CN114418354A - Evaluation method, evaluation device, terminal device and computer-readable storage medium - Google Patents
Evaluation method, evaluation device, terminal device and computer-readable storage medium Download PDFInfo
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Abstract
The application is applicable to the technical field of data processing, and provides an evaluation method, an evaluation device, terminal equipment and a computer-readable storage medium, wherein the evaluation method comprises the following steps: acquiring a sample data set, wherein the sample data set comprises a group of sample data corresponding to each of a plurality of sample evaluation objects, and each group of sample data comprises a first evaluation total score for representing an evaluation result and a first parameter value corresponding to each of a plurality of evaluation parameters; generating an evaluation model according to the sample data set, wherein independent variables in the evaluation model are the evaluation parameters, and dependent variables are the evaluation results; acquiring a second parameter value of the plurality of evaluation parameters of the target evaluation object; and inputting the second parameter value into the evaluation model to obtain a second evaluation total score of the target evaluation object. By the method, the labor cost can be effectively reduced, and the objectivity and reliability of the evaluation result can be improved.
Description
Technical Field
The present application belongs to the technical field of data processing, and in particular, relates to an evaluation method, an evaluation device, a terminal device, and a computer-readable storage medium.
Background
Most businesses need to make evaluations on a regular basis. For example, the financial status of the business is periodically evaluated, the performance of the business is evaluated, and so on. Because different evaluation objects relate to different evaluation indexes and a uniform evaluation system cannot be formed, in the existing method, an evaluator usually collects index data and then carries out manual evaluation according to the index data. The conventional evaluation method has the disadvantages of high labor cost, high subjectivity of manual evaluation, poor objectivity of evaluation results and low reliability.
Disclosure of Invention
The embodiment of the application provides an evaluation method, an evaluation device, terminal equipment and a computer readable storage medium, which can effectively reduce the labor cost and improve the objectivity and reliability of an evaluation result.
In a first aspect, an embodiment of the present application provides an evaluation method, including:
acquiring a sample data set, wherein the sample data set comprises a group of sample data corresponding to each of a plurality of sample evaluation objects, and each group of sample data comprises a first evaluation total score for representing an evaluation result and a first parameter value corresponding to each of a plurality of evaluation parameters;
generating an evaluation model according to the sample data set, wherein independent variables in the evaluation model are the evaluation parameters, and dependent variables are the evaluation results;
acquiring a second parameter value of the plurality of evaluation parameters of the target evaluation object;
and inputting the second parameter value into the evaluation model to obtain a second evaluation total score of the target evaluation object.
In the embodiment of the application, an evaluation model with evaluation parameters as independent variables and evaluation results as dependent variables can be automatically generated through the acquired sample data set. By the method, evaluation result errors caused by subjectivity of manual evaluation are avoided, the objectivity and reliability of evaluation can be effectively improved, and the labor cost is greatly reduced. In addition, the method can be applied to different evaluation systems, and has strong applicability.
In a possible implementation manner of the first aspect, the obtaining the sample data set includes:
for each set of the sample data, calculating a correlation coefficient between the first evaluation total score and each of the first parameter values in the sample data;
and if the correlation coefficient is not in a first preset range, deleting the first parameter value corresponding to the correlation coefficient from the sample data, and adding the first parameter value corresponding to the correlation coefficient into a standby sample library.
In a possible implementation manner of the first aspect, the generating an evaluation model according to the sample data set includes:
calculating a regression coefficient of the independent variable and a constant coefficient between the independent variable and the dependent variable according to multiple groups of sample data in the sample data set;
generating a candidate model between the independent variable and the dependent variable according to the calculated regression coefficient and the constant system;
performing feasibility verification on the candidate model;
and if the verification is passed, determining the candidate model as the evaluation model.
In one possible implementation form of the first aspect, the feasibility verification comprises an accuracy verification;
correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating a determinable coefficient between the third evaluation total score and the first evaluation total score;
if the coefficient exceeds a second preset range, the verification is failed;
and if the coefficient is within the second preset range, the verification is passed.
In one possible implementation form of the first aspect, the feasibility verification comprises joint hypothesis testing;
correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating an actual statistical value of joint hypothesis testing according to the first evaluation total score and the third evaluation total score;
determining a critical statistical value according to a preset significance parameter;
if the actual statistic value is within the range of the critical statistic value, the verification is passed;
if the actual statistic value is not within the range of the critical statistic value, the verification fails.
In one possible implementation form of the first aspect, the feasibility verification comprises a residual error check;
correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating residual values of the first evaluation total score and the third evaluation total score stent;
if the residual error value obeys the preset normal distribution, the verification is passed;
and if the residual error value does not comply with the preset normal distribution, the verification fails.
In a possible implementation manner of the first aspect, after performing feasibility verification on the candidate model, the method further includes:
if the verification is not passed, acquiring standby sample data from the standby sample library;
and regenerating a candidate model according to the standby sample data.
In a second aspect, an embodiment of the present application provides an evaluation apparatus, including:
the system comprises a sample acquisition unit, a data analysis unit and a data analysis unit, wherein the sample acquisition unit is used for acquiring a sample data set, the sample data set comprises a group of sample data corresponding to a plurality of sample evaluation objects, and each group of sample data comprises a first evaluation total score used for representing an evaluation result and a first parameter value corresponding to a plurality of evaluation parameters;
a model generating unit, configured to generate an evaluation model according to the sample data set, where an independent variable in the evaluation model is the multiple evaluation parameters, and a dependent variable is the evaluation result;
a data acquisition unit configured to acquire a second parameter value of the plurality of evaluation parameters of a target evaluation object;
and the object evaluation unit is used for inputting the second parameter value into the evaluation model to obtain a second evaluation total score of the target evaluation object.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the evaluation method according to any one of the above first aspects.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, and an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, where the computer program, when executed by a processor, implements the evaluation method according to any one of the above first aspects.
In a fifth aspect, the present application provides a computer program product, which when run on a terminal device, causes the terminal device to execute the evaluation method of any one of the above first aspects.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of an evaluation method provided in an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a generation process of an evaluation model provided in an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an evaluation device provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise.
Referring to fig. 1, which is a schematic flow chart of an evaluation method provided in the embodiment of the present application, by way of example and not limitation, the method may include the following steps:
s101, a sample data set is obtained, wherein the sample data set comprises a group of sample data corresponding to each of a plurality of sample evaluation objects.
Each group of sample data comprises a first evaluation total score used for representing an evaluation result and a first parameter value corresponding to each of a plurality of evaluation parameters.
Related data can be acquired from the public document through a crawler algorithm, and then the data are generated into a sample data set. Taking the enterprise evaluation application scenario as an example, the temporary filling of the enterprise, the periodic filling of the enterprise, and the public data in some application programs can be obtained. The method can search keywords for the collected texts and documents, and acquire related data according to the searched keywords. For example, the evaluation parameter is annual business, keywords related to annual business, such as "business", "annual business", etc., may be retrieved from the collected text or document, and then data before/after the retrieved keywords is determined as the related data of the evaluation parameter.
Because the sample data set is related to the precision of a subsequent evaluation model, an expert evaluation method can be introduced in the process of constructing the sample data set. Specifically, the expert in the related field analyzes the collected related data and sorts out a set of evaluation system, wherein the evaluation system includes evaluation parameters related to evaluation results and may also include questions under each evaluation parameter. And a plurality of experts score the sample evaluation objects according to the sorted evaluation system. After the highest score and the lowest score of each problem under each evaluation parameter are removed, the average score of each problem under each evaluation parameter is calculated; and after the highest score and the lowest score corresponding to each evaluation parameter are removed, calculating the average score of each evaluation parameter. And calculating the total evaluation score of the sample evaluation object according to the calculated average score. And taking the total evaluation score of each sample evaluation object, the corresponding score of each evaluation parameter and the corresponding score of each question under each evaluation parameter as a group of sample data.
In the embodiment of the application, the score of each evaluation parameter is calculated according to the score of the question under each evaluation parameter, and the total evaluation score of each sample evaluation object is calculated according to the score of each evaluation parameter.
After the sample data set is obtained, the sample data set can be subjected to preliminary error check to judge whether the data is correct. Specifically, a threshold corresponding to each evaluation parameter may be preset, and if a parameter value of the evaluation parameter is within a threshold range, the data is correct; if the parameter value of the evaluation parameter is not within the threshold range, the data is erroneous. For example, the evaluation parameter is the number of developers, and the corresponding threshold value is a positive integer greater than 0.
In order to further obtain a relatively pure sample data set, in an embodiment, the process of obtaining the sample data set further includes a data cleaning step. Specifically, the data cleansing includes:
for each set of the sample data, calculating a correlation coefficient between the first evaluation total score and each of the first parameter values in the sample data; and if the correlation coefficient is not in a first preset range, deleting the first parameter value corresponding to the correlation coefficient from the sample data, and adding the first parameter value corresponding to the correlation coefficient into a standby sample library.
Optionally, in this embodiment of the present application, a pearson formula may be used to calculate the correlation coefficient. Pearson's formula of
Where cov (x, y) represents the covariance between the variables x and y, σxDenotes the standard deviation, σ, of the variable xyThe standard deviation of the variable y is indicated.
As described above, the evaluation result is determined by a plurality of evaluation parameters, each of which may be determined by a plurality of questions. Therefore, the Pearson correlation coefficient can be calculated between every two problems aiming at the problems under each evaluation parameter, and the problems which do not accord with the third preset range are filtered. And calculating the Pearson correlation coefficient between every two evaluation parameters according to the evaluation result, and filtering the evaluation parameters which do not accord with the first preset range.
And S102, generating an evaluation model according to the sample data set, wherein independent variables in the evaluation model are the evaluation parameters, and dependent variables are the evaluation results.
As described above, the evaluation result is determined by a plurality of evaluation parameters, each of which may be determined by a plurality of questions. Therefore, the evaluation model includes two parts, one is a model in which the evaluation result is a dependent variable and the evaluation parameter is an independent variable, and the other is a model in which the evaluation parameter is a dependent variable and the problem under the evaluation parameter is an independent variable.
Illustratively, the model with the dependent variable as the evaluation result and the independent variable as the evaluation parameter is:
y1＝a0+a1x1+a2x2+a3x3+…+anxn+anxn+ε1；
wherein y1 is the evaluation result, xnDenotes the nth evaluation parameter, anFor the regression coefficient corresponding to the nth evaluation parameter,. epsilon.1 is a constant coefficient.
The evaluation parameter is a dependent variable, and the problem under the evaluation parameter is a model of an independent variable:
xn＝bn0+bn1zn1+bn2zn2+bn3zn3+…+bnnznn+ε2n；
wherein z isnnFor the nth evaluation parameter xnN-th problem ofnnFor the nth evaluation parameter xnRegression coefficient corresponding to the nth problem, ε 2nIs a constant coefficient.
As described above, after the model in which the evaluation parameter is the dependent variable and the problem under the evaluation parameter is the independent variable is calculated, the model is substituted into the model in which the evaluation result is the dependent variable and the evaluation parameter is the independent variable, so that the evaluation model can be obtained.
In order to ensure the feasibility of the evaluation model, in an embodiment, the generation manner of the evaluation model further includes a feasibility verification process for the model, specifically:
calculating a regression coefficient of the independent variable and a constant coefficient between the independent variable and the dependent variable according to multiple groups of sample data in the sample data set; generating a candidate model between the independent variable and the dependent variable according to the calculated regression coefficient and the constant system; performing feasibility verification on the candidate model; if the verification is passed, determining the candidate model as the evaluation model; and if the verification fails, acquiring the standby sample data from the standby sample database, and regenerating the candidate model according to the standby sample data.
Optionally, the feasibility verification comprises accuracy verification. Correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score; calculating a determinable coefficient between the third evaluation total score and the first evaluation total score; if the coefficient exceeds a second preset range, the verification is failed; and if the coefficient is within the second preset range, the verification is passed.
The first evaluation total score represents an actual value, and the third evaluation total score represents a predicted value. Specifically, the calculation method of the coefficient may be:
calculate the sum of the squares of the total deviations:
(yiin the form of an actual value of the value,
as an average of actual values);
calculating a regression sum of squares:
(
is a predicted value);
calculating the sum of squares of the residuals:
calculating a block coefficient:
optionally, the coefficient may be trimmed, and then it is determined whether the trimmed coefficient meets a second preset range. In particular, by the formula
The calculation fine-tunes the coefficients, where n is the sample size and k is the number of arguments.
In practical applications, the coefficient of probability is between 0 and 1. Preferably, the second preset range is set to
Optionally, the feasibility verification comprises joint hypothesis testing. Correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score; calculating an actual statistical value of joint hypothesis testing according to the first evaluation total score and the third evaluation total score; determining a critical statistical value according to a preset significance parameter; if the actual statistic value is within the range of the critical statistic value, the verification is passed; if the actual statistic value is not within the range of the critical statistic value, the verification fails.
The joint hypothesis test in this application is the F test. Can be represented by formula
The statistics are calculated subject to the F distribution of (k, n-k-1) (i.e., the actual statistical value).
Preferably, the significance parameter α in the embodiment of the present application may be set to α ═ 0.1, 0.05, and 0.01. The critical statistic F corresponding to alpha can be obtained by inquiring the distribution tableα(k,n-k-1)。
Optionally, the feasibility verification comprises a residual error check. Correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score; calculating residual values of the first evaluation total score and the third evaluation total score stent; if the residual error value obeys the preset normal distribution, the verification is passed; and if the residual error value does not comply with the preset normal distribution, the verification fails.
Preferably, the predetermined normal distribution in the embodiment of the present application may be N (0, σ)2) Distribution of。
Optionally, the actual value and the predicted value can be displayed together in a visual manner, and whether the actual value is close to the predicted value or not can be judged through visual analysis; if so, the representative model is trained well.
As described above, the feasibility verification may include multiple verification methods, which may be parallel or serial. Of course, other verification methods may be included, and are not specifically limited herein.
When the feasibility verification includes multiple verification modes, correspondingly, the failure of the verification of the candidate model may refer to failure of any one verification mode.
And if the candidate model passes the verification, determining the candidate model as an evaluation model, and recording the regression coefficient of each variable in the candidate model as the weight of the variable in the evaluation model.
Further, the weights may be verified by an analytic hierarchy process. The specific process sequentially comprises the following steps: the correlation coefficient is taken as an integer, a proportional scale is obtained, a contrast matrix is constructed, the row mean value is solved, the scale type index weight is obtained, and consistency check is carried out.
In practical application, the weight proportion can be distributed according to different evaluation requirements. For example, the total score of the evaluation result is 100, and 100 is assigned to each variable according to the weight of each variable calculated as described above.
S103, obtaining a second parameter value of the plurality of evaluation parameters of the target evaluation object.
S104, inputting the second parameter value into the evaluation model to obtain a second evaluation total score of the target evaluation object.
In the embodiment of the application, an evaluation model with evaluation parameters as independent variables and evaluation results as dependent variables can be automatically generated through the acquired sample data set. By the method, evaluation result errors caused by subjectivity of manual evaluation are avoided, the objectivity and reliability of evaluation can be effectively improved, and the labor cost is greatly reduced. In addition, the method can be applied to different evaluation systems, and has strong applicability.
Fig. 2 is a schematic diagram of a generation flow of the evaluation model provided in the embodiment of the present application. As shown in fig. 2, the dependent variable and the independent variable are automatically set according to the sample data (pseudo-valid sample shown in fig. 2); establishing an evaluation model (a multiple linear regression model shown in FIG. 2) according to the sample data; substituting the sample data into the evaluation model to solve a constant term and a regression coefficient in the model; the evaluation model is then validated. The verification process includes confirming whether the precision of the evaluation model (the regression equation shown in fig. 2) meets the condition, whether the F-check is significantly established, whether the residual analysis follows normal distribution, and whether the actual value and the predicted value are closely displayed visually. If any of the verification is failed, selecting data with a higher correlation number from the standby sample data (the standby optimization sample database shown in fig. 2) to regenerate the evaluation model. If the verification is passed, carrying out consistency check on the weight according to an analytic hierarchy process; and if the verification is passed, determining the optimal weight, and further determining the final evaluation model. In application, the weight proportion is mapped according to the evaluation total score to obtain the required weight proportion.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 3 is a block diagram of an evaluation apparatus provided in the embodiment of the present application, which corresponds to the evaluation method described in the above embodiment, and only shows a part related to the embodiment of the present application for convenience of description.
Referring to fig. 3, the apparatus includes:
the sample obtaining unit 31 is configured to obtain a sample data set, where the sample data set includes a group of sample data corresponding to each of the multiple sample evaluation objects, and each group of sample data includes a first evaluation total score used for representing an evaluation result and a first parameter value corresponding to each of the multiple evaluation parameters.
A model generating unit 32, configured to generate an evaluation model according to the sample data set, where an independent variable in the evaluation model is the multiple evaluation parameters, and a dependent variable is the evaluation result.
A data acquisition unit 33 configured to acquire a second parameter value of the plurality of evaluation parameters of the target evaluation object.
And the object evaluation unit 34 is configured to input the second parameter value into the evaluation model, and obtain a second evaluation total score of the target evaluation object.
Optionally, the sample acquiring unit 31 is further configured to:
for each set of the sample data, calculating a correlation coefficient between the first evaluation total score and each of the first parameter values in the sample data;
and if the correlation coefficient is not in a first preset range, deleting the first parameter value corresponding to the correlation coefficient from the sample data, and adding the first parameter value corresponding to the correlation coefficient into a standby sample library.
Optionally, the model generating unit 32 is further configured to:
calculating a regression coefficient of the independent variable and a constant coefficient between the independent variable and the dependent variable according to multiple groups of sample data in the sample data set;
generating a candidate model between the independent variable and the dependent variable according to the calculated regression coefficient and the constant system;
performing feasibility verification on the candidate model;
and if the verification is passed, determining the candidate model as the evaluation model.
Optionally, the feasibility verification comprises accuracy verification; correspondingly, the model generation unit 32 is further configured to:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating a determinable coefficient between the third evaluation total score and the first evaluation total score;
if the coefficient exceeds a second preset range, the verification is failed;
and if the coefficient is within the second preset range, the verification is passed.
Optionally, the feasibility verification comprises joint hypothesis testing; correspondingly, the model generation unit 32 is further configured to:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating an actual statistical value of joint hypothesis testing according to the first evaluation total score and the third evaluation total score;
determining a critical statistical value according to a preset significance parameter;
if the actual statistic value is within the range of the critical statistic value, the verification is passed;
if the actual statistic value is not within the range of the critical statistic value, the verification fails.
Optionally, the feasibility verification comprises a residual error check; correspondingly, the model generation unit 32 is further configured to:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating residual values of the first evaluation total score and the third evaluation total score stent;
if the residual error value obeys the preset normal distribution, the verification is passed;
and if the residual error value does not comply with the preset normal distribution, the verification fails.
Optionally, the model generating unit 32 is further configured to:
if the verification is not passed, acquiring standby sample data from the standby sample library;
and regenerating a candidate model according to the standby sample data.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
In addition, the evaluation device shown in fig. 3 may be a software unit, a hardware unit, or a combination of software and hardware unit that is built in the existing terminal device, may be integrated into the terminal device as a separate pendant, or may exist as a separate terminal device.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 4, the terminal device 4 of this embodiment includes: at least one processor 40 (only one shown in fig. 4), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, the processor 40 implementing the steps in any of the various evaluation method embodiments described above when executing the computer program 42.
The terminal device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that fig. 4 is merely an example of the terminal device 4, and does not constitute a limitation of the terminal device 4, and may include more or less components than those shown, or combine some components, or different components, such as an input-output device, a network access device, and the like.
The Processor 40 may be a Central Processing Unit (CPU), and the Processor 40 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may in some embodiments be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. In other embodiments, the memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing an operating system, an application program, a Boot Loader (Boot Loader), data, and other programs, such as program codes of the computer programs. The memory 41 may also be used to temporarily store data that has been output or is to be output.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product, which when running on a terminal device, enables the terminal device to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to an apparatus/terminal device, recording medium, computer Memory, Read-Only Memory (ROM), Random-Access Memory (RAM), electrical carrier wave signals, telecommunications signals, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An evaluation method, comprising:
acquiring a sample data set, wherein the sample data set comprises a group of sample data corresponding to each of a plurality of sample evaluation objects, and each group of sample data comprises a first evaluation total score for representing an evaluation result and a first parameter value corresponding to each of a plurality of evaluation parameters;
generating an evaluation model according to the sample data set, wherein independent variables in the evaluation model are the evaluation parameters, and dependent variables are the evaluation results;
acquiring a second parameter value of the plurality of evaluation parameters of the target evaluation object;
and inputting the second parameter value into the evaluation model to obtain a second evaluation total score of the target evaluation object.
2. The evaluation method of claim 1, wherein said obtaining a sample data set comprises:
for each set of the sample data, calculating a correlation coefficient between the first evaluation total score and each of the first parameter values in the sample data;
and if the correlation coefficient is not in a first preset range, deleting the first parameter value corresponding to the correlation coefficient from the sample data, and adding the first parameter value corresponding to the correlation coefficient into a standby sample library.
3. The evaluation method of claim 2, wherein said generating an evaluation model from said set of sample data comprises:
calculating a regression coefficient of the independent variable and a constant coefficient between the independent variable and the dependent variable according to multiple groups of sample data in the sample data set;
generating a candidate model between the independent variable and the dependent variable according to the calculated regression coefficient and the constant system;
performing feasibility verification on the candidate model;
and if the verification is passed, determining the candidate model as the evaluation model.
4. The evaluation method of claim 3, wherein the feasibility verification comprises an accuracy verification;
correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating a determinable coefficient between the third evaluation total score and the first evaluation total score;
if the coefficient exceeds a second preset range, the verification is failed;
and if the coefficient is within the second preset range, the verification is passed.
5. The evaluation method of claim 3, wherein the feasibility verification comprises joint hypothesis testing;
correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating an actual statistical value of joint hypothesis testing according to the first evaluation total score and the third evaluation total score;
determining a critical statistical value according to a preset significance parameter;
if the actual statistic value is within the range of the critical statistic value, the verification is passed;
if the actual statistic value is not within the range of the critical statistic value, the verification fails.
6. The evaluation method of claim 3, wherein the feasibility verification comprises a residual test;
correspondingly, the performing feasibility verification on the candidate model comprises:
inputting the first parameter value in the sample data into the candidate model to obtain a predicted third evaluation total score;
calculating residual values of the first evaluation total score and the third evaluation total score stent;
if the residual error value obeys the preset normal distribution, the verification is passed;
and if the residual error value does not comply with the preset normal distribution, the verification fails.
7. The evaluation method of claim 3, wherein after performing feasibility verification on the candidate model, the method further comprises:
if the verification is not passed, acquiring standby sample data from the standby sample library;
and regenerating a candidate model according to the standby sample data.
8. An evaluation device, comprising:
the system comprises a sample acquisition unit, a data analysis unit and a data analysis unit, wherein the sample acquisition unit is used for acquiring a sample data set, the sample data set comprises a group of sample data corresponding to a plurality of sample evaluation objects, and each group of sample data comprises a first evaluation total score used for representing an evaluation result and a first parameter value corresponding to a plurality of evaluation parameters;
a model generating unit, configured to generate an evaluation model according to the sample data set, where an independent variable in the evaluation model is the multiple evaluation parameters, and a dependent variable is the evaluation result;
a data acquisition unit configured to acquire a second parameter value of the plurality of evaluation parameters of a target evaluation object;
and the object evaluation unit is used for inputting the second parameter value into the evaluation model to obtain a second evaluation total score of the target evaluation object.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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