CN117852976A - Method and system for intelligently generating rewards - Google Patents
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Abstract
The application discloses a method and a system for intelligently generating rewards, which can improve flexibility and accuracy of generated rewards. The method for intelligently generating the prize comprises the following steps: acquiring dimension data for evaluating the prize of all individuals in a group in preset time; based on an entropy weight method, carrying out weight analysis on each dimension data to obtain a first weight ratio of a plurality of dimension data; screening out individuals meeting preset judgment standards in a group according to the first weight ratio of the dimension data and a fuzzy evaluation method; obtaining the ranking of individuals meeting preset judgment standards in a group based on a factor analysis method or a fuzzy evaluation method; sequentially generating rewards corresponding to different dimension data according to the ranking and a second weight ratio of the dimension data of the individual reaching a preset judgment standard; wherein each of the awards is positively correlated with the second weight ratio.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method and a system for intelligently generating rewards.
Background
Traditional prize types are usually static and fixed, automatic dynamic adjustment cannot be carried out according to other factors such as data, dynamic adjustment cannot be carried out according to actual performances of individuals, more excellent or advanced individuals cannot be popularized, and the selected winning individuals are usually influenced by subjective judgment of an evaluator, so that subjective deviation and unfairness can be caused. Therefore, the method can comprehensively evaluate the quality of the individuals in a data analysis mode, but can be used for evaluating more data dimensions of the individuals, is easy to miss or is lack of focus in evaluation, and cannot establish a flexible, objective and accurate evaluation mode.
Disclosure of Invention
The present application has been made in order to solve the above technical problems. The embodiment of the application provides a method and a system for intelligently generating rewards, which can improve the flexibility and accuracy of the generated rewards.
According to one aspect of the present application, there is provided a method of intelligently generating a prize, comprising: acquiring dimension data for evaluating the prize of all individuals in a group in preset time; based on an entropy weight method, carrying out weight analysis on each dimension data to obtain a first weight ratio of a plurality of dimension data; screening out individuals meeting preset judgment standards in a group according to the first weight ratio of the dimension data and a fuzzy evaluation method; obtaining the ranking of individuals meeting preset judgment standards in a group based on a factor analysis method or a fuzzy evaluation method; sequentially generating rewards corresponding to different dimension data according to the ranking and a second weight ratio of the dimension data of the individual reaching a preset judgment standard; wherein each of the awards is positively correlated with the second weight ratio.
In an embodiment, after screening out the individuals meeting the preset evaluation criteria in the group according to the first weight ratio of the dimension data and the fuzzy evaluation method, the method for intelligently generating the prize further includes: acquiring a collective attribute or an activity attribute; wherein the collective attribute indicates that the collective belongs to any dimension, and the activity attribute indicates that the activity belongs to any dimension; and adjusting the weight ratio coefficient of the dimension data corresponding to the individual reaching the preset judgment standard according to the collective attribute or the activity attribute.
In an embodiment, after the adjusting the weight ratio coefficient of the dimension data corresponding to the individual meeting the preset evaluation criterion according to the collective attribute or the activity attribute, the method for intelligently generating the prize includes: multiplying the dimension data corresponding to the individuals reaching the preset judgment standard by the weight ratio coefficient based on the dimension to which the collective attribute belongs or the dimension to which the activity attribute belongs to obtain the final score of the individuals reaching the preset judgment standard; the ranking of the individuals reaching the preset judgment standard in the group is obtained based on the factor analysis method or the fuzzy evaluation method, and the ranking comprises the following steps: and ranking the individuals reaching the preset evaluation standard in the group based on a factor analysis method or a fuzzy evaluation method according to the final scores of the individuals reaching the preset evaluation standard, and obtaining the ranking of the individuals reaching the preset evaluation standard in the group.
In an embodiment, before the ranking of the individuals in the group reaching the preset evaluation criterion is obtained based on the factor analysis method or the fuzzy evaluation method, the method for intelligently generating the prize further includes: acquiring individual data of individuals meeting preset judging standards in a group; sampling suitability test and bat Li Qiuxing test are carried out on the individual data to obtain test results.
In one embodiment, obtaining the ranking of individuals in the group that meet the preset criteria based on a factor analysis method or a fuzzy evaluation method includes: when the test result indicates that the individual data is not suitable for the factor analysis method, obtaining a first ranking of individuals meeting preset judgment standards in a group through the fuzzy evaluation method; or when the test result indicates that the individual data is suitable for the factor analysis method, obtaining a second ranking of individuals meeting preset judging standards in a group through the factor analysis method.
In an embodiment, screening the individuals meeting the preset evaluation criteria in the group according to the first weight ratio of the dimension data and the fuzzy evaluation method includes: according to the first weight duty ratio of the plurality of dimension data, analyzing the plurality of dimension data respectively to obtain a factor weight vector; and screening out individuals meeting preset judgment standards in a group according to the factor weight vector and the fuzzy evaluation method.
In an embodiment, screening the individuals in the group that reach the preset evaluation criteria according to the factor weight vector and the fuzzy evaluation method includes: constructing a single factor judgment matrix according to a preset comment set and a preset factor set; changing the factor weight on the factor set into a fuzzy vector on the comment set through fuzzy change; and determining individuals meeting preset judgment standards in the collective according to the fuzzy vectors.
In an embodiment, generating the awards corresponding to different dimension data sequentially according to the ranking and the second weight ratio of the dimension data of the individual meeting the preset evaluation criterion includes: generating a prize item for the corresponding individual according to the first dimension data when the second weight ratio of the first dimension data of the individual, any one of which reaches a preset judgment standard, is highest based on the ranking from high to low; or generating a prize item on the second dimension data for the corresponding individual when the second weight ratio of the second dimension data of the individual, any one of which reaches a preset judgment standard, is highest based on the ranking from high to low.
In one embodiment, obtaining dimension data for a prize in a preset time for all individuals in a group includes: triggering an acquisition instruction according to a scoring event required in a group; acquiring dimension data for evaluating the prize of all individuals in the group in preset time according to the acquisition instruction; wherein the preset time is positively correlated with a prize evaluation period of the prize evaluation event.
According to another aspect of the present application, there is provided a system for intelligently generating a prize, comprising: the acquisition module is used for acquiring dimension data for evaluating the prize of all individuals in the group in a preset time; the analysis module is used for carrying out weight analysis on each piece of dimension data based on an entropy weight method to obtain first weight duty ratios of a plurality of pieces of dimension data; the screening module is used for screening out individuals meeting preset judgment standards in a group according to the first weight ratio of the dimension data and the fuzzy evaluation method; the ranking module is used for obtaining the ranking of the individuals reaching the preset judgment standard in the group based on a factor analysis method or a fuzzy evaluation method; the generating module is used for sequentially generating rewards corresponding to different dimension data according to the ranking and the second weight ratio of the dimension data of the individual reaching a preset judgment standard; wherein each of the awards is positively correlated with the second weight ratio.
According to the method and the system for intelligently generating the rewards, the dimension data of the individuals in a period of time are obtained, the rewards are dynamically generated based on the fuzzy evaluation method and the factor analysis algorithm, the score, the weight and the multiple factor analysis of the individuals in different dimensions in a period of time are combined, the rewards standard is judged, the rewards are created, and the characteristic rewards can be generated according to the characteristics of the individuals, so that the method and the system have higher flexibility. The intelligent generation of the rewards not only effectively reduces the artificial workload, but also can improve the fairness, objectivity, high efficiency and diversity of the establishment of the rewards.
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The foregoing and other objects, features and advantages of the present application will become more apparent from the following more particular description of embodiments of the present application, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
FIG. 1 is a flow chart of a method for intelligently generating a prize according to an exemplary embodiment of the present application.
Fig. 2 is a schematic structural diagram of a system for intelligently generating prizes according to an exemplary embodiment of the present application.
Fig. 3 is a block diagram of an electronic device according to an exemplary embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Exemplary method
FIG. 1 is a flow chart of a method for intelligently generating a prize according to an exemplary embodiment of the present application, as shown in FIG. 1, the method for intelligently generating a prize includes:
step 100: and acquiring dimension data for evaluating the prize of all individuals in the group in a preset time.
The individuals may be enterprise employees and are not limited to the individual categories set forth in the examples. For example, dimension data of individuals related to the prize scoring is collected in preset time, the prize scoring is related to the information of the individuals such as moral, intellectual, physical, aesthetic, labor and the like, and daily addition and subtraction score data of the individuals in dimension labels is collected. The preset time can be set within half a year, one year or one quarter according to specific requirements of the awards, and individual dimension data in the activity time are collected, so that the awards corresponding to the activity can be generated in a targeted manner. For example, the relevant evaluation dimension data of the individuals in the whole annual collective is acquired when the annual rewards need to be generated, and the relevant evaluation dimension data of the individuals in the whole annual collective is acquired when the quaternary rewards need to be generated. The dimension data may be divided into five dimensions, namely, de, zhi, body, mei and Law, and other dimension types, such as liveness, completion, etc., may be divided according to individual attributes related to the awards, and are not limited to the dimension types given in the examples. The dimensional data can be periodically collected, so that accuracy in evaluating the awards is improved.
Step 200: and carrying out weight analysis on each dimension data based on an entropy weight method to obtain a first weight ratio of the plurality of dimension data.
The entropy weight method (Entropy Weight Method) is a method for objectively assigning an index according to the size of the entropy of index information (dimension data), and the larger the information entropy is, the larger the degree of dispersion representing the index is, the more information is contained, and the larger the weight is assigned, so the entropy weight method focuses on the diversity of the values of variables. In addition, the process of calculating the weight by the entropy method needs to judge the information amount covered by each numerical value, can reflect the utility value of index information entropy, eliminates artificial factors, and avoids non objectivity caused by a subjective weighting method, so that the obtained result has fairness and objectivity by analyzing and evaluating the dimension data of an individual by the entropy weight method. And analyzing the weight of each dimension according to the weight calculation result by using an entropy weight method, and obtaining a weight analysis matrix through the weight calculation result, thereby obtaining a first weight duty ratio of a plurality of dimensions.
Step 300: and screening out individuals meeting preset judgment standards in the group according to the first weight duty ratio of the dimension data and the fuzzy evaluation method.
And screening out individuals meeting preset judgment standards in the group by utilizing a fuzzy evaluation algorithm (a fuzzy operator is a weighted average) according to the first weight duty ratio obtained by the entropy weight method. The fuzzy evaluation method (Fuzzy Evaluation Method) is a decision method based on fuzzy mathematical theory and is used for solving the problems of uncertainty and ambiguity. The method is suitable for multi-factor and multi-index decision problems, and can convert fuzzy judgment and evaluation into specific numerical calculation. The fuzzy evaluation method can also compare and sort the evaluation results for the first time to form a first fuzzy sorting, and can select individuals reaching a preset judgment standard from the results of the first fuzzy sorting according to the preset quantity or the preset score.
Step 400: and obtaining the ranking of the individuals reaching the preset judgment standard in the group based on a factor analysis method or a fuzzy evaluation method.
After the first sorting screening is carried out by the fuzzy evaluation method, if the screened individual data can be suitable for the factor analysis method, the comprehensive score and the ranking are obtained by the factor analysis method, and if the individual data is not suitable for the factor analysis method, the ranking by the fuzzy evaluation method is used as the final ranking.
Step 500: and sequentially generating rewards corresponding to the different dimension data according to the ranking and the second weight ratio of the dimension data of the individual reaching the preset judgment standard.
Wherein each prize is positively correlated with the second weight ratio.
After the individuals are comprehensively ranked, the score of each dimension is still displayed by each individual except the comprehensive score, so that special awards corresponding to each dimension can be generated besides the comprehensive awards, and when one dimension of the individuals meets the awards standard, the awards can be created according to the ranking of the individuals, so that the system is more flexible, efficient and personalized. For example, the reddish comprehensive rank is first, his "wisdom" score is highest for all individuals, then a prize about "wisdom" is created, the recommended winner is reddish, and the reddish comprehensive rank is third, but his "beauty" score is highest for all individuals, then a prize about "beauty" is created, and the recommended winner is undershot. In addition, the winning winner may be recommended to be reddish for creating the first prize of the overall ranking. The generated prize may be arbitrarily set or selected, and is not limited to the prizes given in the example.
In an embodiment, after the step 300, the method for intelligently generating the prize may further include: acquiring a collective attribute or an activity attribute; wherein, the collective attribute represents that the collective belongs to any dimension, and the activity attribute represents that the activity belongs to any dimension; and adjusting the weight ratio coefficient of the dimension data corresponding to the individual meeting the preset judgment standard according to the collective attribute or the activity attribute.
For example, dimensional data for the awards is classified into De, zhi, body, mei, law, and collective attributes may be classified into one of the categories according to the collective situation, and the collective is more focused on the body-related attributes, and the weight of the body is higher. The collective is more focused on the art related attributes, and the related weight of the art is higher. The collective is more focused on intellectual correlation properties, and the intellectual correlation weight is higher. A common collective coefficient without emphasis may be set to 1, while a personalized collective corresponding coefficient with emphasis is set to 1.1. The weight ratio coefficients of the dimension data corresponding to the individuals of different groups are adjusted, so that the prize can be pertinently evaluated, and the flexibility and the accuracy of finally generating the prize are further improved. Or, the collective pays a prize for the art activity, the academic activity or the sports activity, so that the weight ratio of the corresponding dimension data is improved based on the activity attribute, for example, the related weight ratio of the art activity is higher if the art activity belongs to the dimension of beauty, the related weight ratio of the sports activity belongs to the dimension of the body is higher, the related weight ratio of the body is higher if the academic activity belongs to the dimension of intelligence, the related weight ratio of intelligence is higher, the common activity coefficient is set to be 1, and the corresponding coefficient of the dimension data with the side weight ratio is set to be 1.1.
In an embodiment, after adjusting the weight ratio coefficient of the dimension data corresponding to the individual meeting the preset evaluation criterion according to the collective attribute, the method for intelligently generating the prize may include: multiplying dimension data corresponding to individuals meeting preset judgment standards by a weight ratio coefficient based on the dimension to which the collective attribute belongs or the dimension to which the activity attribute belongs to obtain final scores of the individuals meeting the preset judgment standards; the step 400 may include: and ranking the individuals reaching the preset evaluation standard in the group based on a factor analysis method or a fuzzy evaluation method according to the final scores of the individuals reaching the preset evaluation standard, and obtaining the ranking of the individuals reaching the preset evaluation standard in the group.
After the individuals which reach the preset judging standard are screened out according to the fuzzy evaluation method, according to the collective attribute, the individuals with the weighted individual groups or the individuals without the weighted common groups are multiplied by the corresponding weight duty ratio coefficient (for example, the weighted individual groups with the weighted individual groups are multiplied by 1.1 or the individuals without the weighted common groups are multiplied by 1), or according to the dimension to which the activity attribute belongs, the corresponding weighted dimension data in the individuals are multiplied by 1.1. And carrying out secondary ranking based on a factor analysis method or a fuzzy evaluation method according to the individual data of the multiplied weight duty ratio coefficient. If the screened individual data can be suitable for the factor analysis method, the comprehensive score and the ranking are obtained again by the factor analysis method, if the screened individual data is not suitable for the factor analysis method, the ranking by the fuzzy evaluation method is adopted, and the ranking is directly used as the final ranking after being multiplied by the weight ratio coefficient.
In an embodiment, before the step 400, the method for intelligently generating the prize may further include: acquiring individual data of individuals meeting preset judging standards in a group; sampling suitability test and Butt Li Qiuxing test are carried out on the individual data to obtain test results.
The sampling suitability test is also the KMO test (Kaiser-Meyer-Olkin test), which is a statistical method commonly used to verify the suitability of factor analysis. It is used to evaluate the suitability of the sample data to determine if it is suitable for factor analysis. The core idea of KMO testing is to measure the correlation and commonality between variables to determine if it is appropriate to generalize these variables to fewer potential factors. The KMO test results in a value between 0 and 1, indicating the degree of correlation between the variables. A higher KMO value (near 1) indicates a higher correlation between variables, suitable for factor analysis; whereas a lower KMO value (near 0) indicates a lower correlation between variables and is not suitable for factor analysis. The Bartlett Li Qiuxing test (Bartlett's test of sphericity) is a commonly used statistical test method for assessing the suitability of factor analysis. Its main purpose is to check if the correlation between variables is strong enough to support the generalization of these variables to fewer potential factors. Factor analysis is a statistical method used to study potential structures and relationships between variables. Sampling suitability testing and Bartlett sphere testing are important steps that help ensure the accuracy and reliability of factor analysis prior to performing the factor analysis.
In an embodiment, the step 400 may include: when the test result shows that the individual data is not suitable for the factor analysis method, obtaining a first ranking of the individuals meeting the preset judgment standard in the group through the fuzzy evaluation method; or when the test result indicates that the individual data is suitable for the factor analysis method, obtaining a second ranking of the individuals meeting the preset judgment standard in the group through the factor analysis method.
The KMO test results in a value between 0 and 1, indicating the degree of correlation between the variables. A higher KMO value (near 1) indicates a higher correlation between variables, suitable for factor analysis; whereas a lower KMO value (near 0) indicates a lower correlation between variables and is not suitable for factor analysis. If the Bartlett sphere test results are significant, i.e. the determinant of the correlation matrix is significantly unequal to zero, we can reject the original hypothesis, indicating that there is correlation between the variables, and that the factor analysis is appropriate, if the original hypothesis is not rejected, indicating that the variables may provide some information independently, not suitable for the factor analysis. Therefore, if the screened individual data passes the KMO test and the Bartlett sphere test and can be applied to the factor analysis method, the factor analysis method is used for obtaining comprehensive score and ranking, and if the factor analysis method is not suitable, the ranking of the fuzzy evaluation method is used as the final ranking.
In one embodiment, the step 300 includes: according to the first weight duty ratio of the plurality of dimension data, analyzing the plurality of dimension data respectively to obtain a factor weight vector; and screening out individuals meeting preset judgment standards in the group according to the factor weight vector and the fuzzy evaluation method.
In the evaluation work, the importance degree of each dimension factor is different, so that one weight a is given to each dimension factor according to the weight calculation result 1 The fuzzy set of weight sets for each factor (i.e., factor weight vector) may be represented by a: a= { undefinededa 1 , a 2 , ……, a n And the weight is obtained according to entropy weight analysis. Factor weight vectors are commonly used in multi-factor analysis or decision making to determine the importance or weight of different factors in an overall evaluation or decision.
In one embodiment, screening the individuals in the group that reach the preset evaluation criteria according to the factor weight vector and the fuzzy evaluation method includes: constructing a single factor judgment matrix according to a preset comment set and a preset factor set; the factor weight on the factor set is changed into the fuzzy vector on the comment set through fuzzy change; and determining individuals meeting preset judgment standards in the collective according to the fuzzy vectors.
When the fuzzy evaluation is performed, a factor set can be established firstly, wherein the factor set is a common set formed by taking various factors affecting an evaluation object as elements, and is generally represented by U, and U= { undefined U 1 ，u 2 ，……u n }, where element u i Representing the ith factor affecting the evaluation object. These factors, which generally have different degrees of ambiguity, are, for example, comprehensively evaluated from five aspects of Dewisdom, and thus constitute an evaluation index system set, i.e., a factor set, which can be described as U= { Deu 1 Zhi u 2 Body u 3 Meiu (Meiu) 4 Lauu (Laou) 5 }. Then a comment set is established, wherein the comment set is a set formed by various results possibly made by a comment person on the evaluation object and is generally represented by V, and V= { undefined V 1 ，v 2 ，……v n The elements V and j represent the j-th evaluation result and can be represented by different grades, comments or numbers according to the needs of actual conditions, for example, certain index of an individual in system data is divided into good and bad, if not participated, the index is divided into 0 and represents general, thus the individual can be recorded from three aspects of good, general and bad, and the index is recorded as V= { good V 1 General v 2 Difference v 3 }. The factor set and the comment set can be combined to generate a single factor comment matrix R, and if the membership degree of the ith element in the factor set U to the 1 st element in the comment set V is R i1 The result of the single factor evaluation on the ith element is represented by a fuzzy set as follows: r is R i = {undefinedr i1 , r i2 , ……, r im M single factor evaluation sets R 1 ，R 2 ，……，R n The matrix Rn ∗ m is composed for rows and is called a fuzzy comprehensive evaluation matrix. After the single factor judgment matrix R and the factor weight vector A are determined, the fuzzy vector A on U (namely the factor weight vector A) is changed into the fuzzy vector on V through fuzzy changeVector B, i.e. b=a 1*n * R n*m = {undefinedb 1 ，b 2 ，……，b m }. And finally screening out individuals meeting preset judgment standards. The screening condition may be the number or the score, for example, ten persons with the highest comprehensive score are selected from fifty persons as the individuals meeting the preset evaluation criterion, or the individuals meeting the preset score are selected as the individuals meeting the preset evaluation criterion, or the screening may be performed by combining a plurality of conditions.
In an embodiment, the step 500 may include: generating a prize item for the corresponding individual with respect to the first dimension data when the second weight ratio of the first dimension data of the individual, any one of which reaches the preset criterion, is highest based on the ranking from high to low; or generating a prize item for the second dimension data for the corresponding individual when the second weight ratio of the second dimension data of any one of the individuals reaching the preset criterion is highest based on the ranking from high to low.
For example, the purplish red is ranked first, and his "wisdom" score is highest for all individuals, then a prize is created for "wisdom", and the winner is recommended to be purplish red. The minds are ranked third in combination, but his score of "beauty" is highest for all individuals, then a prize is created for "beauty" and the winner is recommended to be the minds. That is, in addition to generating comprehensive rewards for individuals from the comprehensive ranking, various targeted rewards can be generated according to different dimensions, and characteristic rewards are also established for the single individual with one dimension being prominent, and real-time recommendation is performed according to the individual development characteristics and related ranking. A plurality of awards are generated through one piece of final data, so that the problems that the types of the traditional awards are too few and single and cannot be suitable for more individuals in a targeted manner are solved, and the creation and the use of the awards are more efficient and objective.
In one embodiment, the step 100 may include: triggering an acquisition instruction according to a scoring event required in a group; acquiring dimension data for evaluating the prize of all individuals in the group in preset time according to the acquisition instruction; wherein the preset time is positively correlated with a prize-scoring period of the prize-scoring event.
When a certain event or a certain activity needs to be awarded in a group, triggering an instruction for acquiring dimension data, wherein the acquired dimension data for awarding needs to be associated with the event or the activity, the association between the event or the activity and the dimension data can be determined according to a preset condition or in real time according to the activity, the dimension data for awarding in a preset time is acquired according to the awarding period of the event or the activity, for example, the quarter awarding is acquired, the dimension data for awarding in the last quarter is acquired, and the longer the awarding period is, the longer the acquired time span is, so that the validity and fairness of the dimension data are ensured.
Exemplary System
Fig. 2 is a schematic structural diagram of a system for intelligently generating a prize according to an exemplary embodiment of the present application, and as shown in fig. 2, a system 8 for intelligently generating a prize includes: an acquiring module 81, configured to acquire dimension data for evaluating a prize in a preset time for all individuals in the group; the analysis module 82 is configured to perform weight analysis on each dimension data based on an entropy weight method, so as to obtain a first weight ratio of the plurality of dimension data; the screening module 83 is configured to screen individuals in the group that reach a preset evaluation criterion according to the first weight ratio of the dimension data and the fuzzy evaluation method; a ranking module 84, configured to obtain ranks of individuals in the group that reach a preset criterion based on a factor analysis method or a fuzzy evaluation method; the generating module 85 is configured to sequentially generate awards corresponding to different dimension data according to the ranking and a second weight ratio of the dimension data of the individual reaching the preset evaluation criterion; wherein each prize is positively correlated with the second weight ratio.
According to the intelligent prize generation system provided by the embodiment, the dimension data of the individual in a period of time is obtained, the prizes are dynamically generated based on the fuzzy evaluation method and the factor analysis algorithm, the score, the weight and the multiple factor analysis of the individual in different dimensions in a period of time are combined, the prize standard is judged, the prizes are created, and the characteristic prizes can be generated according to the characteristics of the individual, so that the system has higher flexibility. The intelligent generation of the rewards not only effectively reduces the artificial workload, but also can improve the fairness, objectivity, high efficiency and diversity of the establishment of the rewards.
In an embodiment, the system 8 for intelligently generating prizes may also be configured to: acquiring a collective attribute or an activity attribute; wherein, the collective attribute represents that the collective belongs to any dimension, and the activity attribute represents that the activity belongs to any dimension; and adjusting the weight ratio coefficient of the dimension data corresponding to the individual meeting the preset judgment standard according to the collective attribute or the activity attribute.
In an embodiment, the system 8 for intelligently generating prizes may also be configured to: multiplying dimension data corresponding to individuals meeting preset judgment standards by a weight ratio coefficient based on the dimension to which the collective attribute belongs or the dimension to which the activity attribute belongs to obtain final scores of the individuals meeting the preset judgment standards; correspondingly, ranking module 84 may be configured to: and ranking the individuals reaching the preset evaluation standard in the group based on a factor analysis method or a fuzzy evaluation method according to the final scores of the individuals reaching the preset evaluation standard, and obtaining the ranking of the individuals reaching the preset evaluation standard in the group.
In an embodiment, the system 8 for intelligently generating prizes may also be configured to: acquiring individual data of individuals meeting preset judging standards in a group; sampling suitability test and Butt Li Qiuxing test are carried out on the individual data to obtain test results.
In one embodiment, ranking module 84 may be configured to: when the test result shows that the individual data is not suitable for the factor analysis method, obtaining a first ranking of the individuals meeting the preset judgment standard in the group through the fuzzy evaluation method; or when the test result indicates that the individual data is suitable for the factor analysis method, obtaining a second ranking of the individuals meeting the preset judgment standard in the group through the factor analysis method.
In an embodiment, the filtering module 83 may be configured to: according to the first weight duty ratio of the plurality of dimension data, analyzing the plurality of dimension data respectively to obtain a factor weight vector; and screening out individuals meeting preset judgment standards in the group according to the factor weight vector and the fuzzy evaluation method.
In an embodiment, the filtering module 83 may be configured to: constructing a single factor judgment matrix according to a preset comment set and a preset factor set; the factor weight on the factor set is changed into the fuzzy vector on the comment set through fuzzy change; and determining individuals meeting preset judgment standards in the collective according to the fuzzy vectors.
In an embodiment, the generating module 85 may be configured to: generating a prize item for the corresponding individual with respect to the first dimension data when the second weight ratio of the first dimension data of the individual, any one of which reaches the preset criterion, is highest based on the ranking from high to low; or generating a prize item for the second dimension data for the corresponding individual when the second weight ratio of the second dimension data of any one of the individuals reaching the preset criterion is highest based on the ranking from high to low.
In an embodiment, the obtaining module 81 may be configured to: triggering an acquisition instruction according to a scoring event required in a group; acquiring dimension data for evaluating the prize of all individuals in the group in preset time according to the acquisition instruction; wherein the preset time is positively correlated with a prize-scoring period of the prize-scoring event.
Exemplary electronic device
An electronic device, the electronic device comprising: a processor; a memory for storing processor-executable instructions; and the processor is used for executing the method for intelligently generating the prize according to the embodiment provided by the application.
Next, an electronic device according to an embodiment of the present application is described with reference to fig. 3. The electronic device may be either or both of the first device and the second device, or a stand-alone device independent thereof, which may communicate with the first device and the second device to receive the acquired input signals therefrom.
Fig. 3 illustrates a block diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 3, the electronic device 10 includes one or more processors 11 and a memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 11 to implement the methods of intelligently generating rewards and/or other desired functions of the various embodiments of the present application described above. Various contents such as an input signal, a signal component, a noise component, and the like may also be stored in the computer-readable storage medium.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
When the electronic device is a stand-alone device, the input means 13 may be a communication network connector for receiving the acquired input signals from the first device and the second device.
In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.
The output device 14 may output various information to the outside, including the determined distance information, direction information, and the like. The output means 14 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 10 that are relevant to the present application are shown in fig. 3 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
The computer program product may write program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
A computer readable storage medium storing a computer program for executing the method for intelligently generating a prize according to the embodiments provided herein.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (10)
1. A method for intelligently generating a prize, comprising:
acquiring dimension data for evaluating the prize of all individuals in a group in preset time;
based on an entropy weight method, carrying out weight analysis on each dimension data to obtain a first weight ratio of a plurality of dimension data;
screening out individuals meeting preset judgment standards in a group according to the first weight ratio of the dimension data and a fuzzy evaluation method;
obtaining the ranking of individuals meeting preset judgment standards in a group based on a factor analysis method or a fuzzy evaluation method;
sequentially generating rewards corresponding to different dimension data according to the ranking and a second weight ratio of the dimension data of the individual reaching a preset judgment standard; wherein each of the awards is positively correlated with the second weight ratio.
2. The method of claim 1, wherein after screening out individuals in a group that meet a preset criteria according to a first weight ratio of the dimensional data and a fuzzy evaluation method, the method of intelligently generating a prize further comprises:
acquiring a collective attribute or an activity attribute; wherein the collective attribute indicates that the collective belongs to any dimension, and the activity attribute indicates that the activity belongs to any dimension;
and adjusting the weight ratio coefficient of the dimension data corresponding to the individual reaching the preset judgment standard according to the collective attribute or the activity attribute.
3. The method for intelligently generating a prize according to claim 2, wherein after the weight ratio coefficient of the dimension data corresponding to the individual meeting a preset criterion is adjusted according to the collective attribute or the activity attribute, the method for intelligently generating a prize comprises:
multiplying the dimension data corresponding to the individuals reaching the preset judgment standard by the weight ratio coefficient based on the dimension to which the collective attribute belongs or the dimension to which the activity attribute belongs to obtain the final score of the individuals reaching the preset judgment standard;
the ranking of the individuals reaching the preset judgment standard in the group is obtained based on the factor analysis method or the fuzzy evaluation method, and the ranking comprises the following steps:
and ranking the individuals reaching the preset evaluation standard in the group based on a factor analysis method or a fuzzy evaluation method according to the final scores of the individuals reaching the preset evaluation standard, and obtaining the ranking of the individuals reaching the preset evaluation standard in the group.
4. The method of intelligently generating prizes according to claim 1, wherein before ranking of individuals in a group that meet a preset criterion is obtained based on a factor analysis method or a fuzzy evaluation method, the method of intelligently generating prizes further comprises:
acquiring individual data of individuals meeting preset judging standards in a group;
sampling suitability test and bat Li Qiuxing test are carried out on the individual data to obtain test results.
5. The method for intelligently generating prizes according to claim 4, wherein obtaining the ranking of individuals in the group that meet the preset evaluation criteria based on a factor analysis method or a fuzzy evaluation method comprises:
when the test result indicates that the individual data is not suitable for the factor analysis method, obtaining a first ranking of individuals meeting preset judgment standards in a group through the fuzzy evaluation method; or (b)
And when the test result indicates that the individual data is suitable for the factor analysis method, obtaining a second ranking of the individuals meeting the preset judgment standard in the group through the factor analysis method.
6. The method for intelligently generating a prize according to claim 1, wherein screening out individuals meeting a preset evaluation criterion in a group according to the first weight ratio of the dimensional data and the fuzzy evaluation method comprises:
according to the first weight duty ratio of the plurality of dimension data, analyzing the plurality of dimension data respectively to obtain a factor weight vector;
and screening out individuals meeting preset judgment standards in a group according to the factor weight vector and the fuzzy evaluation method.
7. The method for intelligently generating a prize according to claim 6, wherein screening out individuals meeting a preset evaluation criterion in a group according to the factor weight vector and the fuzzy evaluation method comprises:
constructing a single factor judgment matrix according to a preset comment set and a preset factor set;
changing the factor weight on the factor set into a fuzzy vector on the comment set through fuzzy change;
and determining individuals meeting preset judgment standards in the collective according to the fuzzy vectors.
8. The method of claim 1, wherein generating the awards corresponding to different ones of the dimensional data sequentially according to the ranking and a second weight ratio of the dimensional data of individuals meeting a preset criteria comprises:
generating a prize item for the corresponding individual according to the first dimension data when the second weight ratio of the first dimension data of the individual, any one of which reaches a preset judgment standard, is highest based on the ranking from high to low; or (b)
And generating a prize item related to the second dimension data for the corresponding individual when the second weight ratio of the second dimension data of the individual, any one of which reaches the preset judgment standard, is highest based on the ranking from high to low.
9. The method for intelligently generating a prize according to claim 1, wherein acquiring dimensional data for a prize evaluation of all individuals in a group within a preset time comprises:
triggering an acquisition instruction according to a scoring event required in a group;
acquiring dimension data for evaluating the prize of all individuals in the group in preset time according to the acquisition instruction; wherein the preset time is positively correlated with a prize evaluation period of the prize evaluation event.
10. A system for intelligently generating prizes, comprising:
the acquisition module is used for acquiring dimension data for evaluating the prize of all individuals in the group in a preset time;
the analysis module is used for carrying out weight analysis on each piece of dimension data based on an entropy weight method to obtain first weight duty ratios of a plurality of pieces of dimension data;
the screening module is used for screening out individuals meeting preset judgment standards in a group according to the first weight ratio of the dimension data and the fuzzy evaluation method;
the ranking module is used for obtaining the ranking of the individuals reaching the preset judgment standard in the group based on a factor analysis method or a fuzzy evaluation method;
the generating module is used for sequentially generating rewards corresponding to different dimension data according to the ranking and the second weight ratio of the dimension data of the individual reaching a preset judgment standard; wherein each of the awards is positively correlated with the second weight ratio.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105469208A (en) * | 2015-11-24 | 2016-04-06 | 江苏省电力公司南通供电公司 | Employee training evaluation system based on fuzzy integrated evaluation method |
| CN106373055A (en) * | 2016-08-31 | 2017-02-01 | 武汉颂大教育科技股份有限公司 | Teaching quality assessment system based on big data |
| CN109214660A (en) * | 2018-08-14 | 2019-01-15 | 国网湖北省电力有限公司孝感供电公司 | Meter supplier performance evaluation method and system based on big data analysis |
| CN110070472A (en) * | 2019-04-22 | 2019-07-30 | 重庆多创电子技术有限公司 | A kind of comprehensive quality of students methods of marking |
| CN110491490A (en) * | 2019-07-11 | 2019-11-22 | 深圳市翩翩科技有限公司 | A kind of doctor's appraisal procedure and device |
-
2024
- 2024-03-04 CN CN202410239680.6A patent/CN117852976A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105469208A (en) * | 2015-11-24 | 2016-04-06 | 江苏省电力公司南通供电公司 | Employee training evaluation system based on fuzzy integrated evaluation method |
| CN106373055A (en) * | 2016-08-31 | 2017-02-01 | 武汉颂大教育科技股份有限公司 | Teaching quality assessment system based on big data |
| CN109214660A (en) * | 2018-08-14 | 2019-01-15 | 国网湖北省电力有限公司孝感供电公司 | Meter supplier performance evaluation method and system based on big data analysis |
| CN110070472A (en) * | 2019-04-22 | 2019-07-30 | 重庆多创电子技术有限公司 | A kind of comprehensive quality of students methods of marking |
| CN110491490A (en) * | 2019-07-11 | 2019-11-22 | 深圳市翩翩科技有限公司 | A kind of doctor's appraisal procedure and device |
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