors, the attribute feature weights and the performance feature weights according to the weighted summation method to obtain an assessment score; and outputting the assessment score to the user terminal. Compared with the prior art, the present application greatly improves the efficiency and accuracy of the assessment; at the same time, by introducing natural language processing technology and weight calculation methods, it can also more objectively reflect the real situation of the assessed object and reduce the deviation caused by human factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0047] Figure 2 is a flowchart for implementing the automatic assessment method provided in the embodiment of the present application;

[0048] Figure 3 It is a structural schematic diagram of the automatic assessment device provided in the embodiment of the present application;

[0049] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0051] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0052] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0053] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0054] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0055] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 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), a laptop computer, a desktop computer, etc.

[0056] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0057] It should be noted that the automatic assessment method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the automatic assessment device is generally set in the server / terminal device.

[0058] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0059] Continue to refer Figure 2 , shows a flow chart of an embodiment of the automatic assessment method according to the present application. The automatic assessment method comprises: step S201, step S202, step S203, step S204, step S205 and step S206.

[0060] In step S201, a test request carrying a test identification sent by a user terminal is received.

[0061] In the embodiments of the present application, the user terminal refers to a terminal device used to execute the image processing method for preventing document abuse provided by the present application. The user terminal can be a mobile terminal such as a mobile phone, a smart phone, a laptop computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, etc., as well as a fixed terminal such as a digital TV, a desktop computer, etc. It should be understood that the examples of user terminals here are only for the convenience of understanding and are not used to limit the present application.

[0062] In step S202, the system database is read to obtain the attribute data and performance data corresponding to the assessed identifier in the system database.

[0063] In the embodiment of the present application, after receiving the request, the system will access its internal database according to the provided identification of the person being assessed. The database stores various information related to the person being assessed, including attribute data (static information such as age, gender, position, education, etc.) and performance data (dynamic performance such as work performance, academic performance, attendance rate, etc.). The system will retrieve the corresponding data according to the identification for subsequent processing.

[0064] In step S203, feature extraction operations are performed on the attribute data and the performance data according to natural language processing technology to obtain an attribute feature vector and a performance feature vector.

[0065] In the embodiments of the present application, the extracted data may be unstructured or contain a lot of redundant information, so it is necessary to use natural language processing technology (NLP) to extract features from these data. NLP technology can identify and extract key information from the data and convert it into a form that can be understood and processed by a computer, such as a vector or a numerical value.

[0066] In an embodiment of the present application, with respect to the unstructured attributes and performance data of the examinee, named entity recognition and keyword extraction algorithms in natural language processing technology are used to identify and extract key information that can characterize the attributes and performance of the examinee from text, voice, video and other data. The extracted key information is structured through knowledge graph construction technology to form standardized attribute and performance feature vectors. The purpose of this step is to convert attribute data and performance data into feature vectors that can be used for subsequent analysis and comparison.

[0067] In step S204, a weight calculation operation is performed on the attribute feature vector and the performance feature vector to obtain an attribute feature weight and a performance feature weight.

[0068] In the embodiment of the present application, different attributes or performances may have different importance for the assessment, so it is necessary to calculate the weight of each feature vector.

[0069] In the embodiment of the present application, after obtaining the structured attribute and performance feature vectors, a weighted sum method is used to calculate the comprehensive score. The quantized value of each feature is multiplied by the corresponding weight coefficient, and then the sum is calculated to obtain the final assessment score.

[0070] In the embodiment of the present application, in order to enhance the interpretability of the assessment results, the weighted summation process is gradually decomposed to form a reasoning chain for the score calculation.

[0071] In step S205, the comprehensive scores of the attribute feature vector, the performance feature vector, the attribute feature weight and the performance feature weight are calculated according to the weighted sum method to obtain the assessment score.

[0072] In the embodiment of the present application, after determining the feature vectors and the corresponding weights, the system uses the weighted summation method to calculate the comprehensive score of the assessment object. This method multiplies the value of each feature vector by its weight, and then adds all the products to obtain the final assessment score. This score reflects the comprehensive situation of the assessed object in terms of both attributes and performance.

[0073] In step S206, the assessment score is output to the user terminal.

[0074] In an embodiment of the present application, an automatic assessment method is provided, including: receiving an assessment request carrying an assessment identifier sent by a user terminal; reading a system database, obtaining attribute data and performance data corresponding to the assessment identifier in the system database; performing feature extraction operations on the attribute data and the performance data according to natural language processing technology to obtain attribute feature vectors and performance feature vectors; performing weight calculation operations on the attribute feature vectors and the performance feature vectors to obtain attribute feature weights and performance feature weights; calculating the comprehensive scores of the attribute feature vectors, the performance feature vectors, the attribute feature weights and the performance feature weights according to the weighted summation method to obtain the assessment score; outputting the assessment score to the user terminal. Compared with the prior art, the present application greatly improves the efficiency and accuracy of the assessment; at the same time, by introducing natural language processing technology and weight calculation methods, it can also more objectively reflect the real situation of the assessed object and reduce the deviation caused by human factors.

[0075] In some optional implementations of the embodiments of the present application, the above-mentioned step of reading the system database and obtaining the attribute data and performance data corresponding to the assessed identifier in the system database specifically includes the following steps:

[0076] Obtaining unstructured attribute data corresponding to the assessed identifier in the system database;

[0077] Perform text conversion operations on unstructured attribute data to obtain attribute data.

[0078] In an embodiment of the present application, unstructured attribute data associated with the identification of the assessed object (such as employee ID, student number, etc.) is retrieved from the system database, where the unstructured attribute data refers to data types that cannot be effectively retrieved and processed directly through traditional database query languages ​​(such as SQL), and they may exist in the form of text, images, audio, video, etc.

[0079] In the embodiment of the present application, in the database, the data associated with the identification of the subject to be assessed may be stored in various forms, including but not limited to text fields in tables, JSON or XML documents in document databases, and even metadata of image or video files. The system needs to be able to identify and retrieve these unstructured attribute data.

[0080] In the embodiments of the present application, since the unstructured attribute data are in various forms, it is very difficult to process them directly. Therefore, it is usually necessary to convert them into text form in order to perform text analysis, feature extraction and other operations. Specifically:

[0081] Text extraction: For unstructured attribute data that is already in text form (such as documents, emails, etc.), this step is just a simple reading and extraction operation. However, for non-text data such as images, audio or video, specific technologies (such as optical character recognition OCR, speech recognition, etc.) are required to convert them into text.

[0082] Data cleaning: The converted text data contains noise, irrelevant information, or inconsistent formats. Therefore, data cleaning operations are required to remove these interference factors and ensure the accuracy and consistency of the data.

[0083] Structural processing: After cleaning, the text data still exists in an unstructured form (such as long paragraphs of text). In order to facilitate subsequent processing and analysis, the system needs to further structure this data, such as extracting key information through natural language processing technologies such as word segmentation, part-of-speech tagging, and named entity recognition, and organizing it into structured attribute data.

[0084] In the embodiment of the present application, the system can obtain unstructured attribute data related to the subject of assessment from the system database and convert it into attribute data that can be used in the subsequent assessment process. This provides a basis for subsequent steps such as text analysis, feature extraction, weight calculation and comprehensive scoring.

[0085] In some optional implementations of the embodiments of the present application, after the step of reading the system database and obtaining the attribute data and performance data corresponding to the assessed identifier in the system database, the following steps are also included:

[0086] Determine whether there are missing data in attribute data and performance data based on data exploration and statistical analysis methods;

[0087] If there are missing data in the attribute data and performance data, the missing data will be interpolated according to the similarity K-nearest neighbor algorithm;

[0088] If there is no missing data in the attribute data and performance data, no data interpolation operation is performed.

[0089] In the embodiments of the present application, data exploration involves preliminary visualization and descriptive statistical analysis of the data. By drawing charts such as histograms, box plots, scatter plots, and calculating statistics such as mean, median, and standard deviation, the distribution and characteristics of the data can be intuitively understood, and a preliminary judgment can be made as to whether there are outliers or missing values.

[0090] In the embodiments of the present application, in addition to data exploration, more specific statistical analysis methods can also be used to detect missing data. For example, the non-null value ratio of each attribute can be calculated. If the non-null value ratio of a certain attribute is significantly lower than that of other attributes, then there may be missing data. In addition, statistical test methods (such as chi-square test, T test, etc.) can also be used to detect whether the data is complete.

[0091] In the embodiments of the present application, when it is determined that there are missing data in the data set, appropriate strategies need to be adopted to fill these missing values ​​to ensure the integrity of the data and the effectiveness of subsequent analysis, specifically:

[0092] K-nearest neighbor algorithm principle: KNN algorithm is based on the following assumption: in feature space, if the attribute value of the K nearest neighbors (i.e. the K most similar samples) near a sample is known, then the missing attribute value of the sample can be estimated by the corresponding attribute value of these K neighbors. Specifically, the mean, median or mode of the corresponding attribute values ​​of these K neighbors can be selected as the interpolation value.

[0093] Data interpolation process: First, the system needs to determine the K value (i.e. the number of neighbors), which needs to be determined based on the specific situation of the data and the experimental effect. Then, for each sample containing missing values, the system will find its K nearest neighbors in the feature space. Finally, the missing values ​​of the sample are estimated and filled based on the corresponding attribute values ​​of these neighbors.

[0094] In the embodiment of the present application, for the problem of missing data and noise interference, the distribution of missing data and the type of noise data are first determined through data exploration and statistical analysis. For missing data, the K nearest neighbor algorithm based on similarity is used for data interpolation, that is, the missing value is estimated based on the value of the complete data point similar to the missing data point. For noisy data, the isolation forest algorithm is used to detect and remove outliers, and the data interfered by noise is repaired using data smoothing technology.

[0095] In the embodiments of the present application, the integrity of the data is determined by data exploration and statistical analysis, and the K nearest neighbor algorithm is used to perform data interpolation operations as needed, which is an important step to ensure the quality of the data and the effectiveness of subsequent analysis. These steps provide a reliable data basis for the subsequent assessment process.

[0096] In some optional implementations of the embodiments of the present application, the above-mentioned step of performing weight calculation operations on the attribute feature vector and the performance feature vector to obtain the attribute feature weight and the performance feature weight specifically includes the following steps:

[0097] Determine the importance of attribute feature vectors and performance feature vectors based on the assessment index system;

[0098] The importance is calculated according to the hierarchical analysis method to obtain the attribute feature weight and the performance feature weight.

[0099] In the embodiment of the present application, the importance of each attribute and performance feature is determined according to the assessment index system, and the weight coefficient of each feature is calculated by the hierarchical analysis method. In the weight calculation process, the correlation between the features is determined by correlation analysis, and highly correlated features are merged or eliminated to eliminate information redundancy. At the same time, fairness constraints are introduced to control the distribution range of weight coefficients to avoid unfair assessment problems caused by weight imbalance.

[0100] In some optional implementations of the embodiments of the present application, the above step of calculating the comprehensive score of the attribute feature vector, the performance feature vector, the attribute feature weight, and the performance feature weight according to the weighted sum method to obtain the assessment score specifically includes the following steps:

[0101] Calculate the attribute feature weights and the score interval data of the performance feature weights according to the weighted sum method;

[0102] Obtain a machine learning prediction model corresponding to the score interval data;

[0103] The attribute feature vector and the performance feature vector are input into the machine learning prediction model to perform score prediction operations to obtain the assessment score.

[0104] In the embodiment of the present application, in order to comprehensively evaluate the attributes and performance of the object being assessed, we need to perform a weighted summation of each feature. Among them, the weighted summation method assigns a weight to each feature, then multiplies the value of each feature by its corresponding weight, and finally adds up all the products to obtain a comprehensive score. In this process, the allocation of weights is usually based on expert experience, historical data or statistical analysis results to reflect the importance of different features in the assessment.

[0105] In the embodiment of the present application, for the attribute features and the performance features, we can divide them into different score intervals according to the weighted summation results. These intervals can be determined based on actual business needs or statistical analysis. For example, the scores can be divided into several levels such as excellent, good, average, poor, etc., or divided according to a specific numerical range.

[0106] In the embodiment of the present application, after the score interval data is determined, the next step is to select or construct a machine learning prediction model corresponding to the data. This model will be used to predict the assessment score based on the attribute feature vector and the performance feature vector, specifically:

[0107] Model selection: Depending on the characteristics of the data and the specific requirements of the assessment, we can choose different types of machine learning models. For example, if the data is linear, we can choose a linear regression model; if there is a complex nonlinear relationship between the data, we can choose models such as support vector machines (SVM), random forests, or neural networks.

[0108] Model training: After choosing a model, we need to train the model using historical data or a training dataset. This involves dividing the data into a training set and a test set, using the training set to optimize the model’s parameters, and then using the test set to evaluate the model’s performance.

[0109] Model evaluation: To ensure the accuracy and reliability of the model, we need to evaluate the model. This can be achieved by calculating the model's accuracy, recall, F1 score and other indicators. If the performance of the model does not meet the requirements, we need to adjust the model or choose another model.

[0110] In an embodiment of the present application, the score interval data is calculated by the weighted sum method, a machine learning prediction model corresponding to these data is obtained, and the attribute feature vector and performance feature vector are input into the model for score prediction, which can realize an automated and intelligent assessment process and improve the efficiency and accuracy of the assessment.

[0111] In some optional implementations of the embodiments of the present application, the step of outputting the assessment score to the user terminal specifically includes the following steps:

[0112] Display the scores of attribute data and performance data based on bar charts;

[0113] Use line graphs to show the growth trend of the person being assessed during different assessment cycles;

[0114] The radar chart shows the balance of the examinee’s performance in different dimensions.

[0115] In the embodiment of the present application, in order to improve the visual presentation effect of the assessment results, a visual dashboard of the assessment results is designed. The scores of various attributes and performances are displayed through bar charts, the growth trend of the assessee in different assessment cycles is displayed through line charts, and the performance balance of the assessee in different dimensions is displayed through radar charts. At the same time, an interactive visual exploration function is provided, allowing users to interact with the visualization components through operations such as hovering the mouse and clicking, and obtain more detailed information about the assessment results.

[0116] In some optional implementations of the embodiments of the present application, in response to the interpretability requirements of the assessment model, a rule-based decision tree algorithm is used to construct an assessment decision model. The decision tree model represents the assessment rules in the form of a tree structure, each non-leaf node represents an assessment attribute, and each leaf node corresponds to an assessment decision result. By recursively dividing the attributes and performance characteristics, an assessment decision path is generated. The decision tree model has good interpretability and can generate decision rules such as "If the value of an attribute is A, and the score of a performance is greater than B, then the assessment result is C", which is easy for the assessor and the assessee to understand and accept.

[0117] In some optional implementations of the embodiments of the present application, taking into account the new assessment requirements and data changes that may arise during the assessment process, the assessment model is continuously optimized using incremental learning technology. When new assessment data is added, the model parameters are dynamically updated through the incremental learning algorithm so that the model can adapt to changes in data distribution. At the same time, a model performance monitoring mechanism is set up to trigger the model retraining process when the model performance drops significantly to ensure the accuracy and stability of the assessment results.

[0118] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0119] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0120] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0121] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0122] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of an automatic assessment device, which is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0123] like Figure 3 As shown, the automatic assessment device 200 of the embodiment of the present application includes:

[0124] The request receiving module 210 is used to receive an assessment request carrying an assessment identifier sent by a user terminal;

[0125] The data acquisition module 220 is used to read the system database and acquire the attribute data and performance data corresponding to the assessed identifier in the system database;

[0126] A feature extraction module 230 is used to perform feature extraction operations on the attribute data and the performance data according to natural language processing technology to obtain an attribute feature vector and a performance feature vector;

[0127] The weight calculation module 240 is used to perform a weight calculation operation on the attribute feature vector and the performance feature vector to obtain the attribute feature weight and the performance feature weight;

[0128] The score calculation module 250 is used to calculate the comprehensive score of the attribute feature vector, the performance feature vector, the attribute feature weight and the performance feature weight according to the weighted sum method to obtain the assessment score;

[0129] The score output module 260 is used to output the assessment score to the user terminal.

[0130] In the embodiment of the present application, an automatic assessment device 200 is provided, including: a request receiving module 210, for receiving an assessment request carrying an assessment identifier sent by a user terminal; a data acquisition module 220, for reading a system database, and obtaining attribute data and performance data corresponding to the assessment identifier in the system database; a feature extraction module 230, for performing feature extraction operations on attribute data and performance data according to natural language processing technology, and obtaining attribute feature vectors and performance feature vectors; a weight calculation module 240, for performing weight calculation operations on attribute feature vectors and performance feature vectors, and obtaining attribute feature weights and performance feature weights; a score calculation module 250, for calculating the comprehensive score of attribute feature vectors, performance feature vectors, attribute feature weights and performance feature weights according to a weighted summation method, and obtaining an assessment score; a score output module 260, for outputting the assessment score to the user terminal. Compared with the prior art, the present application greatly improves the efficiency and accuracy of the assessment; at the same time, by introducing natural language processing technology and weight calculation methods, it can also more objectively reflect the real situation of the assessed object and reduce the deviation caused by human factors.

[0131] In some optional implementations of the embodiments of the present application, the data acquisition module includes:

[0132] The unstructured attribute data acquisition submodule is used to acquire the unstructured attribute data corresponding to the assessed identifier in the system database;

[0133] The text conversion submodule is used to perform text conversion operations on unstructured attribute data to obtain attribute data.

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

[0135] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 300 having components 310-330, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

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

[0137] The memory 310 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 310 can be an internal storage unit of the computer device 300, such as a hard disk or memory of the computer device 300. In other embodiments, the memory 310 can also be an external storage device of the computer device 300, such as a plug-in hard disk equipped on the computer device 300, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Of course, the memory 310 can also include both the internal storage unit of the computer device 300 and its external storage device. In the embodiment of the present application, the memory 310 is generally used to store the operating system and various application software installed on the computer device 300, such as computer-readable instructions of the automatic assessment method, etc. In addition, the memory 310 can also be used to temporarily store various data that have been output or are to be output.

[0138] The processor 320 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 320 is generally used to control the overall operation of the computer device 300. In the embodiment of the present application, the processor 320 is used to run the computer-readable instructions stored in the memory 310 or process data, such as computer-readable instructions for running the automatic assessment method.

[0139] The network interface 330 may include a wireless network interface or a wired network interface. The network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0140] The computer equipment provided by this application greatly improves the efficiency and accuracy of the assessment; at the same time, by introducing natural language processing technology and weight calculation methods, it can also more objectively reflect the actual situation of the assessed object and reduce the deviation caused by human factors.

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

[0142] The computer-readable storage medium provided by the present application greatly improves the efficiency and accuracy of the assessment; at the same time, by introducing natural language processing technology and weight calculation methods, it can also more objectively reflect the actual situation of the assessed object and reduce the deviation caused by human factors.

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

[0144] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. An automatic assessment method, characterized in that: The steps include: Receiving an assessment request carrying an assessment identifier sent by a user terminal; Reading a system database, and obtaining attribute data and performance data corresponding to the assessed identifier in the system database; Perform feature extraction operations on the attribute data and the performance data according to natural language processing technology to obtain an attribute feature vector and a performance feature vector; Performing a weight calculation operation on the attribute feature vector and the performance feature vector to obtain an attribute feature weight and a performance feature weight; Calculate the comprehensive score of the attribute feature vector, the performance feature vector, the attribute feature weight, and the performance feature weight according to the weighted sum method to obtain an assessment score; The assessment score is output to the user terminal.

2. The automatic assessment method according to claim 1, characterized in that: The step of reading the system database and obtaining the attribute data and performance data corresponding to the assessed identifier in the system database specifically includes the following steps: Acquiring unstructured attribute data corresponding to the assessed identifier in the system database; A text conversion operation is performed on the unstructured attribute data to obtain the attribute data.

3. The automatic assessment method according to claim 1, characterized in that: After the step of reading the system database and obtaining the attribute data and performance data corresponding to the assessed identifier in the system database, the following step is also included: Determine whether there are missing data in the attribute data and the performance data according to data exploration and statistical analysis methods; If there are missing data in the attribute data and the performance data, a data interpolation operation is performed on the missing data according to a similarity K-nearest neighbor algorithm; If there is no missing data in the attribute data and the performance data, the data interpolation operation is not performed.

4. The automatic assessment method according to claim 1, characterized in that: The step of performing a weight calculation operation on the attribute feature vector and the performance feature vector to obtain the attribute feature weight and the performance feature weight specifically includes the following steps: Determining the importance of the attribute feature vector and the performance feature vector according to the assessment index system; The importance is calculated according to the hierarchical analysis method to obtain the attribute feature weight and the performance feature weight.

5. The automatic assessment method according to claim 1, characterized in that: The step of calculating the comprehensive score of the attribute feature vector, the performance feature vector, the attribute feature weight and the performance feature weight according to the weighted sum method to obtain the assessment score specifically includes the following steps: Calculating the score interval data of the attribute feature weight and the performance feature weight according to the weighted sum method; Obtaining a machine learning prediction model corresponding to the score interval data; The attribute feature vector and the performance feature vector are input into the machine learning prediction model to perform a score prediction operation to obtain the assessment score.

6. The automatic assessment method according to claim 1, characterized in that: The step of outputting the assessment score to the user terminal specifically includes the following steps: Displaying the attribute data and the scores of the performance data according to a bar graph; Use line graphs to show the growth trend of the person being assessed during different assessment cycles; The radar chart shows the balance of the examinee’s performance in different dimensions.

7. An automatic assessment device, characterized in that: include: A request receiving module, used for receiving an assessment request carrying an assessment identifier sent by a user terminal; A data acquisition module, used for reading a system database, and acquiring attribute data and performance data corresponding to the assessed identifier in the system database; A feature extraction module, used to perform feature extraction operations on the attribute data and the performance data according to natural language processing technology to obtain an attribute feature vector and a performance feature vector; A weight calculation module, used to perform a weight calculation operation on the attribute feature vector and the performance feature vector to obtain an attribute feature weight and a performance feature weight; A score calculation module, used to calculate the comprehensive score of the attribute feature vector, the performance feature vector, the attribute feature weight and the performance feature weight according to the weighted sum method to obtain an assessment score; The score output module is used to output the assessment score to the user terminal.

8. The automatic assessment device according to claim 7, characterized in that: The data acquisition module comprises: An unstructured attribute data acquisition submodule, used to acquire the unstructured attribute data corresponding to the assessed identifier in the system database; The text conversion submodule is used to perform a text conversion operation on the unstructured attribute data to obtain the attribute data.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the automatic assessment method according to any one of claims 1 to 6 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 automatic assessment method according to any one of claims 1 to 6 are implemented.

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