Expert talent assessment material data processing method and related device

Abstract

The invention discloses an examination material data processing method, an examination material data processing device and examination material data processing equipment for expert talents, and a computer readable storage medium. The method comprises the following steps: acquiring data through a data input interface; reasoning the data based on a scoring model to obtain scoring data; wherein the scoring model is a model obtained by training the scored data; sending the scoring data to a scoring terminal, so that the scoring terminal performs scoring confirmation processing on the scoring data; and when the scoring confirmation processing is completed, sending the scoring data according to a preset path. The objective of processing material data of expert talents is improved, the resource utilization rate is increased, and the problem of poor accuracy during data processing is avoided.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and more specifically, relates to a method for processing expert talent assessment material data, an assessment material data processing device, an assessment material data processing equipment, and a computer-readable storage medium. Background Art

[0002] In the operation and management of companies today, the evaluation and assessment of expert talents is a vital task, and its results are directly related to the development of expert talents and the company's overall talent strategy layout.

[0003] In the related technologies, companies generally use paper-based assessment to manually process the assessment data of experts and talents. Specifically, at the end of the year, the assessed experts need to print out the relevant materials within the specified time, and then sign at all levels in turn, and then submit them to the assessment department. The assessment department then organizes experts to centrally score the materials of the assessed personnel, and finally obtain the annual assessment results. However, there are many objective shortcomings. First, the assessment results are easily affected by the personal consciousness and emotional factors of the experts in the assessment group, and it is difficult to ensure complete objectivity and fairness. Second, the use of a large amount of paper supporting materials not only consumes resources such as paper, but also brings many inconveniences in the storage, sorting and review of materials, resulting in a waste of resources. Third, due to the need to prepare materials in a short period of time, the assessed experts may miss materials due to time constraints, which in turn affects the accuracy and completeness of the assessment. Fourth, due to the limited assessment time, the review and judgment of the materials by the experts within the limited time may not be comprehensive and in-depth, resulting in the assessment results not being able to fully and truly reflect the actual level of the experts. Fifth, in today's highly developed information and networking era, paper-based assessment methods are difficult to adapt to the requirements of the new era for testing and management, and are not conducive to the efficient processing, analysis and sharing of data. Sixth, the submission and review process of paper materials is relatively closed, lacking an effective supervision mechanism, and some materials may contain cheating or falsification, which undermines the fairness and seriousness of the assessment.

[0004] Therefore, how to improve the objectivity of material data processing by experts and talents, improve resource utilization, and avoid the problem of poor accuracy in data processing are the key issues that technical personnel in this field are concerned about. Summary of the invention

[0005] The purpose of this application is to provide a method for processing expert talent's assessment material data, an assessment material data processing device, an assessment material data processing equipment, and a computer-readable storage medium, so as to improve the objectivity of expert talent's material data processing, improve resource utilization, and avoid the problem of poor accuracy during data processing.

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a method for processing expert talent assessment material data, comprising:

[0007] Obtain information data through the data input interface;

[0008] Inferring the data based on the scoring model to obtain scoring data; wherein the scoring model is a model obtained by training the scored data;

[0009] Sending the scoring data to a scoring terminal so that the scoring terminal performs scoring confirmation processing on the scoring data;

[0010] When the scoring confirmation process is completed, the scoring data is sent according to a preset path.

[0011] Optionally, the training process of the scoring model includes:

[0012] Obtaining raw data;

[0013] Sending the original material data to the scoring terminal so that the scoring terminal obtains original scoring data based on the original material data and returns the original scoring data;

[0014] The initial model is trained based on the original material data and the original scoring data to obtain the scoring model.

[0015] Optionally, also include:

[0016] Acquiring manual scoring data from the scoring terminal;

[0017] Calculate the average of the scoring data and the manual scoring data to obtain target scoring data;

[0018] The target scoring data is sent according to a preset path.

[0019] Optionally, obtain data through the data input interface, including:

[0020] Obtain initial information data through the data input interface;

[0021] The initial data are classified and sorted to obtain the data.

[0022] The present application also provides a data processing device for expert talent assessment materials, comprising:

[0023] A data acquisition module is used to acquire data through a data input interface;

[0024] A scoring inference module, used to infer the data based on the scoring model to obtain scoring data; wherein the scoring model is a model obtained by training the scored data;

[0025] A scoring confirmation module, used for sending the scoring data to the scoring terminal so that the scoring terminal performs scoring confirmation processing on the scoring data;

[0026] The scoring data sending module is used to send the scoring data according to a preset path when the scoring confirmation process is completed.

[0027] Optionally, also include:

[0028] The model training module is used to obtain original material data; send the original material data to the scoring terminal so that the scoring terminal obtains original scoring data based on the original material data and returns the original scoring data; train the initial model based on the original material data and the original scoring data to obtain the scoring model.

[0029] Optionally, also include:

[0030] The scoring calculation module is used to obtain manual scoring data from the scoring terminal; average the scoring data and the manual scoring data to obtain target scoring data; and send the target scoring data according to a preset path.

[0031] Optionally, the data acquisition module is specifically used to acquire initial data through a data input interface; classify and organize the initial data to obtain the data.

[0032] The present application also provides an assessment material data processing device, comprising:

[0033] Memory for storing computer programs;

[0034] A processor is used to implement the steps of the assessment material data processing method as described above when executing the computer program.

[0035] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the assessment material data processing method as described above are implemented.

[0036] The present application provides a method for processing data of expert talent assessment materials, comprising: obtaining material data through a data input interface; inferring the material data based on a scoring model to obtain scoring data; wherein the scoring model is a model obtained by training the scored material data; sending the scoring data to a scoring terminal so that the scoring terminal performs scoring confirmation processing on the scoring data; when the scoring confirmation processing is completed, sending the scoring data along a preset path.

[0037] It has the following beneficial effects:

[0038] Collect data extensively through the data input interface to ensure comprehensive evaluation. Reason the score based on the scoring model to improve efficiency and enhance objectivity. Combine manual confirmation of the scoring terminal to improve accuracy and fairness. Send the scoring data according to the preset path to ensure the effective use of the results, which will help the company make scientific talent decisions, promote the development of expert talents, optimize the overall talent management system, and meet the efficient and precise management needs of modern enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, 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 embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0040] Figure 1 A flowchart of a method for processing expert talent assessment material data provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure of a data processing device for expert talent assessment materials provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the structure of the assessment material data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The purpose of this application is to provide a method for processing expert talent's assessment material data, an assessment material data processing device, an assessment material data processing equipment, and a computer-readable storage medium, so as to improve the objectivity of expert talent's material data processing, improve resource utilization, and avoid the problem of poor accuracy during data processing.

[0044] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0045] The following is an example of an embodiment to illustrate a method for processing expert talent assessment material data provided by the present application.

[0046] Please refer to Figure 1 , Figure 1 A flowchart of a method for processing expert talent assessment material data provided in an embodiment of the present application.

[0047] In this embodiment, the method may include:

[0048] S101, obtaining data through a data input interface;

[0049] This step aims to provide basic data for the entire evaluation process. These data will serve as the basis for subsequent scoring. Various types of information related to expert talents will be collected through specific data input interfaces to ensure the diversity and comprehensiveness of data sources in order to fully reflect the capabilities, performance and other aspects of expert talents.

[0050] The system is equipped with a special data input interface, which can be connected to a variety of data sources, such as expert talent personal information systems, project management systems, achievement databases, etc., so as to obtain initial data such as expert work results, project experience, academic papers, awards, professional skills certificates, etc. Then the initial data obtained is classified and sorted, for example, according to the dimensions of achievement type, time sequence, professional field, etc., and the messy raw data is converted into structured data that is easy to process later, and the data that can be used for scoring model processing is obtained.

[0051] It can be seen that through extensive data collection and collation, the actual situation of experts and talents can be more comprehensively and accurately reflected, avoiding inaccurate evaluation problems caused by missing or confused data. At the same time, it provides a rich and orderly data basis for subsequent scoring reasoning, improving the reliability and effectiveness of the entire evaluation process.

[0052] S102, inferring the data based on the scoring model to obtain scoring data; wherein the scoring model is a model obtained by training the scored data;

[0053] Based on S101, this step aims to utilize a pre-trained scoring model, which is trained based on scored data and has the ability to extract and analyze features of input data, thereby inferring corresponding scoring data based on the algorithms and logic within the model to achieve a preliminary quantitative assessment of expert talent.

[0054] In this step, the classified and organized data can be input into the scoring model. The model will identify and analyze the various features in the data according to the patterns and rules learned during its training. For example, the quality of the work results, the difficulty and influence of the project, the level of the academic paper and other features are comprehensively considered, and a preliminary scoring data is calculated according to the preset algorithm. The training process of the scoring model is to obtain a large amount of original data, send these original data to the scoring terminal, and the experts at the scoring terminal will give the original scoring data based on the original data and return it. Then, the original data and the corresponding original scoring data are used to train the initial model. By continuously adjusting the model parameters, the model's prediction results are as close as possible to the original scores given by the experts, and finally a scoring model that can accurately evaluate expert talents is obtained.

[0055] It can be seen that compared with direct manual evaluation, reasoning based on the scoring model can give a preliminary score more objectively and quickly, reduce the subjectivity and time cost of manual evaluation, and improve evaluation efficiency. At the same time, the model is trained based on a large amount of historical data and can comprehensively evaluate multiple factors, making the scoring results more scientific and reasonable.

[0056] S103, sending the scoring data to the scoring terminal so that the scoring terminal can perform scoring confirmation processing on the scoring data;

[0057] Based on S102, although the scoring model can give a preliminary score, it still needs manual review and confirmation to ensure the accuracy and fairness of the score. The scoring data is sent to the scoring terminal, where professional experts or evaluators further review and confirm the score given by the model, giving full play to the advantages of manual evaluation and correcting possible deviations or omissions in the model.

[0058] The system can transmit the scoring data to the designated scoring terminal device through network communication and other means. The scoring terminal can be a special evaluation software interface or device. Experts or evaluators can intuitively see the scoring data given by the model and the corresponding information data on the terminal for detailed review. They can analyze the scoring data based on their professional knowledge, experience and understanding of expert talents, judge its rationality, and make adjustments or supplement information if problems are found.

[0059] It can be seen that by combining the advantages of manual evaluation and machine evaluation, manual confirmation of machine scores can not only utilize the efficiency and objectivity of machine evaluation, but also make up for the possible shortcomings of the machine through manual professional judgment, thereby improving the accuracy and credibility of the evaluation results and ensuring that the evaluation results are more in line with the actual level of expert talents.

[0060] S104: When the scoring confirmation process is completed, the scoring data is sent according to a preset path.

[0061] Based on S103, this step obtains the final scoring data after the scoring confirmation processing by the scoring terminal. The data is sent along the preset path in order to effectively transmit and utilize the evaluation results, such as for subsequent processes such as performance appraisal of expert talents, promotion decisions, and talent planning, to ensure that the evaluation data can be correctly applied within an appropriate range.

[0062] The sending path of the scoring data is pre-set in the system, and the path can be set according to the purpose and needs of the evaluation data, such as sending it to the company's human resources management system for performance evaluation, or sending it to the expert talent pool for talent classification management, etc. When the scoring terminal completes the scoring confirmation process, the system automatically starts the data sending program and accurately transmits the final scoring data to the corresponding target system or platform according to the preset path.

[0063] It can be seen that ensuring that the evaluation results can reach the place where the data is needed in a timely and accurate manner, achieving the effective circulation of the evaluation data and maximizing its value, will help make the talent management decisions within the company more scientific and reasonable, and will also facilitate the subsequent tracking and management of expert talents.

[0064] In summary, this embodiment collects data extensively through the data input interface to ensure the comprehensiveness of the evaluation. Scoring is based on the scoring model reasoning to improve efficiency and enhance objectivity. Combined with manual confirmation of the scoring terminal, accuracy and fairness are improved. The scoring data is sent according to the preset path to ensure the effective use of the results, which helps the company make scientific talent decisions, promote the development of expert talents, optimize the overall talent management system, and adapt to the efficient and precise management needs of modern enterprises.

[0065] Optional, the scoring model training process includes:

[0066] Step 1, obtaining original data;

[0067] Step 2, sending the original material data to the scoring terminal, so that the scoring terminal obtains original scoring data based on the original material data and returns the original scoring data;

[0068] Step 3: Train the initial model based on the original material data and the original scoring data to obtain a scoring model.

[0069] In this optional solution, a large amount of original data is collected and the corresponding expert manual scores (original score data) are obtained. These data are used to train the initial model so that the model can learn the internal relationship and rules between the data and the scores, so that it can automatically predict reasonable scores based on the new data. This is based on the principle of supervised learning in machine learning, and the parameters of the model are adjusted through known input (original data) and output (original score data) to achieve accurate prediction of unknown data.

[0070] Among them, first of all, the original data are widely collected from multiple data sources (such as the company's internal database, experts' past project records, etc.), which cover a variety of information that can reflect the ability and performance of experts. Then these original data are sent to the scoring terminal through network transmission or data sharing. The experts at the scoring terminal analyze and evaluate each original data in detail based on their professional knowledge and experience, give the corresponding original scoring data, and return these data to the training system. After receiving the original data and original scoring data, the system uses a suitable machine learning algorithm (such as neural network, decision tree, etc.), with the original data as input and the original scoring data as the expected output, to perform multiple iterations of training on the initial model, and continuously adjust the parameters within the model until the prediction ability of the model reaches a certain accuracy and stability, and finally obtains a trained scoring model.

[0071] It can be seen that the trained scoring model has high accuracy and reliability because it is learned based on a large amount of actual data and the professional judgment of experts. The model can automatically process new data and give scores, which greatly improves the evaluation efficiency and reduces the workload and subjectivity of manual scoring. At the same time, it can also ensure that the evaluation results meet the professional judgment standards of experts to a certain extent, providing a scientific and objective tool for subsequent expert talent evaluation.

[0072] Optionally, also include:

[0073] Step 1, obtaining manual scoring data from the scoring terminal;

[0074] Step 2: average the scoring data and the manual scoring data to obtain the target scoring data;

[0075] Step 3: Send the target score data according to the preset path.

[0076] In this optional solution, manual scoring data is introduced on the basis of machine scoring, and the final target scoring data is obtained through average calculation in order to further improve the accuracy and fairness of the scoring. Machine scoring may have certain limitations, while manual scoring can fully take into account some special circumstances and details that are difficult for machines to identify. Combining the two and taking the average value can combine the advantages of both and reduce the error of a single scoring method.

[0077] Among them, in the scoring terminal, in addition to the expert scoring confirmation processing of the scoring data, the expert can also be allowed to give independent manual scoring data according to the actual situation. After the system obtains these manual scoring data from the scoring terminal, it matches them with the scoring data previously obtained through the scoring model, and then performs a simple arithmetic average calculation on each set of matching scoring data and manual scoring data, that is, (scoring data + manual scoring data) ÷ 2, to obtain the target scoring data. Finally, the target scoring data is sent out according to the preset path (such as sending it to the human resources management system for performance evaluation, etc.) for subsequent use.

[0078] It can be seen that this option combines the efficiency of machine evaluation with the flexibility and accuracy of manual evaluation, making the final scoring result more objective and comprehensive, and more truly reflecting the actual level of experts and talents. At the same time, the two scoring data are combined by averaging, which is simple to operate and easy to understand. On the premise of ensuring the accuracy of the evaluation, it improves the practicality and operability of the entire evaluation process.

[0079] Optionally, obtain data through the data input interface, including:

[0080] Step 1, obtaining initial data through the data input interface;

[0081] Step 2: classify and organize the initial data to obtain data.

[0082] In this optional solution, the data input interface is the entry point for obtaining data, but the data initially obtained may be disorganized, in inconsistent formats, and contain various redundant information. By classifying and organizing, these data can be converted into an organized format that is convenient for subsequent processing and analysis, thereby improving the quality and availability of the data and providing a solid data foundation for accurate evaluation.

[0083] Among them, the data input interface uses various data collection technologies (such as file upload, database query, interface call, etc.) to obtain initial data from different data sources. These data sources can include information submitted by experts, data related to experts in various business systems within the company, etc. After obtaining the initial data, data processing algorithms and rules are used to classify and organize it. For example, data types (such as work results data, education background data, training experience data, etc.), time sequence, professional fields and other dimensions are classified, duplicate and invalid data are removed, and related data are associated and integrated. Finally, the initial data is converted into data with clear structure and logical coherence, so that the subsequent scoring model can effectively process these data.

[0084] This optional solution improves the quality and processability of data, reduces the interference of invalid data on the evaluation results, and makes the subsequent scoring reasoning based on these data more accurate and reliable. At the same time, the classified data is easier to understand and analyze, which provides great convenience for both machine models and manual review, and helps to improve the efficiency and quality of the entire evaluation process.

[0085] The following is an introduction to an expert talent assessment material data processing device provided in an embodiment of the present application. The expert talent assessment material data processing device and the expert talent assessment material data processing method described below can correspond to each other.

[0086] Please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a data processing device for expert talent assessment materials provided in an embodiment of the present application.

[0087] In this embodiment, the device may include:

[0088] The data acquisition module 100 is used to acquire data through a data input interface;

[0089] The scoring inference module 200 is used to infer the data based on the scoring model to obtain the scoring data; wherein the scoring model is a model obtained by training the scored data;

[0090] The scoring confirmation module 300 is used to send the scoring data to the scoring terminal so that the scoring terminal can perform scoring confirmation processing on the scoring data;

[0091] The scoring data sending module 400 is used to send the scoring data according to a preset path when the scoring confirmation process is completed.

[0092] Optionally, also include:

[0093] The model training module is used to obtain original material data; send the original material data to the scoring terminal so that the scoring terminal obtains original scoring data based on the original material data and returns the original scoring data; train the initial model based on the original material data and the original scoring data to obtain a scoring model.

[0094] Optionally, also include:

[0095] The scoring calculation module is used to obtain manual scoring data from the scoring terminal; average the scoring data and the manual scoring data to obtain target scoring data; and send the target scoring data according to a preset path.

[0096] Optionally, the data acquisition module is specifically used to acquire initial data through the data input interface; classify and organize the initial data to obtain data.

[0097] This application also provides assessment material data processing equipment, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of the assessment material data processing device provided in an embodiment of the present application. The assessment material data processing device may include:

[0098] Memory for storing computer programs;

[0099] The processor, when used to execute a computer program, can implement the steps of any of the above-mentioned methods for processing data of expert talent assessment materials.

[0100] like Figure 3 As shown, it is a schematic diagram of the composition structure of the assessment material data processing device, which may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all communicate with each other through the communication bus 13.

[0101] In the embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0102] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the abnormal IP identification method.

[0103] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for implementing the following functions:

[0104] Obtain information data through the data input interface;

[0105] Reasoning the data based on the scoring model to obtain scoring data; wherein the scoring model is a model obtained by training the scored data;

[0106] Send the scoring data to the scoring terminal so that the scoring terminal can perform scoring confirmation processing on the scoring data;

[0107] When the scoring confirmation process is completed, the scoring data is sent according to the preset path.

[0108] In a possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0109] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0110] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0111] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the material data processing device in the embodiment of the present application. In actual applications, the material data processing device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0112] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned expert talent assessment material data processing methods can be implemented.

[0113] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0114] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0115] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0116] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0117] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0118] The above is a detailed introduction to the expert talent assessment material data processing method, assessment material data processing device, assessment material data processing equipment, and computer-readable storage medium provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for processing expert talent assessment material data, characterized in that: include: Obtain information data through the data input interface; Inferring the data based on the scoring model to obtain scoring data; wherein the scoring model is a model obtained by training the scored data; Sending the scoring data to a scoring terminal so that the scoring terminal performs scoring confirmation processing on the scoring data; When the scoring confirmation process is completed, the scoring data is sent according to a preset path.

2. The method for processing assessment material data according to claim 1, characterized in that: The training process of the scoring model includes: Obtaining raw data; Sending the original material data to the scoring terminal so that the scoring terminal obtains original scoring data based on the original material data and returns the original scoring data; The initial model is trained based on the original material data and the original scoring data to obtain the scoring model.

3. The method for processing assessment material data according to claim 2, characterized in that: Also includes: Acquiring manual scoring data from the scoring terminal; Calculate the average of the scoring data and the manual scoring data to obtain target scoring data; The target scoring data is sent according to a preset path.

4. The method for processing assessment material data according to claim 3, characterized in that: Obtain information data through the data input interface, including: Obtain initial information data through the data input interface; The initial data are classified and sorted to obtain the data.

5. A data processing device for expert talent assessment materials, characterized in that: include: A data acquisition module is used to acquire data through a data input interface; A scoring inference module, used to infer the data based on a scoring model to obtain scoring data; wherein the scoring model is a model obtained by training the scored data; A scoring confirmation module, used for sending the scoring data to the scoring terminal so that the scoring terminal performs scoring confirmation processing on the scoring data; The scoring data sending module is used to send the scoring data according to a preset path when the scoring confirmation process is completed.

6. The examination material data processing device according to claim 5, characterized in that: Also includes: The model training module is used to obtain original material data; send the original material data to the scoring terminal so that the scoring terminal obtains original scoring data based on the original material data and returns the original scoring data; train the initial model based on the original material data and the original scoring data to obtain the scoring model.

7. The examination material data processing device according to claim 6, characterized in that: Also includes: A scoring calculation module, used to obtain manual scoring data from the scoring terminal; Calculate the average of the scoring data and the manual scoring data to obtain target scoring data; The target scoring data is sent according to a preset path.

8. The examination material data processing device according to claim 7, characterized in that: The data acquisition module is specifically used to acquire initial data through a data input interface; and classify and sort the initial data to obtain the data.

9. An assessment material data processing device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the examination material data processing method as described in any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for processing assessment material data as described in any one of claims 1 to 4 are implemented.

Images