Object evaluation method and device
By processing and analyzing the object attribute information of the evaluated object, we can deeply explore the potential value behind the data, and solve the problems of the traditional talent evaluation method that is subjective, inefficient and difficult to comprehensively evaluate, achieving a more objective and fair Evaluation results of depth and breadth.
Patent Information
- Application Number
- CN202411312930.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-20
- Publication Date
- 2025-02-14
AI Technical Summary
Traditional talent evaluation methods have problems such as strong subjectivity, low efficiency, and difficulty in comprehensive evaluation.
By obtaining the object attribute information of the object to be evaluated, performing feature extraction, determining the evaluation results of multiple evaluation dimensions, and generating an evaluation result report.
It improves the depth and breadth of evaluation, reduces the burden of manual review, improves the efficiency and accuracy of evaluation, and makes the evaluation results more objective and fair.
Smart Images
Figure CN119443889A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to an object evaluation method and device. Background Art
[0002] With the rapid development of science and technology, talent evaluation has become one of the important tasks of enterprises, educational institutions, government departments and other organizations. Traditional talent evaluation methods often rely on manual review, questionnaires or simple data analysis, which are highly subjective, inefficient and difficult to evaluate comprehensively. Summary of the invention
[0003] The present application provides an object evaluation method and device to solve the problems existing in the prior art such as strong subjectivity, low efficiency, and difficulty in comprehensive evaluation.
[0004] In a first aspect, the present application provides an object evaluation method, the method comprising:
[0005] Obtaining object attribute information of the object to be evaluated; wherein the object attribute information includes attribute information of the object to be evaluated under a plurality of first dimensions;
[0006] Extracting features from the object attribute information to obtain a number of key feature information of the object to be evaluated; the key feature information of the object to be evaluated includes features of the object to be evaluated in a first dimension;
[0007] Determine evaluation results under several second dimensions according to several key feature information of the object to be evaluated;
[0008] An evaluation result report of the object to be evaluated is generated according to the evaluation results under the plurality of second dimensions.
[0009] In a second aspect, the present application provides an object evaluation device, the device comprising:
[0010] A first unit is used to obtain object attribute information of an object to be evaluated; wherein the object attribute information includes attribute information of the object to be evaluated under a plurality of first dimensions;
[0011] The second unit is used to extract features from the object attribute information to obtain some key feature information of the object to be evaluated; the key feature information of the object to be evaluated includes features of the object to be evaluated in a first dimension;
[0012] The third unit is used to determine the evaluation results under the second dimension according to the key feature information of the object to be evaluated;
[0013] The fourth unit is used to generate an evaluation result report of the object to be evaluated according to the evaluation results under the plurality of second dimensions.
[0014] In a third aspect, the present application provides a readable medium comprising execution instructions. When a processor of an electronic device executes the execution instructions, the electronic device executes any method as described in the first aspect.
[0015] In a fourth aspect, the present application provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor executes any method described in the first aspect.
[0016] It can be seen from the above technical scheme that the present application provides an object evaluation method, which can obtain object attribute information of an object to be evaluated; wherein, the object attribute information includes attribute information of the object to be evaluated under several first dimensions. Then, feature extraction can be performed on the object attribute information to obtain several key feature information of the object to be evaluated; the key feature information of the object to be evaluated includes the features of the object to be evaluated under a first dimension. Then, based on the several key feature information of the object to be evaluated, the evaluation results under several second dimensions can be determined. Finally, based on the evaluation results under the several second dimensions, an evaluation result report of the object to be evaluated can be generated. It can be seen that this application can process and analyze the object attribute information of the object to be evaluated, deeply explore the potential value behind the data, discover the relationship and rules between the data, and provide more abundant and accurate information support for talent evaluation, which not only improves the depth and breadth of the evaluation, but also provides strong data support for organizational decision-making; in this way, this application can comprehensively evaluate the candidate's skill level, comprehensive quality, development potential and other aspects, overcome the limitations of the single dimension of traditional evaluation methods, and improve the comprehensiveness and accuracy of the evaluation, which not only reduces the burden of manual review, but also improves the efficiency and accuracy of the evaluation, making the evaluation results more objective and fair. In addition, this application can present complex evaluation results to users in an intuitive and easy-to-understand evaluation result report, which not only facilitates users to understand and use the evaluation results, but also improves user satisfaction.
[0017] The further effects of the above-mentioned non-conventional preferred manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the embodiments of the present application or the existing technical solutions, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.
[0019] Figure 1 A flowchart of an object evaluation method provided for this application;
[0020] Figure 2 A schematic diagram of the system architecture of an object evaluation system provided in this application;
[0021] Figure 3 A schematic diagram of the structure of an object evaluation device provided in this application;
[0022] Figure 4 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION
[0023] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.
[0024] The inventors found that talent evaluation has become one of the important tasks of enterprises, educational institutions, government departments and other organizations. Traditional talent evaluation methods often rely on manual review, questionnaires or simple data analysis. These methods have the problems of strong subjectivity, low efficiency and difficulty in comprehensive evaluation. Specifically, the shortcomings of the existing technology include:
[0025] 1. Single evaluation dimension: Since only a few key characteristics are focused on, the evaluation results are one-sided and cannot fully reflect the candidate's true ability.
[0026] 2. Poor model adaptability: The existing model lacks flexibility and scalability, and is difficult to adapt to complex and changing evaluation scenarios and needs.
[0027] 3. Insufficient data processing capabilities: The data processing and analysis methods are simple and cannot fully utilize big data resources to dig out more valuable information.
[0028] Therefore, the present application provides an object evaluation method, which can process and analyze the object attribute information of the object to be evaluated, deeply explore the potential value behind the data, discover the associations and rules between the data, and provide more abundant and accurate information support for talent evaluation, which not only improves the depth and breadth of the evaluation, but also provides strong data support for organizational decision-making; in this way, the present application can comprehensively evaluate the candidate's skill level, comprehensive quality, development potential and other aspects, overcome the limitations of the single dimension of the traditional evaluation method, and improve the comprehensiveness and accuracy of the evaluation, which not only reduces the burden of manual review, but also improves the efficiency and accuracy of the evaluation, making the evaluation results more objective and fair. In addition, the present application can present complex evaluation results to users in an intuitive and easy-to-understand evaluation result report, which not only facilitates users to understand and use the evaluation results, but also improves users' satisfaction with the use.
[0029] Various non-limiting implementations of the present application are described in detail below in conjunction with the accompanying drawings.
[0030] See also Figure 1 , shows an object evaluation method in an embodiment of the present application. In this embodiment, the method may include the following steps:
[0031] S101: Obtain object attribute information of an object to be evaluated.
[0032] The object to be evaluated can be understood as an object that needs to be evaluated, for example, it can be a user in a talent pool. Among them, the object attribute information includes the attribute information of the object to be evaluated under several first dimensions; in one implementation, the object to be evaluated is the user to be evaluated, and the attribute information under the several first dimensions includes at least one of the following: attribute information under the user portrait dimension of the user to be evaluated (such as name, gender, age, education), attribute information under the work experience dimension (such as work experience information), attribute information under the work results dimension (such as papers, journal publications, awards), attribute information under the professional skills dimension (such as professional skills certificates, professional technical certifications, educational background, project experience), and attribute information under the user awareness dimension (such as security awareness).
[0033] In one implementation, the method of obtaining the object attribute information of the object to be evaluated may include the following steps:
[0034] First, obtain the object evaluation test content and / or object introduction information of the object to be evaluated. Among them, the object evaluation test content and / or the object introduction information include the test content and test response information corresponding to each of the several first dimensions, or introduction text information. It should be noted that the several first dimensions may include at least one of the following: user portrait dimension, work experience dimension, work results dimension, professional skills dimension, and user awareness dimension of the user to be evaluated. The test content and test response information can be questionnaires and questionnaire responses, test papers and test results of test papers, operating environment tests, and scores of operating tests. The introduction text information can be a resume or questionnaire text. For example, Figure 2 As shown, the object attribute information of the object to be evaluated can be collected through the data collection module, for example, through the candidate database, resume upload interface, and questionnaire survey data.
[0035] Utilize the object evaluation test content and / or object introduction information of the object to be evaluated to extract the attribute information of the object to be evaluated under the first dimensions. As an example, after obtaining the object evaluation test content and / or object introduction information of the object to be evaluated, a text processing algorithm or a neural network model can be used to extract the attribute information of the object to be evaluated under the first dimensions. Then, the attribute information of the object to be evaluated under the first dimensions can be used as the object attribute information of the object to be evaluated.
[0036] S102: Extracting features from the object attribute information to obtain some key feature information of the object to be evaluated.
[0037] The key feature information of the object to be evaluated includes the features of the object to be evaluated in a first dimension. In one implementation, the features in the first dimension include at least one of the following: feature information in the user portrait dimension of the user to be evaluated, feature information in the work experience dimension, feature information in the work results dimension, feature information in the professional skills dimension, and feature information in the user awareness dimension.
[0038] Specifically, the object attribute information of the object to be evaluated can be semantically understood first, and the features of the object to be evaluated under several first dimensions can be extracted. As an example, the object attribute information of the object to be evaluated is preprocessed to obtain the preprocessed object attribute information; wherein the preprocessing operation includes at least one of the following: data cleaning, data denoising, and data unification operation. It should be noted that the data unification operation can be understood as a unified standard that can be implemented. For example, the gender may be filled in by the user as [male, male], and the standard can be unified into [male]. Next, the preprocessed object attribute information is semantically understood, and the features of the object to be evaluated under several first dimensions can be extracted. As an example, the semantic understanding ability of the large model can be used to input the preprocessed object attribute information into the trained large model, so that the large model can extract the text class of the preprocessed object attribute information to obtain the features of the object to be evaluated under several first dimensions. Then, the features of the object to be evaluated under several first dimensions can be used as several key feature information of the object to be evaluated.
[0039] S103: Determine evaluation results under a plurality of second dimensions according to a plurality of key feature information of the object to be evaluated.
[0040] In this embodiment, after obtaining several key feature information of the object to be evaluated, the evaluation results under several second dimensions can be determined according to the several key feature information of the object to be evaluated. The several second dimensions may include at least one of the following: skill level, comprehensive quality, development potential, professional and technical ability, safety awareness ability, skill knowledge reserve ability, skill knowledge practice ability. As an example, several key feature information of the object to be evaluated can be input into the evaluation model so that the evaluation model determines the evaluation results under several second dimensions according to the several key feature information of the object to be evaluated. It should be noted that the evaluation model can be an evaluation model constructed based on deep learning, natural language processing and other technologies. In one implementation, the evaluation result under each second dimension can be a score under the second dimension.
[0041] S104: Generate an evaluation result report of the object to be evaluated according to the evaluation results under the plurality of second dimensions.
[0042] After obtaining the evaluation results under several second dimensions, a comprehensive evaluation result (e.g., a comprehensive score) of the object to be evaluated can be generated based on the evaluation results under several second dimensions. And, based on the evaluation results under several second dimensions and the comprehensive evaluation results of the object to be evaluated, the stronger and weaker aspects of the object to be evaluated, as well as the dimensions to be supplemented and improved (e.g., what kind of knowledge needs to be supplemented, what kind of skills need to be mastered) are analyzed, and the overall evaluation of the user can be achieved by providing the user's capability map. Then, the evaluation results under several second dimensions, the comprehensive evaluation results, and the analysis results (i.e., the stronger and weaker aspects of the object to be evaluated, the dimensions to be supplemented and improved, and the improvement suggestions) can be used as the evaluation result report of the object to be evaluated.
[0043] It should be noted that the evaluation result report of the object to be evaluated can also be displayed. Specifically, the evaluation result report can be presented to the user in the form of a report, and a visual analysis tool can be provided to help the user better understand the evaluation results. Figure 2 As shown, the evaluation result report can be presented in a visual manner such as charts and dashboards.
[0044] In another implementation of this embodiment, the method may further include:
[0045] Obtaining a target evaluation scenario corresponding to the object to be evaluated;
[0046] Accordingly, the inputting of the key feature information of the object to be evaluated into the evaluation model so that the evaluation model determines the evaluation results under the second dimensions according to the key feature information of the object to be evaluated, including:
[0047] The key feature information of the object to be evaluated and the target evaluation scenario are input into the evaluation model, so that the evaluation model uses the key feature information of the object to be evaluated and the model parameters and model structure corresponding to the target evaluation scenario to determine the evaluation results under the second dimensions.
[0048] In other words, this application can be dynamically adjusted, that is, the model parameters and model structure of the evaluation model can be adjusted according to the key feature information of the object to be evaluated and the target evaluation scenario, so as to dynamically adjust the model parameters and model structure of the evaluation model to meet the evaluation needs in different scenarios. For example, if some companies attach importance to knowledge reserves, they can set the percentage of knowledge reserve scores to achieve the proportion of knowledge in the overall talent evaluation score.
[0049] It can be seen from the above technical scheme that the present application provides an object evaluation method, which can obtain object attribute information of an object to be evaluated; wherein, the object attribute information includes attribute information of the object to be evaluated under several first dimensions. Then, feature extraction can be performed on the object attribute information to obtain several key feature information of the object to be evaluated; the key feature information of the object to be evaluated includes the features of the object to be evaluated under a first dimension. Then, based on the several key feature information of the object to be evaluated, the evaluation results under several second dimensions can be determined. Finally, based on the evaluation results under the several second dimensions, an evaluation result report of the object to be evaluated can be generated. It can be seen that this application can process and analyze the object attribute information of the object to be evaluated, deeply explore the potential value behind the data, discover the relationship and rules between the data, and provide more abundant and accurate information support for talent evaluation, which not only improves the depth and breadth of the evaluation, but also provides strong data support for organizational decision-making; in this way, this application can comprehensively evaluate the candidate's skill level, comprehensive quality, development potential and other aspects, overcome the limitations of the single dimension of traditional evaluation methods, and improve the comprehensiveness and accuracy of the evaluation, which not only reduces the burden of manual review, but also improves the efficiency and accuracy of the evaluation, making the evaluation results more objective and fair. In addition, this application can present complex evaluation results to users in an intuitive and easy-to-understand evaluation result report, which not only facilitates users to understand and use the evaluation results, but also improves user satisfaction.
[0050] It can be understood that the present application has the following advantages:
[0051] 1. Multi-dimensional evaluation: By building a comprehensive evaluation model, a comprehensive evaluation of candidates in multiple dimensions can be achieved.
[0052] 2. Deep learning technology: Use deep learning algorithms to automatically learn complex patterns in data and improve the accuracy and reliability of evaluation.
[0053] 3. Dynamic adjustment mechanism: Dynamically adjust the evaluation model based on user feedback and actual application effects to ensure that the model always adapts to evaluation needs.
[0054] 4. Big data processing technology: Use advanced data processing and analysis technology to deeply explore the potential value behind the data.
[0055] 5. Comprehensiveness: By constructing a comprehensive, multi-dimensional talent evaluation model, it is possible to comprehensively evaluate multiple aspects of candidates, such as their skill level, overall quality, and development potential. This overcomes the limitations of the single dimension of traditional evaluation methods and improves the comprehensiveness and accuracy of the evaluation.
[0056] 6. Intelligence: Based on advanced artificial intelligence technologies such as deep learning, it can automatically learn complex patterns in data and achieve intelligent evaluation. This not only reduces the burden of manual review, but also improves the efficiency and accuracy of evaluation, making the evaluation results more objective and fair.
[0057] 7. Dynamic adaptability: By introducing a dynamic adjustment mechanism, the parameters and structure of the evaluation model can be flexibly adjusted according to user feedback and actual application effects to meet the evaluation needs of different industries and positions. This dynamic adaptability gives the present invention a wider application prospect and stronger market competitiveness.
[0058] 8. Deep data mining: Using advanced data processing and analysis technologies, we can deeply explore the potential value behind the data, discover the connections and patterns between the data, and provide more abundant and accurate information support for talent evaluation. This not only improves the depth and breadth of the evaluation, but also provides strong data support for organizational decision-making.
[0059] 9. User experience optimization: Focus on user experience, and present complex evaluation results to users in an intuitive and easy-to-understand way through result output and visualization modules. This not only facilitates users to understand and use evaluation results, but also improves user satisfaction and loyalty.
[0060] like Figure 3 As shown, it is a specific embodiment of an object evaluation device described in the present application. The device described in this embodiment is a physical device for executing the method described in the above embodiment. Its technical solution is essentially consistent with the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. The device described in this embodiment includes:
[0061] The first unit 301 is used to obtain object attribute information of the object to be evaluated; wherein the object attribute information includes attribute information of the object to be evaluated under several first dimensions;
[0062] The second unit 302 is used to extract features from the object attribute information to obtain some key feature information of the object to be evaluated; the key feature information of the object to be evaluated includes features of the object to be evaluated in a first dimension;
[0063] The third unit 303 is used to determine the evaluation results under the second dimension according to the key feature information of the object to be evaluated;
[0064] The fourth unit 304 is used to generate an evaluation result report of the object to be evaluated according to the evaluation results under the plurality of second dimensions.
[0065] Optionally, obtaining object attribute information of the object to be evaluated includes:
[0066] Obtaining object evaluation test content and / or object introduction information of the object to be evaluated; the object evaluation test content and / or the object introduction information include test content and test answer information corresponding to each of the plurality of first dimensions, or introduction text information;
[0067] Extracting attribute information of the object to be evaluated under the first dimensions by using the object evaluation test content and / or object introduction information of the object to be evaluated;
[0068] The attribute information of the object to be evaluated in the first dimensions is used as the object attribute information of the object to be evaluated.
[0069] Optionally, the feature extraction of the object attribute information to obtain several key feature information of the object to be evaluated includes:
[0070] Performing semantic understanding on the object attribute information of the object to be evaluated, and extracting features of the object to be evaluated under a plurality of first dimensions;
[0071] The characteristics of the object to be evaluated in the first dimensions are used as the key characteristic information of the object to be evaluated.
[0072] Optionally, the performing semantic understanding on the object attribute information of the object to be evaluated and extracting features of the object to be evaluated in a plurality of first dimensions includes:
[0073] Performing a preprocessing operation on the object attribute information of the object to be evaluated to obtain preprocessed object attribute information; wherein the preprocessing operation includes at least one of the following: data cleaning, data denoising, and data unification operations;
[0074] The preprocessed object attribute information is semantically understood to extract features of the object to be evaluated in a plurality of first dimensions.
[0075] Optionally, determining evaluation results under several second dimensions according to several key feature information of the object to be evaluated includes:
[0076] The key feature information of the object to be evaluated is input into the evaluation model, so that the evaluation model determines the evaluation results under the second dimensions according to the key feature information of the object to be evaluated.
[0077] Optionally, the several second dimensions include at least one of the following: skill level, comprehensive quality, development potential, professional and technical ability, safety awareness ability, skill knowledge reserve ability, and skill knowledge practice ability.
[0078] Optionally, the device further includes a fifth unit, configured to:
[0079] Obtaining a target evaluation scenario corresponding to the object to be evaluated;
[0080] Accordingly, the inputting of the key feature information of the object to be evaluated into the evaluation model so that the evaluation model determines the evaluation results under the second dimensions according to the key feature information of the object to be evaluated, including:
[0081] The key feature information of the object to be evaluated and the target evaluation scenario are input into the evaluation model, so that the evaluation model uses the key feature information of the object to be evaluated and the model parameters and model structure corresponding to the target evaluation scenario to determine the evaluation results under the second dimensions.
[0082] Optionally, the object to be evaluated is a user to be evaluated, and the attribute information under the several first dimensions includes at least one of the following: attribute information under the user portrait dimension of the user to be evaluated, attribute information under the work experience dimension, attribute information under the work results dimension, attribute information under the professional skills dimension, and attribute information under the user awareness dimension.
[0083] Optionally, the features under the first dimension include at least one of the following: feature information under the user portrait dimension of the user to be evaluated, feature information under the work experience dimension, feature information under the work results dimension, feature information under the professional skills dimension, and feature information under the user awareness dimension.
[0084] Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.
[0085] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.
[0086] The memory is used to store execution instructions. Specifically, the execution instructions are computer programs that can be executed. The memory may include internal memory and non-volatile memory, and provides execution instructions and data to the processor.
[0087] In one possible implementation, the processor reads the corresponding execution instructions from the non-volatile memory into the memory and then runs them, and can also obtain the corresponding execution instructions from other devices to form an object evaluation device at the logical level. The processor executes the execution instructions stored in the memory to implement the object evaluation method provided in any embodiment of the present application through the executed execution instructions.
[0088] The above application Figure 1 The method performed by the object evaluation device provided in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.
[0089] The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.
[0090] An embodiment of the present application also proposes a readable storage medium, which stores execution instructions. When the stored execution instructions are executed by a processor of an electronic device, the electronic device can execute the object evaluation method provided in any embodiment of the present application, and is specifically used to execute the above-mentioned object evaluation method.
[0091] The electronic device described in the above embodiments may be a computer.
[0092] Those skilled in the art should understand that the embodiments of the present application can be provided as methods or computer program products. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware.
[0093] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0094] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0095] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims
1. An object evaluation method, characterized in that: The method comprises: Obtaining object attribute information of the object to be evaluated; wherein the object attribute information includes attribute information of the object to be evaluated under a plurality of first dimensions; Extracting features from the object attribute information to obtain a number of key feature information of the object to be evaluated; the key feature information of the object to be evaluated includes features of the object to be evaluated in a first dimension; Determine evaluation results under several second dimensions according to several key feature information of the object to be evaluated; An evaluation result report of the object to be evaluated is generated according to the evaluation results under the plurality of second dimensions.
2. The method according to claim 1, characterized in that: The obtaining of object attribute information of the object to be evaluated includes: Obtaining object evaluation test content and / or object introduction information of the object to be evaluated; the object evaluation test content and / or the object introduction information include test content and test answer information corresponding to each of the plurality of first dimensions, or introduction text information; Extracting attribute information of the object to be evaluated under the first dimensions by using the object evaluation test content and / or object introduction information of the object to be evaluated; The attribute information of the object to be evaluated in the first dimensions is used as the object attribute information of the object to be evaluated.
3. The method according to claim 1, characterized in that The feature extraction of the object attribute information to obtain several key feature information of the object to be evaluated includes: Performing semantic understanding on the object attribute information of the object to be evaluated, and extracting features of the object to be evaluated under a plurality of first dimensions; The characteristics of the object to be evaluated in the first dimensions are used as the key characteristic information of the object to be evaluated.
4. The method according to claim 3, characterized in that The semantic understanding of the object attribute information of the object to be evaluated and the extraction of features of the object to be evaluated under a plurality of first dimensions include: Performing a preprocessing operation on the object attribute information of the object to be evaluated to obtain preprocessed object attribute information; wherein the preprocessing operation includes at least one of the following: data cleaning, data denoising, and data unification operations; The preprocessed object attribute information is semantically understood to extract features of the object to be evaluated in a plurality of first dimensions.
5. The method according to claim 1, characterized in that Determining the evaluation results under the second dimension according to the key feature information of the object to be evaluated includes: The key feature information of the object to be evaluated is input into the evaluation model, so that the evaluation model determines the evaluation results under the second dimensions according to the key feature information of the object to be evaluated.
6. The method according to claim 5, characterized in that The several second dimensions include at least one of the following: skill level, comprehensive quality, development potential, professional and technical ability, safety awareness ability, skill knowledge reserve ability, and skill knowledge practice ability.
7. The method according to claim 5, characterized in that The method further comprises: Obtaining a target evaluation scenario corresponding to the object to be evaluated; Accordingly, the inputting of the key feature information of the object to be evaluated into the evaluation model so that the evaluation model determines the evaluation results under the second dimensions according to the key feature information of the object to be evaluated, including: The key feature information of the object to be evaluated and the target evaluation scenario are input into the evaluation model, so that the evaluation model uses the key feature information of the object to be evaluated and the model parameters and model structure corresponding to the target evaluation scenario to determine the evaluation results under the second dimensions.
8. The method according to claim 1, characterized in that The object to be evaluated is a user to be evaluated, and the attribute information under the plurality of first dimensions includes at least one of the following: attribute information under a user portrait dimension of the user to be evaluated, attribute information under a work experience dimension, attribute information under a work result dimension, attribute information under a professional skill dimension, and attribute information under a user awareness dimension.
9. The method according to claim 1 or 2, characterized in that: The features under the first dimension include at least one of the following: feature information under the user portrait dimension of the user to be evaluated, feature information under the work experience dimension, feature information under the work results dimension, feature information under the professional skills dimension, and feature information under the user awareness dimension.
10. An object evaluation device, characterized in that: The device comprises: A first unit is used to obtain object attribute information of an object to be evaluated; wherein the object attribute information includes attribute information of the object to be evaluated under a plurality of first dimensions; The second unit is used to extract features from the object attribute information to obtain some key feature information of the object to be evaluated; the key feature information of the object to be evaluated includes features of the object to be evaluated in a first dimension; The third unit is used to determine the evaluation results under the second dimension according to the key feature information of the object to be evaluated; The fourth unit is used to generate an evaluation result report of the object to be evaluated according to the evaluation results under the plurality of second dimensions.