[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0047] The invention is applicable to numerous general purpose or special purpose computing device environments or configurations. For example: personal computer, server computer, handheld or portable device, tablet type device, multiprocessor device, distributed computing environment including any of the above devices or devices, etc.
[0048] An embodiment of the present invention provides a data processing method, which can be applied to a processor of a computer or a terminal, and the processor executes the method, figure 1 shows the method flowchart of the data processing method provided by the embodiment of the present invention, as figure 1 shown, including:
[0049] S101: Collect a plurality of different information categories corresponding to the target object in the current processing cycle, and each information category contains a plurality of information data;
[0050] In the method provided by the embodiment of the present invention, for each data processing cycle, a plurality of different information categories of the target object are collected, and each information category contains a plurality of information data, and the target object can be an employee of an enterprise, and the corresponding The information category can be the information indicators of the employees in the process of working in the enterprise, such as the daily working hours, and the information data in the information category can be the personal information of the target object in a specific scene.
[0051]S102: Perform normalization processing on each information data in each of the information categories to obtain a plurality of relative data corresponding to each of the information categories;
[0052] In the method provided by the embodiment of the present invention, the categories in each information class are different, and there is no unified measurement standard for the information data contained in each information class. In the method provided by the embodiment of the present invention, the information data in each information class Each information data is normalized to obtain a plurality of relative data corresponding to each information category, and a unified measurement standard for each information data in each information category originally collected.
[0053] S103: Convert each relative data corresponding to all the information classes into the same standard interval, and determine each interval value corresponding to each information class in the standard interval;
[0054] In the method provided by the embodiment of the present invention, the units of measurement of the relative data corresponding to different information categories are different. In the embodiment of the present invention, by standardizing the relative data corresponding to all information categories, each relative data corresponding to all information categories The data is converted into the same standard interval, and the unit of measurement of each relative data is the same.
[0055] S104: Calculate the information entropy of each interval value corresponding to each information class in the standard interval;
[0056] In the method provided by the embodiment of the present invention, the relative data corresponding to each information class is converted into the standard interval, and each information class corresponds to multiple interval values in the standard interval. In the embodiment of the present invention, each information A class corresponds to a set of interval values, and the information entropy of a set of interval values is calculated to determine the information entropy corresponding to each information class.
[0057] S105: Calculate the data score of the target object in the current processing cycle according to the information entropy corresponding to each of the information categories.
[0058] In the method provided by the embodiment of the present invention, the data score of the target object in the current processing cycle is calculated according to the information entropy corresponding to each information category, so as to determine the data scores of all information categories corresponding to the target object in the data processing process, Using this data score as a measurement standard, the performance of the target object in the performance process can be evaluated.
[0059] The data processing method provided by the embodiment of the present invention, in the specific application process, collects a variety of different information categories of the target object, performs normalization processing on the information data in each information category, and obtains the information corresponding to each information category. a plurality of relative data, and then convert each relative data corresponding to all the information classes into the same standard interval, and determine each interval value corresponding to each information class in the standard interval; thus the multiple The information data in different information categories are converted into the same standard interval, and then the corresponding data score of the target object in the current processing cycle is determined by calculating the information entropy. Using this data score as a measurement standard, the performance of the target object can be evaluated. to evaluate. The data processing method provided by the embodiment of the present invention can convert the information data in a variety of different information categories corresponding to the target object into the same standard interval, enrich the data source for the performance evaluation process of the target object, and provide various types of evaluation data Comprehensive, improving the accuracy of performance evaluation of target objects.
[0060] The data processing method provided by the embodiment of the present invention can convert the data of various different units into the same standard interval, and realize the unified measurement standard of various different data. In specific applications, the data in the scene where the target object is located can be A variety of data are used as reference data to score the performance of the target object in the scene, the evaluation basis for the target object is more sufficient, and the accuracy of performance evaluation is higher.
[0061] In the data processing method provided by the embodiment of the present invention, the specific process of calculating the data score of the target object in the current processing cycle according to the information entropy of each of the information categories includes:
[0062] calculating the weight of each information class according to the information entropy corresponding to each of the information classes;
[0063] The data score of the target object in the current processing cycle is calculated in combination with the weight of each information class and each interval value corresponding to each information class in the standard interval.
[0064] In the method provided by the embodiment of the present invention, different information categories have different influences on the target object. In the embodiment of the present invention, the weight of each information category is calculated according to the information entropy corresponding to each information category. Since the interval values corresponding to each information class have the same evaluation standard, in the specific calculation process, combined with the weight of each information class, the data score corresponding to the target object can be calculated.
[0065] In the data processing method provided by the embodiment of the present invention, the specific process of calculating the weight of each information class according to the information entropy corresponding to each information class includes:
[0066] performing a summation operation on the information entropy corresponding to each of the information categories to obtain the sum of each of the information entropies;
[0067] Substituting the information entropy corresponding to each information class and the sum of the information entropy into a preset weight calculation formula to obtain the weight of each information class.
[0068] In the data processing method provided by the embodiment of the present invention, a weight calculation formula is preset, and the information entropy corresponding to each information class and the synthesis of the information entropy are substituted into the weight calculation formula to obtain the weight of each information class,
[0069] In the data processing method provided by the embodiment of the present invention, the weight of each information class and each interval value corresponding to each information class in the standard interval are combined to calculate the current processing period of the target object The specific process of scoring data includes:
[0070] Respectively performing a product operation on the weight of each information class and the corresponding interval value of the information class in the standard interval to obtain the corresponding data score of the target object in each of the information classes;
[0071] The data scores corresponding to the target object in each information category are summed to obtain the total data score of the target object in the current processing cycle.
[0072] When the method provided by the embodiment of the present invention is applied to the performance evaluation of enterprise employees, after processing the information data corresponding to each information category, the comprehensive performance score of each employee can be calculated, and the comprehensive performance score is the employee's The sum of the corresponding weighted interval values in each information category. In the embodiment of the present invention, employee performance is evaluated according to the comprehensive performance score.
[0073] When the method provided by the embodiment of the present invention is used to evaluate the performance of enterprise employees, each data processing cycle corresponds to an evaluation cycle, and in each evaluation cycle, the information of enterprise employees is collected, and the collected information can be Including four categories, attendance data indicators, work efficiency indicators, rewards and punishment indicators and leadership comprehensive indicators.
[0074] In the method provided by the embodiment of the present invention, the attendance data indicators may include: working hours, overtime hours, weekend working days, late days, early leave days, absenteeism days, etc.
[0075] Work efficiency indicators can include: email reply efficiency, transaction application processing efficiency, number of articles published, number of likes on articles, etc.
[0076] Reward and punishment indicators may include: invention innovation, reward information, punishment information, etc.
[0077] The leadership comprehensive index may include: the leader's rating of the employee.
[0078] refer to figure 2 , which shows in the embodiment of the present invention, the specific process of performing normalization processing on each information data in each of the information categories to obtain a plurality of relative data corresponding to each of the information categories, including:
[0079] calculating the mean and standard deviation of each information data in each of said information categories;
[0080] Obtaining the difference between each of the information data and the average value; and using the quotient of the difference and the standard deviation as the relative data of the information data.
[0081] In the method provided by the embodiment of the present invention, each information category contains a plurality of information data. For example, the company has N employees, and the overtime hours of the employees are included in the multiple different information categories collected in the current evaluation cycle. The information category corresponding to the overtime hours includes the overtime hours of N employees. The overtime hours of each employee are different, and there is no uniform standard. Therefore, the information data in each information category needs to be normalized.
[0082] In the embodiment of the present invention, the formula Z=(X-Xbar)/S is used to normalize each information data, wherein Z represents the relative data corresponding to each information data, X represents the data value corresponding to each information data, Xbar is the average value of each information data in each information class, and S is the standard deviation of each information data in each information class.
[0083] Taking the overtime hours as an example, the overtime hours of employees A, B, and C are 8 hours, 4 hours, and 2 hours respectively, and the information data contained in the information category - overtime hours are (8 hours, 4 hours, 2 hours).
[0084] Calculate the average of the overtime hours of A, B, and C to be 4.7 hours, and the standard deviation is S. In the information category - overtime hours, the relative data of A's corresponding information data is (8-4.7)/S.
[0085] In the embodiment of the present invention, for employee A, 8 hours of information data in information category-overtime hours can be used as employee A's initial score in information category-overtime hours.
[0086] refer to image 3 , which shows the specific process of standardizing the relative data corresponding to each of the information categories and converting the relative data corresponding to all the information categories into the same standard interval in the embodiment of the present invention, including:
[0087] Obtaining the maximum value and the minimum value in each relative data corresponding to each information class, and determining a first difference, the first difference being the difference between the maximum value and the minimum value;
[0088] determining the difference between each relative data and the minimum value, and dividing the difference between the relative data and the minimum value by the first difference to obtain an interval value corresponding to the relative data, to convert the relative data into the standard interval.
[0089] In the method provided by the embodiments of the present invention, the measurement units of the information data in each information category are different. For example: Assuming that the company includes three employees, A, B, and C, the information data contained in the information category - overtime hours are (8 hours, 4 hours, 2 hours), the information category - the number of days late (1 day, 2 days, 3 days), information The relative data corresponding to category-overtime hours and the relative data corresponding to information category-late days are not the same unit of measurement. Therefore, in the embodiment of the present invention, the relative data corresponding to each information category is converted into the same standard interval for unified calculation standard.
[0090] In the embodiment of the present invention, on the basis of obtaining relative data through normalization, it is assumed that K information classes X are given 1 , X 2 ,...,X k , where X i ={x 1 , x 2 ,...,x n}, assuming that after standardization, the corresponding interval values are Y 1 , Y 2 ,...,Y k , where X i middle x 1 … x n is the relative data calculated by the formula Z=(X-Xbar)/S.
[0091]Then the interval value corresponding to each relative data is the difference between the relative data and the minimum value in each relative data, and the quotient of the first difference, and the first difference is the difference between the maximum value and the minimum value in each relative data . The corresponding calculation formula is:
[0092]
[0093] Among them, X ij is the current relative data, min(X i ) is the minimum value in each relative data, max (X i ) is the maximum value in each relative data.
[0094] The Y ij It is the interval value in the standard interval after the relative data is calculated, that is, the standard score is obtained after the data is standardized.
[0095] In the method provided by the embodiment of the present invention, the specific process of calculating the information entropy of each interval value corresponding to each information class in the standard interval includes:
[0096] Each interval value corresponding to each information class is substituted into a preset information entropy calculation formula, and the information entropy of each information class is obtained through calculation.
[0097] In the method provided by the embodiment of the present invention, an appropriate information entropy calculation formula can be selected according to the actual operation process, and the information entropy calculation can be performed on each interval value in the standard interval corresponding to each information class to obtain the information corresponding to each information class. information entropy.
[0098] In the method provided by the embodiment of the present invention, the difference between the data generated by employees in the enterprise every year is found out through the way of information entropy, and different evaluation points and different evaluation indicators are used to conduct a comprehensive analysis of the data used by employees in the enterprise. Azimuth objective assessment.
[0099] In the method provided by the embodiment of the present invention, various information entropy calculation formulas can be used to calculate the information entropy. Preferably, the formula is used in the embodiment of the present invention:
[0100]
[0101] Among them, E j Represents information entropy, where,
[0102] when p ij = 0, then define
[0103] where Y ij to apply the formula
[0104]
[0105] The standard score of the information indicator obtained. where n is a positive integer.
[0106] In the embodiment of the present invention, in the process of calculating the weight of each information class according to the information entropy corresponding to each information class, the information entropy of each information class is summed to obtain the information entropy of each information class The sum of the information entropy of each information class and the sum of the information entropy are substituted into a preset weight calculation formula to obtain the weight of each information class.
[0107] In the embodiment of the present invention, the weight of each information class is calculated according to the difference of each information class. For example, if everyone works 8.5 hours a day, everyone is the same, so the people who work 8.5 hours normally score Basically the same, you can’t see any difference. If someone worked overtime on weekends and someone didn’t work overtime, in this way, those who work overtime will get some points. Similarly, people who have normal attendance records will get points for being late and leaving early. value will be relatively high. In the embodiment of the present invention, a preset information entropy calculation formula is used to calculate the weight of each information category.
[0108] In the method provided by the embodiment of the present invention, on the basis of the information entropy formula, the weight of each information class can be calculated, and the calculation formula of the weight is preferably:
[0109]
[0110] Among them, W i Indicates the weight of the information class. E. i In order to obtain the information entropy of each information class by using the information entropy formula provided by the embodiment of the present invention, the information entropy of each information class is substituted into the weight formula to obtain the weight of each information class.
[0111] In the process of evaluating employee performance, in the method provided by the embodiment of the present invention, the weight of each information category and the corresponding interval values of each information category in the standard interval are used to calculate the weight of each information category. The specific process of comprehensive performance score of each employee, including:
[0112] The weight of each information class is multiplied by the interval value of the employee corresponding to the information class in the standard interval to obtain the corresponding performance score of the employee in the information class;
[0113] The employee's performance score corresponding to each information category is summed to obtain the employee's comprehensive performance score in the current evaluation cycle.
[0114] In the method provided by the embodiment of the present invention, in the process of calculating the comprehensive score, the formula can be used:
[0115] where X li is the standard score corresponding to each information category, W i is the weight of each information category, after the summing operation, the sum of the scores corresponding to each information category is taken as the total score of employee performance.
[0116] In the method provided in the embodiment of the present invention, according to the final score of each employee, the weight ratio of each index can be obtained, and the enterprise can fine-tune the weights of different indexes according to its own actual situation, or it can be based on each A person's score provides a comprehensive assessment of the employee.
[0117] The method provided by the embodiment of the present invention uses massive data to comprehensively evaluate the employees of the enterprise, starting from the data of the relationship between the employees and the enterprise, and objectively evaluates, so that the performance appraisal of the employees can be open, fair and fair, and each employee can pass the data To check your performance or evaluation results, you can also adjust your work status according to the data, and bring practical results to the company's year-end evaluation.
[0118] and figure 1 Corresponding to the data processing method shown, the embodiment of the present invention also provides a data processing device for processing figure 1 The specific implementation of the data processing method in the present invention, the data processing device of the embodiment of the present invention can be applied in the processor of the computer or the terminal, and its structural diagram is as follows Figure 4 shown, including:
[0119] The collection unit 401 is configured to collect a plurality of different information categories corresponding to the target object in the current processing cycle, and each information category contains a plurality of information data;
[0120] A processing unit 402, configured to perform normalization processing on each information data in each of the information categories, and obtain a plurality of relative data corresponding to each of the information categories;
[0121] A determining unit 403, configured to convert each relative data corresponding to all the information classes into the same standard interval, and determine each interval value corresponding to each information class in the standard interval;
[0122] The first calculation unit 404 is configured to calculate the information entropy of each interval value corresponding to each of the information types in the standard interval;
[0123] The second calculation unit 405 is configured to calculate the data score of the target object in the current processing cycle according to the information entropy corresponding to each of the information categories.
[0124] The data processing device provided by the embodiment of the present invention collects a variety of different information categories of the target object, performs normalization processing on the information data in each information category, obtains a plurality of relative data corresponding to each information category, and then The respective relative data corresponding to all the information classes are converted into the same standard interval, and the respective interval values corresponding to each of the information classes in the standard interval are determined; thus the information data in a plurality of different information classes of the target object are converted Convert to the same standard interval, and then calculate the information entropy to determine the corresponding data score of the target object in the current processing cycle, and use the data score as a measurement standard to evaluate the performance of the target object. The data processing device provided by the embodiment of the present invention can convert the information data of various information types corresponding to the target object into the same standard interval, enrich the data source for the performance evaluation process of the target object, and provide various types of evaluation data Comprehensive, improving the accuracy of performance evaluation of target objects.
[0125] An embodiment of the present invention also provides a storage medium, the storage medium includes a stored program, wherein, when the program is running, the device where the storage medium is located is controlled to execute the above data processing method, and the method specifically includes:
[0126] A data processing method, comprising:
[0127] Collect multiple different information categories corresponding to the target object in the current processing cycle, and each information category contains multiple information data;
[0128] Perform normalization processing on each information data in each of the information categories to obtain a plurality of relative data corresponding to each of the information categories;
[0129] converting each relative data corresponding to all the information classes into the same standard interval, and determining each interval value corresponding to each information class in the standard interval;
[0130] Calculating the information entropy of each interval value corresponding to each of the information classes in the standard interval;
[0131] According to the information entropy corresponding to each of the information categories, the data score of the target object in the current processing cycle is calculated.
[0132] In the above method, optionally, performing normalization processing on each information data in each of the information categories to obtain a plurality of relative data corresponding to each of the information categories, including:
[0133] calculating the mean and standard deviation of each information data in each of said information categories;
[0134] Obtaining the difference between each of the information data and the average value; and using the quotient of the difference and the standard deviation as the relative data of the information data.
[0135] In the above method, optionally, converting the relative data corresponding to all the information types into the same standard interval includes:
[0136] Obtaining the maximum value and the minimum value in each relative data corresponding to each information class, and determining a first difference, the first difference being the difference between the maximum value and the minimum value;
[0137] determining the difference between each of the relative data and the minimum value, and dividing the difference between the relative data and the minimum value by the first difference to obtain an interval value corresponding to the relative data , to convert the relative data into the standard interval.
[0138] In the above method, optionally, calculating the information entropy of each interval value corresponding to each information class in the standard interval includes:
[0139] Call the preset information entropy calculation formula;
[0140] Each interval value corresponding to each information class in the standard interval is substituted into the preset information entropy calculation formula, and the information entropy corresponding to each information class is obtained through calculation.
[0141]In the above method, optionally, calculating the data score of the target object in the current processing cycle according to the information entropy of each information category includes:
[0142] calculating the weight of each information class according to the information entropy corresponding to each of the information classes;
[0143] The data score of the target object in the current processing cycle is calculated in combination with the weight of each information class and each interval value corresponding to each information class in the standard interval.
[0144] In the above method, optionally, the calculation of the weight of each information class according to the information entropy corresponding to each of the information classes includes:
[0145] performing a summation operation on the information entropy corresponding to each of the information categories to obtain the sum of each of the information entropies;
[0146] Substituting the information entropy corresponding to each information class and the sum of the information entropy into a preset weight calculation formula to obtain the weight of each information class.
[0147] In the above method, optionally, the data score of the target object in the current processing cycle is calculated in combination with the weight of each information class and each interval value corresponding to each information class in the standard interval ,include:
[0148] Respectively performing a product operation on the weight of each information class and the corresponding interval value of the information class in the standard interval to obtain the corresponding data score of the target object in each of the information classes;
[0149] The data scores corresponding to the target object in each information category are summed to obtain the total data score of the target object in the current processing cycle.
[0150] The embodiment of the present invention also provides an electronic device, the schematic diagram of which is shown in Figure 5 As shown, it specifically includes a memory 501, and one or more programs 502, wherein one or more programs 502 are stored in the memory 501, and are configured to be executed by one or more processors 503 502 contains instructions for:
[0151] Collect multiple different information categories corresponding to the target object in the current processing cycle, and each information category contains multiple information data;
[0152] Perform normalization processing on each information data in each of the information categories to obtain a plurality of relative data corresponding to each of the information categories;
[0153] converting each relative data corresponding to all the information classes into the same standard interval, and determining each interval value corresponding to each information class in the standard interval;
[0154] Calculating the information entropy of each interval value corresponding to each of the information classes in the standard interval;
[0155] According to the information entropy corresponding to each of the information categories, the data score of the target object in the current processing cycle is calculated.
[0156] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. For the same and similar parts in each embodiment, refer to each other, that is, Can. As for the device-type embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for related parts, please refer to part of the description of the method embodiments.
[0157] Finally, it should also be noted that in this text, relational terms such as first and second etc. are only used to distinguish one entity or operation from another, and do not necessarily require or imply that these entities or operations, any such actual relationship or order exists. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.
[0158] For the convenience of description, when describing the above devices, functions are divided into various units and described separately. Of course, when implementing the present invention, the functions of each unit can be implemented in one or more pieces of software and/or hardware.
[0159] It can be seen from the above description of the implementation manners that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in storage media, such as ROM/RAM, disk , CD, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments of the present invention.
[0160] A data processing method and device provided by the present invention have been introduced in detail above. In this paper, specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only used to help understand the method of the present invention. and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation and scope of application. limits.
