CN117611011A - Data processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a data processing method, a device, electronic equipment and a storage medium, which concretely comprise the following steps: acquiring data to be processed, and determining a sample set and a test set based on the data to be processed; acquiring a fuzzy set of data variables of a sample set, and determining a membership function of the fuzzy set; obtaining a plurality of preset grid area numbers, carrying out area division on a sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and a membership function of a fuzzy set; performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; and determining a target data evaluation model based on the test error. The embodiment of the invention can utilize semantic blurring and grid parameter fitting methods to prepare an interpretable data evaluation model, and improves the scientificity, rationality and usability of the whole evaluation system.
Description
Technical Field
The present invention relates to the field of data analysis technologies, and in particular, to a data processing method, a data processing device, an electronic device, and a storage medium.
Background
Today, data-driven decision making, efficient talent evaluation has important significance for enterprises to promote core competitiveness and achieve strategic development goals. Scientific talent evaluation rules are also the precondition for the effective development of talent resources and the basis for developing talent works.
In the prior art, according to subjective experience of experts in related industries or enterprises, assessment indexes are manually selected, and each index weight is distributed to determine a data evaluation model. However, the human determination of the data evaluation model is affected by subjectivity of the constructor, and cannot obtain an optimal decision.
Disclosure of Invention
The invention provides a data processing method, a device, electronic equipment and a storage medium, which utilize semantic blurring and grid parameter fitting methods to prepare an interpretable data evaluation model and improve the scientificity, rationality and usability of an overall evaluation system.
According to an aspect of the present invention, there is provided a data processing method, comprising:
acquiring data to be processed, and determining a sample set and a test set based on the data to be processed; wherein the data to be processed comprises a plurality of data variables;
acquiring a fuzzy set of data variables of a sample set, and determining a membership function of the fuzzy set;
Obtaining a plurality of preset grid area numbers, carrying out area division on a sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and a membership function of a fuzzy set;
performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; and determining a target data evaluation model based on the test error.
According to another aspect of the present invention, there is provided a data processing apparatus characterized by comprising:
the data acquisition module is used for acquiring data to be processed and determining a sample set and a test set based on the data to be processed; wherein the data to be processed comprises a plurality of data variables;
the membership function determining module is used for acquiring a fuzzy set of data variables of the sample set and determining membership functions of the fuzzy set;
the data evaluation model determining module is used for obtaining a plurality of preset grid area numbers, carrying out area division on the sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and membership functions of the fuzzy set;
the target data evaluation model determining module is used for carrying out error test on the data evaluation models of the plurality of grid area numbers based on the test set to obtain test errors; and determining a target data evaluation model based on the test error.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method of any one of the embodiments of the present invention.
According to another aspect of the present invention there is provided a computer readable storage medium storing computer instructions for causing a processor to perform a data processing method of any of the embodiments of the present invention.
According to the technical scheme, the sample set and the test set are determined based on the data to be processed by acquiring the data to be processed; acquiring a fuzzy set of data variables of a sample set, and determining a membership function of the fuzzy set; obtaining a plurality of preset grid area numbers, carrying out area division on a sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and a membership function of a fuzzy set; performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; and determining a target data evaluation model based on the test error, combining data and expert experience, and preparing an interpretable data evaluation model, so that the problem that an optimal decision cannot be obtained only by evaluating subjective experience of a manager is solved, and the scientificity, rationality and usability of the whole evaluation system are improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data processing method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a data processing method according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a data processing apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a data processing method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention, where the method is applicable to human resource assessment, and the method may be performed by a data processing device, where the data processing device may be implemented in hardware and/or software, and the data processing device may be configured in an electronic device such as a mobile phone, a computer, a server, etc. As shown in fig. 1, the method includes:
S110, acquiring data to be processed, and determining a sample set and a test set based on the data to be processed; wherein the data to be processed includes a plurality of data variables.
In the present embodiment, the data to be processed d= (X) 1 ,…,X i ,…,X p ，Y 1 ,…,Y j ,…Y q ) T Comprising a plurality of data, each data comprising p+q data items of one sample, wherein X i Data of the ith self-variable data item of each sample, Y j For the data of the j-th result variable data item of each sample, it can be understood that the data to be processed can be obtained according to application scenes, and the types of the data to be processed corresponding to different assessment scenes can be obtainedDifferent from each other. The data type in the data to be processed corresponding to the application scene may be preset.
The data to be processed is preprocessed due to the fact that data types are inconsistent or data ranges are inconsistent, wherein preprocessing comprises, but is not limited to, data cleaning, integrated processing, normalization processing, data transformation and the like. Data cleansing may include correcting format errors, filtering data with missing or invalid data items, and filtering data with data item associations that are not common sense, ensuring validity of the data. The integration process may include merging pieces of sample data in multiple tables and merging data items of samples in different tables. The normalization process can adopt methods such as linear function normalization or Z-value normalization. Data transformation is the transformation of one or more original data variables into a new variable by some functional relationship.
The sample set is a data set obtained by sampling the data to be processed and is used for determining a data evaluation model, and the number of the sample set can be one or more; the test set is a set formed by data which are not sampled in the data to be processed, and is used for carrying out error verification on a data evaluation model obtained according to the sample set.
Taking the construction of a hospital management evaluation system as an example, the data to be processed can be acquired from a hospital information system and can comprise the following data variables: number of topics X 1 Quantity X of papers 2 Number of achievements prize X 3 Number of patents X 4 Scientific research score Y 1 And carrying out integrated processing and data cleaning on the data to obtain data to be processed. The data to be processed is randomly equally divided into K groups, where the data of K-1 groups is used as training set, leaving one group as test set, for example, k=5.
S120, acquiring a fuzzy set of data variables of the sample set, and determining a membership function of the fuzzy set.
In this embodiment, the fuzzy set is a set for expressing fuzzy semantic form types, and the semantic form types may be many, etc. Membership functions are relationship functions that map data variables to fuzzy sets, such as trigonometric functions, trapezoidal functions, gaussian functions, etc., table 1 lists common membership functions:
TABLE 1
Specifically, a plurality of fuzzy sets are set for each data variable based on semantic form types according to application scenes, membership function types of the fuzzy sets are selected, and membership function parameter values of the fuzzy sets are determined through data fitting. The data fitting method can adopt a least square method, an expected variance matching method and the like. Taking an expected variance matching method as an example, firstly, sorting the size of data in each data variable of data in a sample set, extracting the data variables at the corresponding positions of each fuzzy set to form a data subset of each data variable according to the semantic form relation among each fuzzy set of each data variable, and estimating membership function parameter values by calculating the average value and variance of the data subset of each data variable to obtain membership functions of each fuzzy set.
Optionally, the fuzzy set may be determined by: and acquiring fuzzy classification labels of the data variables in the sample set, and determining fuzzy sets of the data variables based on the fuzzy classification labels of the data variables.
In this embodiment, the data of each data variable in the sample set may be data labeled with a fuzzy classification label in advance. The fuzzy classification labels are classification labels which are artificially assigned to each data in each data variable according to semantic form relations among the data in each data variable in the sample set.
Specifically, for each data in each data variable in the sample set, clustering the data according to the fuzzy classification label of each data to obtain a plurality of fuzzy sets of each data variable.
Taking the construction of a hospital management evaluation system as an example, the data with the data variable of the sample set as the scientific research score has three fuzzy classification labels of high, high and low, and the data of the same label are respectively aggregated together to obtain three fuzzy sets of high scientific research score, high scientific research score and low scientific research score.
Optionally, the determining manner of the fuzzy set may also be: and carrying out fuzzy classification on each data variable in the sample set to obtain a plurality of fuzzy sets of the data variables.
In this embodiment, the data of each data variable in the sample set may be data that is not previously labeled with a fuzzy classification tag. The fuzzy classification may be a classification of each data variable in the sample set based on a fuzzy class numerical range determined manually from semantic formal relationships between the respective data in each data variable.
Specifically, the data in each data variable in the sample set is subjected to size sorting, a fuzzy category numerical range is determined artificially, the data variables in the sample set are subjected to fuzzy classification by adopting a fuzzy classification algorithm to obtain fuzzy classification labels of each data variable, and the data are clustered according to the fuzzy classification labels of each data variable to obtain a plurality of fuzzy sets of each data variable.
Taking the construction of a hospital management evaluation system as an example, the data of each data variable, such as scientific research score, topic number, paper number, result prize number and patent number, are respectively subjected to size sorting, and the sorted data are divided into three parts in a uniform or non-uniform mode according to the size, so that fuzzy classification is realized. The fuzzy classification types of the scientific scores can be 'very high', 'not high', the fuzzy classification types of the subject numbers can be 'many', 'not many', the fuzzy classification types of the paper numbers can be 'many', and the fuzzy classification types of the result prize number and the patent number can be 'not few', 'few'. Taking the scientific research score as an example, determining that the fuzzy classification type of the data in the middle part of all data ordering of the scientific research score variable is 'high' according to the semantic form relation among all data in the scientific research score variable, clustering the data into fuzzy sets with 'high scientific research score' according to the fuzzy classification labels of the data as 'high scientific research score', and obtaining fuzzy sets with 'high scientific research score' and 'low scientific research score' by the same method.
Optionally, for any data variable, determining a reference fuzzy set in a plurality of fuzzy sets of the data variable, and setting a membership function of the reference fuzzy set; and determining membership functions of other fuzzy sets based on the functional relation between the other fuzzy sets and the reference fuzzy set and the membership functions of the reference fuzzy set.
In this embodiment, the reference fuzzy set of each data variable is one fuzzy set of all fuzzy sets of each data variable, and the reference fuzzy set may be a fuzzy set of intermediate positions of all fuzzy set semantic form relationships, for example. Table 2 lists the semantic formal relationships of the common reference fuzzy sets and other fuzzy sets and their corresponding membership function expressions:
TABLE 2
Specifically, for any data variable, one fuzzy set is selected from a plurality of fuzzy sets of the data variable to serve as a reference fuzzy set, the membership function type of the reference fuzzy set is selected, and membership function parameter values of the reference fuzzy set are determined through data fitting. Taking an expected variance matching method as an example, firstly, sorting the sizes of each data variable of data in a sample set, extracting the data variables at the positions corresponding to the reference fuzzy sets to form a reference data subset of each data variable according to the semantic form relation between the reference fuzzy sets and other fuzzy sets of each data variable, and estimating membership function parameter values by calculating the average value and variance of the reference data subset of each data variable to obtain membership functions of the reference fuzzy sets. And determining membership functions of other fuzzy sets by using membership functions of the reference fuzzy set of each data variable according to semantic form relations between the reference fuzzy set of each data variable and other fuzzy sets.
Taking the construction of a hospital management evaluation system as an example, selecting a fuzzy set of the middle part of the semantic form relation of each data variable as a reference fuzzy set, and setting the membership function type of the reference fuzzy set as a Gaussian function. Taking scientific research score as an example, fitting parameters mu and sigma of the variable membership function by adopting an expected variance matching method according to data in a fuzzy set with high scientific research score, and determining membership function u with high scientific research score of a reference fuzzy set High scientific research (x) According to the semantic form relation of the reference fuzzy set of high scientific research score and other fuzzy sets of high scientific research score and low scientific research score, determining a membership function u of the fuzzy set of high scientific research score and low scientific research score Has high scientific research (x) And u Scientific research is not high (x) A. The invention relates to a method for producing a fibre-reinforced plastic composite Table 3 lists the semantic form relationships between the reference fuzzy set and other fuzzy sets of each data variable of the hospital management evaluation system and the corresponding membership function expressions:
TABLE 3 Table 3
S130, acquiring a plurality of preset grid area numbers, carrying out area division on the sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and membership functions of the fuzzy set.
In the present embodiment, the grid area number is the data variable X 1 ,…,X i ,…,X p ，Y 1 ,…,Y j ,…Y q The number of grid areas obtained by cutting the value space (p+q dimension) is N 1 ,…,N i ,…,N p A grid area, where N i Is for the number of ith self-variable data space cuts, illustratively, N can be selected 1 ＝N 2 ＝…N i …＝N p =n. The data evaluation model is "IfThe evaluation model of the type of the ten is input as a fuzzy set to which the independent variable belongs, and output as an evaluation result, wherein the evaluation result is the fuzzy set to which the result variable belongs.
Specifically, according to the preset number of the space cuts of each self-variable data, the value space (p+q dimension) of the data in the sample set is cut to form N 1 ,…,N i ,…,N p The space size of each grid area can be the same or different. All grid areas are traversed in a certain order, which may be, for example, traversing in dimensions, which is not limited by the present embodiment. For each grid region, determining fuzzy set of each data variable of the region and corresponding membership function according to each self-variable data and result variable data of the sample set divided by the region, and determining fuzzy set A of the independent variable with maximum membership degree 1 ,…,A i ,…,A p Fuzzy set B of result variables 1 ,…,B j ,…,B q Determine "If A 1 ,…,A i ,…,A p ，Then B 1 ,…,B j ,…,B q "formal data evaluation model.
Taking the construction of a hospital management evaluation system as an example, the value space (4+1 dimensions) of data in the sample set can be cut, and the 4-dimensional independent variable space can be equally divided. Traversing each grid area in a dimension loop mode. And substituting the data variable value into membership functions of all fuzzy sets of the data variable for data of each grid region to obtain membership degree, and assuming that the membership degree of the fuzzy sets of the data in the region is maximum, wherein the fuzzy sets of the data comprise high scientific research score, high topic number, large paper quantity, small result prize quantity and small patent quantity, and the data evaluation model of the region comprises the advantages of large If topic quantity, large paper quantity, small result prize quantity, small patent quantity and high Then scientific research score.
S140, performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; and determining a target data evaluation model based on the test error.
In this embodiment, based on the number of S different mesh areas, the data evaluation models of S candidates may be determined, and error testing is performed on the data evaluation models of S candidates through a test set, so as to determine a target data evaluation model among the data evaluation models of N candidates. The test error is used for representing the accuracy of the data evaluation model in evaluating the data.
Specifically, the number of S grid areas may be traversed, for each grid area number, the difference between the evaluation result predicted by the data evaluation model of the corresponding candidate and the actual evaluation result is calculated based on the test set, and the data evaluation model of the candidate having the smallest test error is selected as the target data evaluation model.
By way of example, assuming 3 different grid area numbers, a linear search method may be employed to traverse each grid area number, and a 5-fold cross-validation method may be employed for each grid area number to obtain 5 candidate data evaluation models. For each data in the test data set, if the predicted evaluation result of the candidate data evaluation model is the same as the real evaluation result, the test error is 1, otherwise, 0. And respectively calculating test errors of the data evaluation models of the 5 candidates, and selecting the data evaluation model of the candidate corresponding to the minimum test error as the data evaluation model of the candidate of the grid area number. And determining a candidate data evaluation model corresponding to the minimum test error from the 3 candidate data evaluation models based on the 3 different grid area numbers as a target data evaluation model.
Optionally, based on a data evaluation model of any grid area number, evaluating independent variables in the test set to obtain an evaluation result; and determining the test error of the data evaluation model based on the fuzzy set to which the result variable in the test set belongs and the evaluation result.
In this embodiment, the test error is an error between a fuzzy set with the largest membership degree obtained by the test set result variable based on the membership function and a fuzzy set corresponding to an evaluation conclusion obtained by the test set independent variable based on the data evaluation model.
Specifically, for the data evaluation model of any grid area number, performing error test based on a test set, substituting result variables of all data in the test set into fuzzy sets of maximum membership obtained by corresponding one or more membership functions, performing evaluation processing on independent variables of corresponding data in the test set through the data evaluation model to obtain fuzzy sets corresponding to evaluation results, comparing the two determined fuzzy sets, and determining the test error of the data evaluation model of the grid area number through the difference of the fuzzy sets.
Taking the construction of a hospital management evaluation system as an example, assume that the number of grid areas is 2 4 、4 4 And 8 4 When the grid area numbers are calculated respectively, the fuzzy set of the maximum membership degree obtained by the scientific research score variable of each piece of data of the test set according to the membership function and the variable of the subject number, the thesis number, the result prize number and the patent number of each piece of data of the test set are evaluated by using the corresponding data evaluation model obtained based on the sample set, so that an evaluation result is obtained, if the two variables are the same, the loss is 0, otherwise, the loss is 1, and the average test error of the test set is calculated as the test error of the data evaluation model corresponding to the grid area numbers. The grid area number is 2 4 、4 4 And 8 4 Comparing the test errors of the corresponding data evaluation models to be 0.27, 0.31 and 0.21 respectively, and setting the grid area number with the minimum test error to be 8 4 As a target data evaluation model.
According to the technical scheme, the sample set and the test set are determined based on the data to be processed by acquiring the data to be processed; acquiring a fuzzy set of data variables of a sample set, and determining a membership function of the fuzzy set; obtaining a plurality of preset grid area numbers, carrying out area division on a sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and a membership function of a fuzzy set; performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; the method has the advantages that the objective data evaluation model is determined based on the test error, the interpretable data evaluation model is made by utilizing the semantic fuzzification and grid parameter fitting method, the problem that an optimal decision cannot be obtained only by means of subjective experience evaluation of a manager is solved, and the scientificity, rationality and usability of the whole evaluation system are improved.
Example two
Fig. 2 is a flowchart of a data processing method according to a second embodiment of the present invention, where the technical solution of the embodiment of the present invention is further optimized based on any of the foregoing embodiments. As shown in fig. 2, the method includes:
S210, acquiring data to be processed, and determining a sample set and a test set based on the data to be processed; wherein the data to be processed includes a plurality of data variables.
S220, acquiring a fuzzy set of data variables of the sample set, and determining a membership function of the fuzzy set.
S230, for the data variable of each region, determining a fuzzy set to which the data variable in the region belongs based on the data variable of the region and membership functions of the data variable corresponding to a plurality of fuzzy sets; wherein the data variables include a result variable and at least one argument.
Specifically, the data variables of the sample set include one or more independent variables and a result variable. For each grid region, a membership degree is calculated by utilizing a plurality of membership functions for each data variable, and one fuzzy set with the largest membership degree is used as the fuzzy set to which the data variable belongs in the region.
Optionally, processing the data value of the data variable in the region based on the membership functions of the data variable corresponding to the plurality of fuzzy sets, and determining membership data of the data variable to the plurality of fuzzy sets; and determining the fuzzy set corresponding to the maximum membership data as the fuzzy set to which the data variable belongs.
Specifically, for each grid region, a membership degree is calculated for each data variable of each piece of data in the sample set by using a plurality of corresponding membership functions, and a fuzzy set with the largest membership degree is used as a fuzzy set to which the data variable of the piece of data belongs. And comparing the membership degree of the fuzzy set of all the data membership in the grid area aiming at each data variable, wherein the fuzzy set corresponding to the maximum membership degree is the fuzzy set of the data variable membership in the area.
S240, constructing a data evaluation strategy of the region based on the fuzzy set to which at least one independent variable belongs in the region and the fuzzy set to which the result variable belongs, and forming a data evaluation model by the data evaluation strategies of the regions.
In this embodiment, the data evaluation policy is a policy for judging a fuzzy set to which a result variable belongs according to a fuzzy set to which an independent variable belongs, and further evaluating the result variable.
Specifically, for each grid region, fuzzy set A of one or more independent variables and result variable membership of the sample set partitioned according to the region 1 ,…,A i ,…,A p B, B 1 Determining the region "If A 1 ,…,A i ,…,A p ，Then B 1 "formal data evaluation strategy, N 1 ,…,N i ,…,N p The data evaluation strategies of the grid areas form a data evaluation model of the whole value space.
Taking the construction of a hospital management evaluation system as an example, the value space (4+1 dimension) of the data in the sample set can be cut, and N is selected 1 ＝N 2 ＝N 3 ＝N 4 Each grid region is determined based on the fuzzy sets to which the independent variable topic number, thesis number, result prize number and patent number, and result variable scientific score are respectively affiliated, and the region' If A =2 Problem (S) ,A Paper article ,A Achievements prize ,A Patent (S) ，Then B Scientific research "formal data evaluation strategy, will 2 4 The data evaluation strategies of the grid areas form a data evaluation model of the whole value space.
Optionally, the sample set includes a plurality of pieces of data, each piece of data including at least one argument and one result variable; constructing a candidate data evaluation strategy based on the fuzzy set to which at least one independent variable in each piece of data belongs and the fuzzy set to which a result variable belongs; determining strategy strength data of candidate data evaluation strategies based on membership data of independent variables in each piece of data to the affiliated fuzzy set and membership data of result variables to the affiliated fuzzy set; and determining the data evaluation strategy of the region based on the strategy intensity data of each of the plurality of candidate data evaluation strategies.
In this embodiment, each candidate data evaluation policy is a data evaluation policy formulated according to a fuzzy set to which a data variable of each data belongs, in the form of "If a 1 ,…,A i ,…,A p ，Then B 1 ". The policy intensity data is the product of membership degrees of corresponding fuzzy sets to which all data variables of each piece of data belongThe data evaluation policy of the region is the candidate data evaluation policy with the largest policy intensity data in the region.
Specifically, for each piece of data in the sample set, calculating fuzzy sets to which one or more pieces of self-variable data and one piece of result variable data of the piece of data belong respectively, constructing a candidate data evaluation strategy according to the fuzzy sets to which each data variable of each piece of data belongs for each grid area, calculating strategy intensity data of the candidate data evaluation strategies corresponding to all pieces of data in the area, and selecting the candidate data evaluation strategy corresponding to the maximum strategy intensity data as the data evaluation strategy of the area.
Taking the construction of a hospital management evaluation system as an example, for each grid area, utilizing each piece of data in the area by a sample set to formulate a candidate data evaluation strategy consisting of fuzzy sets to which each data variable of the data belongs, and calculating strategy intensity data D=u of the candidate data evaluation strategy Problem (S) (x)×u Paper article (x)×u Achievements prize (x)×u Patent (S) (x)×u Scientific research (x) And selecting a candidate data evaluation strategy corresponding to the maximum strategy intensity data of the area as the data evaluation strategy of the area.
S250, performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; and determining a target data evaluation model based on the test error.
According to the technical scheme, the sample set and the test set are determined based on the data to be processed by acquiring the data to be processed; acquiring a fuzzy set of data variables of a sample set, and determining a membership function of the fuzzy set; for the data variable of each region, determining the fuzzy set to which the data variable in the region belongs based on the data variable of the region and membership functions of the data variable corresponding to a plurality of fuzzy sets; wherein the data variables include a result variable and at least one argument; constructing a data evaluation strategy of the region based on the fuzzy set to which at least one independent variable belongs in the region and the fuzzy set to which the result variable belongs, and forming a data evaluation model by the data evaluation strategies of a plurality of regions; performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; the method has the advantages that the objective data evaluation model is determined based on the test error, the interpretable data evaluation model is made by utilizing the semantic fuzzification and grid parameter fitting method, the problem that an optimal decision cannot be obtained only by means of subjective experience evaluation of a manager is solved, and the scientificity, rationality and usability of the whole evaluation system are improved.
Example III
Fig. 3 is a schematic structural diagram of a data processing apparatus according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
a data acquisition module 310, configured to acquire data to be processed, and determine a sample set and a test set based on the data to be processed; wherein the data to be processed comprises a plurality of data variables;
a membership function determining module 320, configured to obtain a fuzzy set of data variables of the sample set, and determine membership functions of the fuzzy set;
the regional data evaluation model determining module 330 is configured to obtain a plurality of preset grid region numbers, perform regional division on the sample set for each network region number, and determine a data evaluation model corresponding to the network region number based on membership functions of the regional divided sample set and the fuzzy set;
the target data evaluation model determining module 340 is configured to perform an error test on the data evaluation models of the plurality of grid area numbers based on the test set, so as to obtain a test error; and determining a target data evaluation model based on the test error.
According to the technical scheme, the sample set and the test set are determined based on the data to be processed by acquiring the data to be processed; acquiring a fuzzy set of data variables of a sample set, and determining a membership function of the fuzzy set; obtaining a plurality of preset grid area numbers, carrying out area division on a sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and a membership function of a fuzzy set; performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; the method has the advantages that the objective data evaluation model is determined based on the test error, the interpretable data evaluation model is made by utilizing the semantic fuzzification and grid parameter fitting method, the problem that an optimal decision cannot be obtained only by means of subjective experience evaluation of a manager is solved, and the scientificity, rationality and usability of the whole evaluation system are improved.
Based on the above embodiment, optionally, the membership function determining module 320 includes:
the fuzzy set acquisition unit is used for acquiring fuzzy sets of data variables of the sample set;
and the membership function determining unit is used for determining membership functions of the fuzzy set.
On the basis of the above embodiment, optionally, the fuzzy set obtaining unit is specifically configured to obtain fuzzy classification labels of the data variables in the sample set, and determine the fuzzy set of the data variables based on the fuzzy classification labels of the data variables.
On the basis of the above embodiment, optionally, the fuzzy set obtaining unit is specifically configured to perform fuzzy classification on each data variable in the sample set to obtain a plurality of fuzzy sets of the data variables.
On the basis of the above embodiment, optionally, the membership function determining unit is specifically configured to: for any data variable, determining a reference fuzzy set in a plurality of fuzzy sets of the data variable, and setting a membership function of the reference fuzzy set; and determining membership functions of other fuzzy sets based on the functional relation between the other fuzzy sets and the reference fuzzy set and the membership functions of the reference fuzzy set.
Based on the above embodiment, optionally, the area data evaluation model determining module 330 includes:
The fuzzy set determining unit is used for determining fuzzy sets to which the data variable in the region belongs based on the data variable of the region and membership functions of the data variable corresponding to the fuzzy sets for the data variable of each region; wherein the data variables include a result variable and at least one argument;
the data evaluation model forming unit is used for constructing a data evaluation strategy of the region based on the fuzzy set to which at least one independent variable in the region belongs and the fuzzy set to which the result variable belongs, and the data evaluation strategies of the plurality of regions form a data evaluation model.
On the basis of the above embodiment, optionally, the area ambiguity set determining unit is specifically configured to: processing the data values of the data variables in the region based on the membership functions of the data variables corresponding to the fuzzy sets, and determining membership data of the data variables to the fuzzy sets; and determining the fuzzy set corresponding to the maximum membership data as the fuzzy set to which the data variable belongs.
On the basis of the above embodiment, optionally, the sample set includes a plurality of pieces of data, each piece of data including at least one argument and one result variable; a data evaluation model forming unit specifically for: constructing a candidate data evaluation strategy based on the fuzzy set to which at least one independent variable in each piece of data belongs and the fuzzy set to which a result variable belongs; determining strategy strength data of candidate data evaluation strategies based on membership data of independent variables in each piece of data to the affiliated fuzzy set and membership data of result variables to the affiliated fuzzy set; and determining the data evaluation strategy of the region based on the strategy intensity data of each of the plurality of candidate data evaluation strategies.
Based on the above embodiment, optionally, the target data evaluation model determining module 340 is specifically configured to: based on a data evaluation model of any grid area number, evaluating independent variables in the test set to obtain an evaluation result; and determining the test error of the data evaluation model based on the fuzzy set to which the result variable in the test set belongs and the evaluation result.
The data processing device provided by the embodiment of the invention can execute the data processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 is a schematic structural diagram of an electronic device implementing a data processing method according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as data processing methods.
In some embodiments, the data processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more of the steps of the data processing method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the data processing method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out data processing methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
Example five
The fifth embodiment of the present invention also provides a computer readable storage medium storing computer instructions for causing a processor to execute a data processing method, the method comprising:
acquiring data to be processed, and determining a sample set and a test set based on the data to be processed; wherein the data to be processed comprises a plurality of data variables; acquiring a fuzzy set of data variables of a sample set, and determining a membership function of the fuzzy set; obtaining a plurality of preset grid area numbers, carrying out area division on a sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and a membership function of a fuzzy set; performing error test on the data evaluation models of the grid area numbers based on the test set to obtain test errors; and determining a target data evaluation model based on the test error.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of data processing, comprising:
acquiring data to be processed, and determining a sample set and a test set based on the data to be processed; wherein the data to be processed comprises a plurality of data variables;
acquiring a fuzzy set of the data variables of the sample set, and determining a membership function of the fuzzy set;
obtaining a plurality of preset grid area numbers, carrying out area division on the sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set subjected to the area division and the membership function of the fuzzy set;
performing error testing on the data evaluation models of the grid area numbers based on the testing set to obtain testing errors; and determining a target data evaluation model based on the test error.
2. The method of claim 1, wherein the obtaining the fuzzy set of the data variables of the sample set comprises:
acquiring fuzzy classification labels of the data variables in the sample set, and determining fuzzy sets of the data variables based on the fuzzy classification labels of the data variables; or,
and carrying out fuzzy classification on each data variable in the sample set to obtain a plurality of fuzzy sets of the data variables.
3. The method of claim 1, wherein the determining membership functions of the fuzzy sets comprises:
for any data variable, determining a reference fuzzy set in a plurality of fuzzy sets of the data variable, and setting a membership function of the reference fuzzy set;
and determining membership functions of other fuzzy sets based on the functional relation between the other fuzzy sets and the reference fuzzy set and the membership functions of the reference fuzzy set.
4. The method of claim 1, wherein determining the data evaluation model corresponding to the number of network regions based on the regional division sample set and the membership function of the fuzzy set comprises:
for the data variable of each region, determining a fuzzy set to which the data variable in the region belongs based on the data variable of the region and membership functions of the data variable corresponding to a plurality of fuzzy sets; wherein the data variables include a result variable and at least one argument;
and constructing a data evaluation strategy of the region based on the fuzzy set to which at least one independent variable belongs in the region and the fuzzy set to which the result variable belongs, and forming a data evaluation model by the data evaluation strategies of a plurality of regions.
5. The method of claim 4, wherein the determining the fuzzy set to which the data variable belongs within the region based on the data variable of the region and membership functions of the data variable corresponding to the plurality of fuzzy sets comprises:
processing the data values of the data variables in the region based on the membership functions of the data variables corresponding to the fuzzy sets, and determining membership data of the data variables to the fuzzy sets;
and determining the fuzzy set corresponding to the maximum membership data as the fuzzy set to which the data variable belongs.
6. The method of claim 4, wherein the sample set comprises a plurality of pieces of data, each piece of data comprising at least one argument and one result argument;
the constructing a data evaluation strategy of the region based on the fuzzy set to which at least one independent variable belongs in the region and the fuzzy set to which the result variable belongs comprises the following steps:
constructing a candidate data evaluation strategy based on the fuzzy set to which at least one independent variable belongs in each piece of data and the fuzzy set to which the result variable belongs;
Determining strategy strength data of the candidate data evaluation strategy based on membership data of independent variables in each piece of data to the affiliated fuzzy set and membership data of the result variables to the affiliated fuzzy set;
and determining the data evaluation strategy of the area based on the strategy intensity data of each of the plurality of candidate data evaluation strategies.
7. The method according to claim 1, wherein the performing error testing on the data evaluation models of the plurality of grid area numbers based on the test set to obtain test errors includes:
based on any data evaluation model of the grid area number, performing evaluation processing on the independent variables in the test set to obtain an evaluation result;
and determining the test error of the data evaluation model based on the fuzzy set to which the result variable in the test set belongs and the evaluation result.
8. A data processing apparatus, comprising:
the data acquisition module is used for acquiring data to be processed and determining a sample set and a test set based on the data to be processed; wherein the data to be processed comprises a plurality of data variables;
the membership function determining module is used for acquiring a fuzzy set of the data variables of the sample set and determining membership functions of the fuzzy set;
The data evaluation model determining module is used for obtaining a plurality of preset grid area numbers, carrying out area division on the sample set aiming at each network area number, and determining a data evaluation model corresponding to the network area number based on the sample set divided by the area and the membership function of the fuzzy set;
the target data evaluation model determining module is used for carrying out error testing on the data evaluation models of the grid area numbers based on the testing set to obtain testing errors; and determining a target data evaluation model based on the test error.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions for causing a processor to implement the data processing method of any one of claims 1-7 when executed.
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