CN117807631B - Online evaluation method and system based on multiparty security calculation - Google Patents
Online evaluation method and system based on multiparty security calculation Download PDFInfo
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Abstract
The application relates to the technical field of data evaluation, and provides an online evaluation method and an online evaluation system based on multiparty security calculation. The method comprises the following steps: determining effective teaching features; combining the teaching evaluation model to evaluate the teaching quality and determine a first evaluation result; generating an online evaluation popup window, carrying out data recording, and determining multiparty teaching evaluation data; coding encryption processing is carried out on the multiparty teaching evaluation data at the user side, and multiparty encryption teaching evaluation data is determined; determining differential conversion evaluation data by combining a differential conversion model; determining a second evaluation result; and fusing the first evaluation result and the second evaluation result to determine the teaching quality evaluation result of the target teacher. The application solves the technical problems that the angle of evaluating teachers in the prior art is too one-sided and the privacy of users is not protected, and achieves the technical effects of providing more comprehensive and accurate feedback and protecting the privacy of users and the safety of data.
Description
Technical Field
The application relates to the technical field of data evaluation, in particular to an online evaluation method and system based on multiparty security calculation.
Background
Multiparty security computing is a cryptographic technique that aims to protect data privacy while allowing legitimate parties to make certain computations or exchanges. Under the framework of multiparty safety calculation, the participating parties can cooperatively complete specific tasks such as data aggregation, predictive analysis and the like under the condition of not revealing respective data, in the prior art, assessment software for online assessment of teacher courses is too one-sided, the teacher cannot be assessed from multiple dimensions, and the assessment result is unreliable and has strong subjective factors.
In summary, in the prior art, the angle of evaluating the teacher is too one-sided, and the technical problem of protecting the privacy of the user does not exist.
Disclosure of Invention
Based on the above, it is necessary to provide an online evaluation method and system based on multiparty security calculation, which can provide more comprehensive and accurate feedback, protect user privacy and data security.
In a first aspect, the present application provides an online assessment method based on multiparty security computation, the method being applied to a teaching assessment platform, the method comprising: reading teaching record data of a target teacher, importing the teaching record data into the teaching evaluation platform, preprocessing the data, extracting teaching features and determining effective teaching features; combining a teaching evaluation model, carrying out teaching quality evaluation based on the effective teaching features, and determining a first evaluation result, wherein the teaching evaluation model is constructed based on a preset evaluation rule and is embedded in the teaching evaluation platform; generating an online evaluation popup window, carrying out data recording based on a display interface of a multi-user terminal, and determining multiparty teaching evaluation data; identifying personal privacy data based on the online evaluation popup window, carrying out coding encryption processing on the multiparty teaching evaluation data at a user side, and determining multiparty encryption teaching evaluation data; carrying out batch differential conversion processing on the multiparty encryption teaching evaluation data by combining a differential conversion model to determine differential conversion evaluation data; transmitting the differential conversion evaluation data to the teaching evaluation platform, and carrying out multiparty data joint decision to determine a second evaluation result; and fusing the first evaluation result and the second evaluation result to determine the teaching quality evaluation result of the target teacher.
In a second aspect, the present application provides an online assessment system based on multiparty security computing, the system being applied to a teaching assessment platform, the system comprising: the effective teaching feature determining module is used for reading teaching record data of a target teacher, importing the teaching record data into the teaching evaluation platform, preprocessing the data and extracting teaching features, and determining effective teaching features; the teaching quality assessment module is used for carrying out teaching quality assessment based on the effective teaching features in combination with a teaching assessment model to determine a first assessment result, wherein the teaching assessment model is constructed based on a preset assessment rule and is embedded in the teaching assessment platform; the online evaluation popup generating module is used for generating an online evaluation popup, carrying out data recording based on a display interface of a multi-user terminal and determining multiparty teaching evaluation data; the coding encryption processing module is used for identifying personal privacy data based on the online evaluation popup window, carrying out coding encryption processing on the multiparty teaching evaluation data at a user side and determining multiparty encryption teaching evaluation data; the differential conversion evaluation data determining module is used for carrying out batch differential conversion processing on the multiparty encryption teaching evaluation data by combining a differential conversion model to determine differential conversion evaluation data; the differential conversion evaluation data transmission module is used for transmitting the differential conversion evaluation data to the teaching evaluation platform to carry out multi-party data joint decision and determine a second evaluation result; the teaching quality assessment result determining module is used for fusing the first assessment result and the second assessment result and determining a teaching quality assessment result of the target teacher.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
Firstly, reading teaching record data of a target teacher, importing the teaching record data into the teaching evaluation platform, preprocessing the data, extracting teaching features and determining effective teaching features; secondly, combining a teaching evaluation model, carrying out teaching quality evaluation based on the effective teaching features, and determining a first evaluation result, wherein the teaching evaluation model is constructed based on a preset evaluation rule and is embedded in the teaching evaluation platform; generating an online evaluation popup window, carrying out data recording based on a display interface of a multi-user terminal, and determining multiparty teaching evaluation data; then, identifying personal privacy data based on the online evaluation popup window, carrying out coding encryption processing on the multiparty teaching evaluation data at a user side, and determining multiparty encryption teaching evaluation data; then, combining a differential conversion model, carrying out batch differential conversion processing on the multiparty encryption teaching evaluation data, and determining differential conversion evaluation data; transmitting the differential conversion evaluation data to the teaching evaluation platform, and carrying out multiparty data joint decision to determine a second evaluation result; and finally, fusing the first evaluation result and the second evaluation result, and determining the teaching quality evaluation result of the target teacher. The application solves the technical problems that the angle of evaluating teachers in the prior art is too one-sided and the privacy of users is not protected, and achieves the technical effects of providing more comprehensive and accurate feedback and protecting the privacy of users and the safety of data.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a flow diagram of an online assessment method based on multiparty security computing in one embodiment;
FIG. 2 is a flow diagram of determining differential conversion rating data based on an online rating method of multiparty security computation in one embodiment;
FIG. 3 is a block diagram of an online evaluation system based on multiparty security computing in one embodiment.
Reference numerals illustrate: the system comprises an effective teaching characteristic determining module 11, a teaching quality evaluating module 12, an online evaluation popup generating module 13, a coding encryption processing module 14, a differential conversion evaluation data determining module 15, a differential conversion evaluation data transmitting module 16 and a teaching quality evaluating result determining module 17.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, the present application provides an online evaluation method based on multiparty security computation, which is characterized in that the method is applied to a teaching evaluation platform, and the method comprises:
reading teaching record data of a target teacher, importing the teaching record data into the teaching evaluation platform, preprocessing the data, extracting teaching features and determining effective teaching features;
The method comprises the steps of collecting relevant data of teachers and students through a teaching evaluation platform, such as course completion rate, student score distribution, evaluation of the teachers and the like, carrying out data processing on the collected original data to meet subsequent calculation and analysis, and constructing a teaching evaluation model based on a multiparty safety calculation technology by utilizing the preprocessed data, wherein the model can comprehensively and objectively reflect actual teaching conditions; training historical data by using the constructed evaluation model, continuously optimizing model parameters, improving evaluation accuracy, carrying out online evaluation by the trained evaluation model according to teaching data collected in real time to obtain quantitative results of various teaching indexes, and timely feeding back the online evaluation results to related personnel so as to carry out teaching improvement and optimization. The online assessment method based on multiparty security calculation can realize privacy protection and security calculation of teaching data, improve accuracy and instantaneity of teaching assessment, and provide powerful support for teaching quality improvement.
Cleaning and arranging imported teaching record data, namely teaching information related to course design, teaching methods and the like of a teacher, removing irrelevant information, filling missing values, correcting error data and the like so as to ensure the accuracy and consistency of the data; features related to teaching, such as course design, teaching method, student participation, job correction, etc., are extracted from the preprocessed data. These features can reflect the teaching style, ability and effect of the teacher; and screening out effective features which have obvious influence on the evaluation result by analyzing the association degree of the features and the teaching quality. Invalid or redundant features may be removed to improve the accuracy and efficiency of the assessment. The online evaluation method based on multiparty safety calculation is used for constructing and training a subsequent evaluation model by utilizing the effective teaching features. By the method, the teaching quality of teachers is objectively and accurately evaluated while the privacy of data is protected.
Identifying the teaching record data, and performing abnormal data segment positioning and compensation processing to determine preprocessing record data, wherein the abnormal data segment comprises data missing and data error;
based on the preprocessing record data, combining with a teaching quality index matrix, extracting index features as the effective teaching features;
wherein, confirm the compensation processing mode, include:
If the length of the abnormal data segment is smaller than the preset length interval, performing compensation processing based on a mean value interpolation mode;
And if the length of the abnormal data segment is greater than the preset length interval, performing compensation processing based on a trend prediction mode.
The abnormal data segments in the teaching record data, such as continuous data missing or obvious data errors, are identified and positioned, the abnormal data segments can be identified through preset rules or algorithms, the abnormal data segments are determined, compensation processing needs to be carried out on the abnormal data segments, and the compensation processing aims at recovering the missing or erroneous data so as to be as close to a true value as possible. Different compensation processing modes can be adopted according to the length of the abnormal data segment: if the length of the abnormal data segment is smaller than the preset length interval, compensation processing can be performed in a mean value interpolation-based mode, wherein the preset length interval is set by a worker. The method is suitable for abnormal data in a short time, the missing or wrong data value can be estimated by calculating the average value before and after the abnormal data segment and then interpolating, and if the length of the abnormal data segment is greater than the preset length interval, the compensation processing can be performed in a mode based on trend prediction. The method is suitable for long-time data abnormality, and the value of the abnormal data segment is predicted and compensated by analyzing the data trend before the abnormal data segment and utilizing regression analysis or other prediction models. After the compensation processing is completed, the determined preprocessing record data is combined with the teaching quality index matrix, and index features related to teaching quality are further extracted. These features will become effective teaching features for subsequent online evaluation. The online evaluation method based on multiparty safety calculation can effectively process abnormal data segments, extract effective index features and provide an accurate and reliable data basis for subsequent evaluation. Meanwhile, the method can protect the privacy and safety of the data and realize the safety calculation and evaluation among multiple parties.
Combining a teaching evaluation model, carrying out teaching quality evaluation based on the effective teaching features, and determining a first evaluation result, wherein the teaching evaluation model is constructed based on a preset evaluation rule and is embedded in the teaching evaluation platform;
The teaching assessment model is constructed based on predetermined assessment rules, which are set according to the requirements and goals of the teaching assessment. The model is used for calculating and analyzing according to the effective teaching characteristics by utilizing the rules to obtain a teaching quality evaluation result, the extracted effective teaching characteristics are input into a teaching evaluation model, and the model is calculated according to preset evaluation rules. These rules may include aspects of the quality of the course design, the effectiveness of the teaching method, the participation of students, job corrections, etc. By comprehensively analyzing the characteristics, the model can obtain a quantized evaluation result, namely a first evaluation result. The teaching assessment model is generally embedded in the teaching assessment platform, so that the teaching assessment model can be conveniently integrated with other functions of the platform, such as data visualization, result feedback and the like. Through the embedded model, teaching quality evaluation can be performed more efficiently, and an evaluation result can be provided in real time. And the teaching quality evaluation is carried out by combining the teaching evaluation model and the effective teaching characteristics, so that an objective and accurate evaluation result can be obtained. This result can provide valuable feedback to teachers, helping them to improve teaching methods and to improve teaching quality. Meanwhile, based on the characteristics of multiparty security calculation, the whole evaluation process can protect the privacy and security of data, and information among all parties is ensured not to be revealed.
Generating an online evaluation popup window, carrying out data recording based on a display interface of a multi-user terminal, and determining multiparty teaching evaluation data;
Through the display interface of the multi-user terminal, the appearance and the function of the bullet window can be designed and displayed and evaluated. The online evaluation popup window is clear and concise, and is easy to understand and operate by a user. In addition, the online evaluation popup should also provide necessary evaluation indexes in aspects of teaching content, teacher performance and the like, so that a user can evaluate the online evaluation popup, and the online evaluation popup can be automatically popped up or can be manually triggered when the user accesses the teaching evaluation platform through a multi-user terminal. And the user carries out online evaluation on the teaching quality of the target teacher according to the provided evaluation index. These rating data will be an important component of multiparty teaching rating data; the key steps of generating an online evaluation popup window and carrying out data recording based on a display interface of a multi-user terminal are realized for multi-party teaching evaluation data collection. By the method, the comments and suggestions of different users on teaching can be collected widely, and comprehensive data support is provided for subsequent teaching quality evaluation. Meanwhile, the online evaluation mode can improve the evaluation efficiency and participation degree, so that teaching evaluation is more objective and fair.
Identifying personal privacy data based on the online evaluation popup window, carrying out coding encryption processing on the multiparty teaching evaluation data at a user side, and determining multiparty encryption teaching evaluation data;
In online assessment methods based on multiparty security computing, it is very important to protect user privacy data. Therefore, when generating the online evaluation popup, special attention needs to be paid to identifying and processing data related to personal privacy, and identification and classification need to be performed on personal privacy data collected in the online evaluation popup, such as sensitive information of user names, contact ways and the like. The data are not directly used for teaching evaluation analysis so as to avoid the privacy of users, and the teaching evaluation data can be converted into an unreadable format through an encryption algorithm, so that the safety of the data in the transmission and storage processes is ensured. The encryption processing can effectively prevent data from being illegally acquired or tampered; the multiparty encrypted teaching evaluation data after the encoding encryption processing can be transmitted to a teaching evaluation platform for subsequent analysis and processing. Because the data is encrypted, even if the data is intercepted in the transmission process, the original data cannot be easily decrypted and read, so that the privacy security of a user is protected. The online evaluation popup data related to personal privacy is identified and encrypted, and the online evaluation method based on multiparty security calculation can ensure that the user privacy is not revealed, and meanwhile, the reliable transmission and storage of teaching evaluation data are realized.
Carrying out batch differential conversion processing on the multiparty encryption teaching evaluation data by combining a differential conversion model to determine differential conversion evaluation data;
The differential conversion model is a method for data processing and analysis that converts raw data into a differential form by performing a differential operation on each element in a data set. Such transformations may eliminate sensitive information and trends in the data, making the data more difficult to recover or analyze; and for the multiparty encryption teaching evaluation data, firstly dividing the multiparty encryption teaching evaluation data according to batches, and then carrying out differential conversion processing on the data of each batch. In the conversion process, the previous data point is subtracted from each data point to obtain a differential value. In this way, continuity and trends in the data may be eliminated, making the data more difficult to correlate and infer. The data subjected to the differential conversion processing is referred to as differential conversion evaluation data. Such data forms cannot be used directly for quality of teaching assessment analysis because they have lost specific values of the original data. The differential conversion evaluation data can be used for safe multiparty calculation and comparative analysis to obtain relevant indexes or ranking results of teaching quality. The multi-party encryption teaching evaluation data is subjected to batch differential conversion processing by combining the differential conversion model, so that the safety and privacy protection of the data can be further enhanced. The method can prevent the leakage and abuse of the original data, and simultaneously ensure the accuracy and fairness of the evaluation result. The multi-party encryption teaching evaluation data is subjected to batch differential conversion processing by combining the differential conversion model, so that the data safety and privacy protection are improved. By the method, the online assessment method based on multiparty security calculation can better balance the requirements of data privacy and teaching quality assessment, and a safer and more reliable teaching quality assessment solution is provided for the education field.
As shown in fig. 2, the availability and privacy degree of data are balanced, and a differential conversion degree is determined;
dividing the multiparty encryption teaching evaluation data based on the type of the user end to determine a plurality of data sets;
and transmitting the multiple data sets to the differential conversion model, and sequentially carrying out differential conversion processing on the data sets by taking the differential conversion degree as a constraint to determine the differential conversion evaluation data.
In online evaluation methods based on multiparty security computing, in order to balance data availability with privacy, a suitable degree of differential conversion needs to be determined. The differential conversion degree determines the degree of data conversion and the protection degree of sensitive information, and a reasonable differential conversion degree range can be set according to the requirements of data availability and privacy degree. This range can be adjusted according to the actual application scenario and safety requirements. By adjusting the differential conversion degree, the usability of the data can be kept while the privacy is protected, so that the data can still be used for teaching quality evaluation after conversion; based on the type of the user end, the multiparty encryption teaching evaluation data are segmented to form a plurality of data sets. The client type may include different devices, browsers, or operating systems, etc. The presentation and transmission efficiency of data may vary according to the type of the client. Therefore, dividing the data into a plurality of data sets can improve the processing efficiency and the transmission stability of the data; and transmitting the multiple data sets to the differential conversion model one by one for processing. On each data group, differential conversion processing is performed according to the differential conversion degree determined previously. The protection degree and usability of sensitive information of the data can be controlled by adjusting the parameters of the differential conversion degree; after differential conversion processing, a series of differential conversion evaluation data can be obtained. These data will be used for subsequent multiparty security calculations and analysis. Because the data is subjected to differential conversion processing, even if the data is intercepted in the transmission and storage processes, the original data is difficult to directly restore, so that the privacy safety of a user is protected. The differential conversion degree is determined by balancing the availability and the privacy degree of the data, the multiparty encryption teaching evaluation data is divided and subjected to differential conversion processing based on the type of the user side, and the online evaluation method based on multiparty security calculation can better balance the requirements of data privacy and teaching quality evaluation. The method can provide a safer and more reliable teaching quality assessment solution, and simultaneously protect the privacy rights and interests of users.
Configuring evaluation weight distribution based on the type of the user terminal;
and carrying out weighting configuration on a plurality of differential conversion results based on the evaluation weight distribution, and integrating and determining the differential conversion evaluation data.
In the online evaluation method based on multiparty security calculation, in order to further optimize the accuracy and fairness of the evaluation result, the distribution of the evaluation weight can be configured according to the type of the user terminal. The differential conversion evaluation data can be more reasonably integrated by adjusting the weights of different types of user terminals; different evaluation weights can be set according to the characteristics of the user side type and the data quality. For example, for a user side where the operating user is a student, a higher weight may be given due to its broad nature; for a specific browser or an operating system, the weight can be adjusted correspondingly because of possible differences between the use groups and habits of the specific browser or the operating system; and carrying out weighting configuration on the multiple differential conversion results based on the evaluation weight distribution. Specifically, each differential conversion result is multiplied by a corresponding weight to reflect the contribution degree of different user side types to the evaluation result. By the method, the evaluation data of different user terminals can be comprehensively considered, and the evaluation accuracy and representativeness are improved. And carrying out weighted average or other statistical processing on the integrated differential conversion evaluation data to obtain a final evaluation result. This result will more objectively reflect the quality of the teaching, while taking into account the opinion and contributions of the different clients. The online evaluation method based on multiparty security calculation can further improve the accuracy and fairness of the evaluation result by configuring the evaluation weight distribution based on the user side type and carrying out weighting configuration and integration on the multiple differential conversion results. The method can better balance the data weight and privacy protection of different user terminals, and provides more comprehensive and reliable support for teaching quality evaluation.
Determining a multi-element evaluation dimension based on the online evaluation popup window;
constructing a multi-element evaluation coordinate system by taking the multi-element evaluation dimension as a coordinate axis;
For the differential conversion evaluation data, based on the attribution of the user terminal type, sequentially carrying out distribution based on the multi-element evaluation coordinate system, and determining a plurality of groups of coordinate evaluation data, wherein the plurality of groups of coordinate evaluation data are in one-to-one correspondence with the user terminal type;
and determining the second evaluation result by joint analysis based on the plurality of sets of coordinate evaluation data.
Based on the online evaluation popup window, multiple evaluation dimensions, such as teaching targets, teaching contents, teaching methods, class atmosphere and the like, can be determined. The dimensions can comprehensively reflect different aspects of teaching quality, and a richer evaluation view angle is provided for a user; and constructing a multi-element evaluation coordinate system by taking the multi-element evaluation dimension as the coordinate axis. This coordinate system can be used to represent combinations and relationships of different evaluation dimensions. The differential conversion evaluation data can be mapped to corresponding positions through a coordinate system, and the evaluation result is displayed in a visual mode; and mapping the differential conversion evaluation data into a corresponding multi-element evaluation coordinate system according to attribution of the user side type. The data of each user side type will generate a set of coordinate evaluation data reflecting the evaluation result of the teaching quality by the user of the type. By the method, the views and opinions of different user ends can be integrated, so that the evaluation result is more comprehensive and objective. Multiple sets of coordinate evaluation data may be jointly analyzed and a second evaluation result determined. By comparing the data distribution and the difference of different user end types, the characteristics and the advantages of the teaching quality can be further analyzed. By combining the teaching evaluation model and preset rules, a second evaluation result which is more accurate and reliable can be obtained. The online evaluation method based on multiparty safety calculation can provide more comprehensive and accurate evaluation results by determining a multielement evaluation dimension based on the online evaluation popup window, constructing a multielement evaluation coordinate system and determining a second evaluation result based on joint analysis of multiple sets of coordinate evaluation data. The method can better reflect the opinion and the view of different user terminals on the teaching quality, and provides valuable feedback for teachers to improve the teaching method and the teaching quality.
Performing data discrete analysis and trend evaluation on each group of coordinate evaluation data to determine a plurality of data evaluation results;
And carrying out weighting calculation on the plurality of data evaluation results based on the evaluation weight distribution, and determining the second evaluation result.
Discrete analysis and trend evaluation were performed on each set of coordinate evaluation data. Discrete analysis can identify the degree of dispersion and outliers of the data, while trend assessment can reveal trends in the data over time or other factors. Through the analysis, the characteristics and rules of each group of data can be deeply known, a basis is provided for subsequent weighting calculation, a plurality of data evaluation results are determined, and the weighting calculation is performed on the plurality of data evaluation results based on the evaluation weight distribution. And according to the weight distribution determined before, giving corresponding weights to each group of coordinate evaluation data so as to reflect the importance and contribution degree of the coordinate evaluation data in the overall evaluation. Through weighted calculation, a more objective and accurate second evaluation result can be obtained, and the final second evaluation result is integrated and presented. The result comprehensively considers a plurality of factors such as multiple evaluation dimensions, user side types, discrete analysis, trend evaluation, weight calculation and the like, and provides a comprehensive visual angle and an accurate conclusion for teaching quality evaluation. The online evaluation method based on multiparty safety calculation can further improve the accuracy and reliability of the evaluation result by performing discrete analysis and trend evaluation on each group of coordinate evaluation data and performing weighting calculation by combining evaluation weight distribution. The method can provide more reliable teaching quality assessment results for teachers and teaching management staff.
Transmitting the differential conversion evaluation data to the teaching evaluation platform, and carrying out multiparty data joint decision to determine a second evaluation result;
And transmitting the differential conversion evaluation data from the user side to the teaching evaluation platform. This process needs to ensure the security and privacy protection of the data, preventing the data from being tampered or stolen. The security of data transmission can be ensured by adopting technical means such as encryption transmission, a secure channel and the like, and in the transmission process, differential conversion evaluation data are subjected to differential conversion processing and cannot be directly restored to original data. This can protect the privacy of the user and the security of the data. After the teaching evaluation platform receives the data, multiparty data joint decision and analysis can be further carried out. In multiparty data joint decision, the teaching evaluation platform needs to integrate data from different user terminals for comprehensive analysis and comparison. The process needs to consider the discrete degree, trend change, weight distribution and other factors of the data so as to obtain a more accurate and objective evaluation result. Through the joint decision, the teaching assessment platform can determine a second assessment result. Through the combined decision, multiparty data and opinions can be comprehensively considered, more accurate and reliable results are obtained, and comprehensive visual angles and accurate conclusions are provided for teaching quality evaluation. Meanwhile, the method can protect the privacy of the user and the safety of the data, and meets the requirement of multiparty safety calculation.
And fusing the first evaluation result and the second evaluation result to determine the teaching quality evaluation result of the target teacher.
By comprehensively analyzing the characteristics, the model can obtain a quantized evaluation result, namely a first evaluation result, and belongs to the objective evaluation category. The second evaluation result is based on a multiparty safety calculation method, and factors such as the type of the user side, the multiple evaluation dimension, the data conversion processing and the like are comprehensively considered to belong to the subjective evaluation category; the two results are fused, so that the advantages of the two results can be fully exerted, and the accuracy and the comprehensiveness of teaching quality assessment are improved. In the fusion process, methods such as weighted average, weight adjustment and the like can be adopted, and reasonable weight distribution can be carried out on the first evaluation result and the second evaluation result according to actual conditions. The weight distribution can be adjusted according to factors such as data quality, importance of evaluation indexes and the like so as to reflect the relative contribution degree of each evaluation result. By fusing the first evaluation result and the second evaluation result, the teaching quality evaluation result of the target teacher can be determined. This result will provide a comprehensive and accurate assessment, providing the teacher with targeted feedback and improvement advice. Meanwhile, the teaching quality evaluation result can also provide decision basis for teaching management personnel, is used for teaching management activities such as teacher evaluation and resource allocation, and can obtain more accurate and comprehensive teaching quality evaluation results by fusing the advantages and limitations of the first evaluation result and the second evaluation result, thereby providing valuable feedback and guidance for teachers to improve teaching methods and teaching quality. Meanwhile, the method meets the requirement of multiparty security calculation and protects the privacy and data security of the user.
Performing point-to-point mapping on the first evaluation result and the second evaluation result to determine a plurality of mapping sequences;
Traversing the mapping sequences, performing common-frequency correction, and determining N common-frequency mapping sequences and M difference-frequency mapping sequences;
Performing intra-group average value calculation on the M difference frequency mapping sequences to determine M effective difference frequency data;
and integrating the N same-frequency mapping sequences and the M effective difference frequency data to serve as teaching quality assessment results of the target teacher.
And performing point-to-point mapping on the first evaluation result and the second evaluation result to determine a plurality of mapping sequences. The point-to-point mapping can establish a corresponding relation between two evaluation results, so that each data point can find a corresponding mapping relation. Through the mapping, the difference and the consistency of two evaluation results can be better compared, a plurality of mapping sequences are traversed, and the same-frequency correction is performed. The same frequency calibration can identify mapped sequences of the same frequency that have similarity and comparability in data. Through the same frequency calibration, N same frequency mapping sequences and M difference frequency mapping sequences can be determined, wherein M and N are natural numbers such as 1,2, 3 and the like, and intra-group average value calculation is performed on the M difference frequency mapping sequences so as to determine M effective difference frequency data. The intra-group mean value calculation can eliminate abnormal values and fluctuation in the difference frequency data, and improve the stability and reliability of the data. Through intra-group mean value calculation, more accurate and reliable difference frequency data can be obtained, and N same-frequency mapping sequences and M effective difference frequency data are integrated to serve as teaching quality assessment results of target teachers. The result comprehensively considers the advantages and limitations of the same-frequency data and the difference-frequency data, provides a comprehensive and accurate evaluation, namely, the results in two modes are consistent and are directly applied; and (5) taking the result mean value as a final evaluation result and integrating the result mean value. Meanwhile, the integrated evaluation result can provide more targeted feedback and improvement suggestions for teachers. The first evaluation result and the second evaluation result are processed through methods such as point-to-point mapping, same-frequency correction and intra-group mean value calculation, and the accuracy and reliability of the teaching quality evaluation result can be further improved. The method can provide more comprehensive and accurate feedback for teachers and teaching management staff, and is helpful for improving the teaching method and the teaching quality. The application solves the technical problems that the angle of evaluating teachers in the prior art is too one-sided and the privacy of users is not protected, and achieves the technical effects of providing more comprehensive and accurate feedback and protecting the privacy of users and the safety of data.
As shown in fig. 3, the system is applied to a teaching evaluation platform and comprises:
The effective teaching feature determining module 11 is used for reading teaching record data of a target teacher, importing the teaching record data into the teaching evaluation platform, preprocessing the data and extracting teaching features, and determining effective teaching features;
The teaching quality assessment module 12 is used for carrying out teaching quality assessment based on the effective teaching features in combination with a teaching assessment model, and determining a first assessment result, wherein the teaching assessment model is constructed based on a preset assessment rule and is embedded in the teaching assessment platform;
The online evaluation popup generating module 13 is used for generating an online evaluation popup, carrying out data recording based on a display interface of a multi-user terminal and determining multiparty teaching evaluation data;
The coding encryption processing module 14 is used for identifying personal privacy data based on the online evaluation popup window, performing coding encryption processing on the multiparty teaching evaluation data at a user side, and determining multiparty encryption teaching evaluation data;
The differential conversion evaluation data determining module 15 is used for carrying out batch differential conversion processing on the multiparty encryption teaching evaluation data by combining a differential conversion model to determine differential conversion evaluation data;
The differential conversion evaluation data transmission module 16 is configured to transmit the differential conversion evaluation data to the teaching evaluation platform, perform a multiparty data joint decision, and determine a second evaluation result;
The teaching quality assessment result determining module 17, where the teaching quality assessment result determining module 17 is configured to fuse the first assessment result and the second assessment result, and determine a teaching quality assessment result of the target teacher.
Further, the embodiment of the application further comprises:
The preprocessing record data determining module is used for identifying the teaching record data, carrying out abnormal data segment positioning and compensation processing and determining preprocessing record data, wherein the abnormal data segment comprises data missing and data error;
The effective teaching feature extraction module is used for extracting index features based on the preprocessing record data and combining with a teaching quality index matrix to serve as the effective teaching features;
The compensation processing mode determining module is used for determining a compensation processing mode, and comprises the following steps:
The average value interpolation mode processing module is used for carrying out compensation processing based on the average value interpolation mode if the length of the abnormal data segment is smaller than the preset length interval;
And the trend prediction mode processing module is used for carrying out compensation processing based on the trend prediction mode if the length of the abnormal data segment is greater than the preset length interval.
Further, the embodiment of the application further comprises:
The differential conversion degree determining module is used for balancing the availability and privacy degree of the data and determining the differential conversion degree;
the multi-item data set determining module is used for dividing the multi-party encryption teaching evaluation data based on the type of the user side to determine a plurality of item data sets;
And the differential conversion evaluation data determining module is used for transmitting the plurality of data sets to the differential conversion model, and sequentially carrying out differential conversion processing on each data set by taking the differential conversion degree as a constraint to determine the differential conversion evaluation data.
Further, the embodiment of the application further comprises:
The evaluation weight distribution configuration module is used for configuring evaluation weight distribution based on the type of the user side;
And the differential conversion evaluation data integration module is used for carrying out weighting configuration on a plurality of differential conversion results based on the evaluation weight distribution and integrating and determining the differential conversion evaluation data.
Further, the embodiment of the application further comprises:
The multi-element evaluation dimension determining module is used for determining multi-element evaluation dimensions based on the online evaluation popup window;
The multi-element evaluation coordinate system building module is used for building a multi-element evaluation coordinate system by taking the multi-element evaluation dimension as a coordinate axis;
the multi-set coordinate evaluation data determining module is used for determining multi-set coordinate evaluation data according to the distribution of the multi-element evaluation coordinate system based on the attribution of the user side type aiming at the differential conversion evaluation data, wherein the multi-set coordinate evaluation data corresponds to the user side type one by one;
And the evaluation result determining module is used for determining the second evaluation result based on the plurality of sets of coordinate evaluation data through joint analysis.
Further, the embodiment of the application further comprises:
The system comprises a plurality of data evaluation result determining modules, a data analysis module and a data analysis module, wherein the data evaluation result determining modules are used for performing data discrete analysis and trend evaluation on each group of coordinate evaluation data to determine a plurality of data evaluation results;
And the data evaluation result weighting calculation module is used for carrying out weighting calculation on the plurality of data evaluation results based on the evaluation weight distribution and determining the second evaluation result.
Further, the embodiment of the application further comprises:
the mapping sequence determining module is used for performing point-to-point mapping on the first evaluation result and the second evaluation result to determine a plurality of mapping sequences;
The same-frequency correction module is used for traversing the plurality of mapping sequences, carrying out same-frequency correction and determining N same-frequency mapping sequences and M difference-frequency mapping sequences;
The effective difference frequency data determining module is used for carrying out intra-group mean value calculation on the M difference frequency mapping sequences to determine M effective difference frequency data;
The teaching quality evaluation result acquisition module is used for integrating the N same-frequency mapping sequences and the M effective difference frequency data to serve as a teaching quality evaluation result of the target teacher.
For a specific embodiment of the online evaluation system based on multiparty security computation, reference may be made to the above embodiment of the online evaluation method based on multiparty security computation, which is not described herein. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (6)
1. An online evaluation method based on multiparty security calculation, which is characterized in that the method is applied to a teaching evaluation platform, and comprises the following steps:
reading teaching record data of a target teacher, importing the teaching record data into the teaching evaluation platform, preprocessing the data, extracting teaching features and determining effective teaching features;
Combining a teaching evaluation model, carrying out teaching quality evaluation based on the effective teaching features, and determining a first evaluation result, wherein the teaching evaluation model is constructed based on a preset evaluation rule and is embedded in the teaching evaluation platform;
generating an online evaluation popup window, carrying out data recording based on a display interface of a multi-user terminal, and determining multiparty teaching evaluation data;
Identifying personal privacy data based on the online evaluation popup window, carrying out coding encryption processing on the multiparty teaching evaluation data at a user side, and determining multiparty encryption teaching evaluation data;
Carrying out batch differential conversion processing on the multiparty encryption teaching evaluation data by combining a differential conversion model to determine differential conversion evaluation data;
Transmitting the differential conversion evaluation data to the teaching evaluation platform, and carrying out multiparty data joint decision to determine a second evaluation result;
Fusing the first evaluation result and the second evaluation result to determine a teaching quality evaluation result of the target teacher;
and carrying out batch differential conversion processing on the multiparty encryption teaching evaluation data by combining a differential conversion model, wherein the batch differential conversion processing comprises the following steps:
Equalizing the availability and privacy degree of data, determining differential conversion degree, wherein the differential conversion degree is the degree of data conversion and the protection degree of sensitive information, and setting a reasonable differential conversion degree range according to the requirements of the availability and privacy degree of the data;
dividing the multiparty encryption teaching evaluation data based on the type of the user end to determine a plurality of data sets;
transmitting the multiple data sets to the differential conversion model, and sequentially carrying out differential conversion processing on the data sets by taking the differential conversion degree as a constraint to determine the differential conversion evaluation data;
Making multi-party data federation decisions, comprising:
Determining a multi-element evaluation dimension based on the online evaluation popup window;
constructing a multi-element evaluation coordinate system by taking the multi-element evaluation dimension as a coordinate axis;
For the differential conversion evaluation data, based on the attribution of the user terminal type, sequentially carrying out distribution based on the multi-element evaluation coordinate system, and determining a plurality of groups of coordinate evaluation data, wherein the plurality of groups of coordinate evaluation data are in one-to-one correspondence with the user terminal type;
and determining the second evaluation result by joint analysis based on the plurality of sets of coordinate evaluation data.
2. The method of claim 1, wherein performing data preprocessing and teaching feature extraction comprises:
Identifying the teaching record data, and performing abnormal data segment positioning and compensation processing to determine preprocessing record data, wherein the abnormal data segment comprises data missing and data error;
based on the preprocessing record data, combining with a teaching quality index matrix, extracting index features as the effective teaching features;
wherein, confirm the compensation processing mode, include:
If the length of the abnormal data segment is smaller than the preset length interval, performing compensation processing based on a mean value interpolation mode;
And if the length of the abnormal data segment is greater than the preset length interval, performing compensation processing based on a trend prediction mode.
3. The method of claim 1, wherein the differential conversion processing of each data set is sequentially performed, and then comprising:
Configuring evaluation weight distribution based on the type of the user terminal;
and carrying out weighting configuration on a plurality of differential conversion results based on the evaluation weight distribution, and integrating and determining the differential conversion evaluation data.
4. A method according to claim 3, wherein determining the second evaluation result by joint analysis comprises:
performing data discrete analysis and trend evaluation on each group of coordinate evaluation data to determine a plurality of data evaluation results;
And carrying out weighting calculation on the plurality of data evaluation results based on the evaluation weight distribution, and determining the second evaluation result.
5. The method of claim 1, wherein fusing the first evaluation result with the second evaluation result comprises:
Performing point-to-point mapping on the first evaluation result and the second evaluation result to determine a plurality of mapping sequences;
traversing the mapping sequences, performing same-frequency correction, and determining 1 same-frequency mapping sequence and 0 difference-frequency mapping sequence;
performing intra-group average value calculation on the 0 difference frequency mapping sequences to determine 0 effective difference frequency data;
and integrating the 1 same-frequency mapping sequence and the 0 effective difference frequency data to serve as a teaching quality evaluation result of the target teacher.
6. An online assessment system based on multiparty security computing, wherein the system is applied to a teaching assessment platform, the system comprising:
The effective teaching feature determining module is used for reading teaching record data of a target teacher, importing the teaching record data into the teaching evaluation platform, preprocessing the data and extracting teaching features, and determining effective teaching features;
the teaching quality assessment module is used for carrying out teaching quality assessment based on the effective teaching features in combination with a teaching assessment model to determine a first assessment result, wherein the teaching assessment model is constructed based on a preset assessment rule and is embedded in the teaching assessment platform;
The online evaluation popup generating module is used for generating an online evaluation popup, carrying out data recording based on a display interface of a multi-user terminal and determining multiparty teaching evaluation data;
The coding encryption processing module is used for identifying personal privacy data based on the online evaluation popup window, carrying out coding encryption processing on the multiparty teaching evaluation data at a user side and determining multiparty encryption teaching evaluation data;
the differential conversion evaluation data determining module is used for carrying out batch differential conversion processing on the multiparty encryption teaching evaluation data by combining a differential conversion model to determine differential conversion evaluation data;
The differential conversion evaluation data transmission module is used for transmitting the differential conversion evaluation data to the teaching evaluation platform to carry out multi-party data joint decision and determine a second evaluation result;
The teaching quality assessment result determining module is used for fusing the first assessment result and the second assessment result and determining a teaching quality assessment result of the target teacher;
The differential conversion degree determining module is used for balancing the availability and the privacy degree of the data and determining the differential conversion degree, wherein the differential conversion degree is the degree of data conversion and the protection degree of sensitive information, and a reasonable differential conversion degree range is set according to the requirements of the availability and the privacy degree of the data;
the multi-item data set determining module is used for dividing the multi-party encryption teaching evaluation data based on the type of the user side to determine a plurality of item data sets;
The differential conversion evaluation data determining module is used for transmitting the plurality of data sets to the differential conversion model, and sequentially carrying out differential conversion processing on each data set by taking the differential conversion degree as a constraint to determine the differential conversion evaluation data;
The multi-element evaluation dimension determining module is used for determining multi-element evaluation dimensions based on the online evaluation popup window;
The multi-element evaluation coordinate system building module is used for building a multi-element evaluation coordinate system by taking the multi-element evaluation dimension as a coordinate axis;
the multi-set coordinate evaluation data determining module is used for determining multi-set coordinate evaluation data according to the distribution of the multi-element evaluation coordinate system based on the attribution of the user side type aiming at the differential conversion evaluation data, wherein the multi-set coordinate evaluation data corresponds to the user side type one by one;
And the evaluation result determining module is used for determining the second evaluation result based on the plurality of sets of coordinate evaluation data through joint analysis.
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