CN117726222A - Method, equipment and storage medium for scoring scientific and creative enterprises based on AHP (advanced high performance) method - Google Patents
Method, equipment and storage medium for scoring scientific and creative enterprises based on AHP (advanced high performance) method Download PDFInfo
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Abstract
The invention discloses a scoring method, equipment and storage medium for a scientific and invasive enterprise based on an AHP method, which comprises the following steps: constructing an index evaluation system with characteristics of a scientific enterprise; calculating the weight of each index in the index evaluation system by adopting an analytic hierarchy process; calculating the score of each index in the index evaluation system by adopting a linear interpolation method based on the index weight; collecting related data related to scoring of a scientific and creative enterprise; and calculating the comprehensive score of the scientific enterprise according to the weight and the score of each index in the index evaluation system, and displaying the result. The invention can realize comprehensive, objective and accurate scoring of scientific enterprises.
Description
Technical Field
The invention belongs to the technical field of evaluation in a financial system, and particularly relates to a scientific and invasive enterprise scoring method, equipment and a storage medium based on an AHP method.
Background
The scientific enterprises attract a great deal of attention of investors due to the characteristics of strong innovation, great growth potential and the like. However, the evaluation process of the scientific and creative enterprise often involves multiple aspects, such as technical innovation, market prospect, financial condition, and the like, so that the accurate and comprehensive evaluation of the scientific and creative enterprise becomes quite complex.
In the field of the growth evaluation of the scientific enterprises, the popular scoring method of the scientific enterprises at present is mainly based on financial indexes and expert scores, such as a 5C scoring method, a CAMEL scoring method and the like. The method obtains the comprehensive score of the enterprise through comprehensive analysis of the enterprise financial report and expert opinion. Although the method can evaluate the growth of the scientific enterprises to a certain extent, the characteristics of innovation, growth and the like of the scientific enterprises are ignored, so that a large deviation exists between a scoring result and an actual situation. In addition, these methods lack objectivity and operability, are susceptible to subjective factors, and are not conducive to fair evaluation and comparison by scientific enterprises.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, equipment and a storage medium for scoring scientific enterprises based on an AHP method.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
in a first aspect, the invention discloses a method for scoring a scientific and invasive enterprise based on an AHP method, which comprises the following steps:
step S1: constructing an index evaluation system with characteristics of a scientific enterprise;
step S2: calculating the weight of each index in the index evaluation system by adopting an analytic hierarchy process;
step S3: calculating each index score in the index evaluation system by adopting a linear interpolation method based on the index weight in the step S2;
step S4: collecting related data related to scoring of a scientific and creative enterprise;
step S5: and calculating the comprehensive score of the scientific enterprise according to the weight and the score of each index in the index evaluation system, and displaying the result.
On the basis of the technical scheme, the following improvement can be made:
as a preferable scheme, step S1 specifically includes:
step S1.1: establishing a preliminary evaluation system with M evaluation indexes;
step S1.2: aiming at the importance degree and the true belief degree of M evaluation indexes, the experience of an expert is scored and summarized;
step S1.3: according to scoring summarization conditions of expert experience, N evaluation indexes are selected from M evaluation indexes, and an index evaluation system with characteristics of a scientific enterprise is constructed, wherein N is less than or equal to M.
As a preferable scheme, step S2 specifically includes:
step S2.1: calculating importance scores of all indexes in the index evaluation system according to scoring summarization conditions of expert experience;
step S2.2: according to the importance scores of the indexes, a scoring difference matrix of each index major class is constructed, K index major classes are arranged in an index evaluation system, and the kth index major class is provided with n k A number of indices, k=1, 2 …, K;
element A in scoring difference matrix ij The importance score difference value between the ith index and the jth index in the corresponding index large class is obtained;
wherein: i=1, 2 …, n k ；
j＝1,2…,n k ；
Step S2.3: according to the scoring difference matrix of each index major class, constructing an AHP comparison matrix of each index major class, and constructing an element B in the AHP comparison matrix ij The comparison value between the ith index and the jth index in the corresponding index class is specifically judged as follows;
if element A ij Not less than 0, then B ij ＝int(A ij )+1；
If element A ij <0, then B ij ＝1/(int(-A ij )+1)；
int is a rounding function;
step S2.4: adjusting an AHP comparison matrix of each index major class;
step S2.4: carrying out normalization processing on the AHP comparison matrix, and calculating based on the normalized matrix to obtain weight vectors of all indexes of each index major class;
step S2.5: and calculating the consistency index CI and the random consistency ratio CR of the AHP judgment matrix of each index class, checking whether the consistency of the AHP judgment matrix of each index class meets the requirement, and if not, readjusting the corresponding AHP judgment matrix until the requirement is met.
As a preferable scheme, step S3 specifically includes:
step S3.1: analyzing scoring summarization conditions of expert experience in the step S1 to obtain weight vectors of all index major categories;
summarizing and counting the index weight vectors in the step S2 to obtain weight vectors of which each index occupies a corresponding index major class;
the weight vector of each index accounting for all indexes is obtained through the following calculation;
b ki ＝a ki *a k ；
wherein: a, a k A weight vector which is the kth index major class;
a ki the ith index of the kth index large class occupies the weight vector of the kth index large class;
b ki the ith index which is the kth index major class occupies weight vectors of all indexes;
k＝1,2…,K；
i＝1,2,…,n k ；
step S3.2: setting the upper limit E of score card score max Lower score limit E of score card min Score card score weight E, e=e max -E min ；
Step S3.3: calculating the weight of each index distribution score, the lower limit of the index distribution score and the upper limit of the index distribution score through the following steps; e, e ki ＝b ki *E；
e min-ki ＝E min /N；
e max-ki ＝e ki +e min-ki ；
Wherein: e, e ki Assigning a score weight to the ith index of the kth index major class;
e min-ki assigning a lower score limit to the ith index of the kth index class;
e max-ki assigning an upper score limit to the ith index of the kth index class;
k＝1,2…,K；
i＝1,2,…,n k ；
step S3.4: subdividing each index value into a plurality of intervals, and carrying out index value horizontal sorting according to the credit quality of the index, wherein the better the credit quality of the index is, the smaller the sorting numerical value of the corresponding interval is;
step S3.5: and calculating the index value horizontal score of the index corresponding section by using a linear interpolation method according to the index distribution score lower limit, the index distribution score upper limit and the index value horizontal sequencing.
In a second aspect, the present invention further discloses a scoring device for a scientific and invasive enterprise based on the AHP method, including:
the index evaluation system construction module is used for constructing an index evaluation system with the characteristics of a scientific enterprise;
the index weight calculation module is used for calculating the weight of each index in the index evaluation system by adopting an analytic hierarchy process;
the index score calculating module is used for calculating each index score in the index evaluation system by adopting a linear interpolation method based on the index weight obtained by the index weight calculating module;
the data acquisition module is used for acquiring related data related to scoring of the scientific and invasive enterprises;
and the comprehensive scoring module is used for calculating the comprehensive score of the scientific enterprise according to the weight and the score of each index in the index evaluation system and displaying the result.
As a preferred solution, the index evaluation system construction module specifically includes:
a preliminary evaluation system establishing unit for establishing a preliminary evaluation system having M evaluation indexes;
the scoring summarization unit is used for scoring summarization of expert experience aiming at the importance degree and the true belief degree of the M evaluation indexes;
the index selection unit is used for selecting N evaluation indexes from M evaluation indexes according to scoring summarization conditions of expert experience, and constructing an index evaluation system with characteristics of a scientific enterprise, wherein N is less than or equal to M.
As a preferred solution, the index weight calculation module specifically includes:
the importance score calculating unit is used for calculating the importance scores of the indexes in the index evaluation system according to the scoring summarization condition of expert experience;
the scoring difference matrix construction unit is used for constructing the scoring difference matrix of each index major class according to the importance scores of the indexes, K index major classes are arranged in the index evaluation system, and the kth index major class is provided with n k A number of indices, k=1, 2 …, K;
element A in scoring difference matrix ij The importance score difference value between the ith index and the jth index in the corresponding index large class is obtained;
wherein: i=1, 2 …, n k ；
j＝1,2…,n k ；
An AHP comparison matrix construction unit for constructing an AHP comparison matrix of each index major class according to the scoring difference matrix of each index major class, wherein the elements B in the AHP comparison matrix are as follows ij The comparison value between the ith index and the jth index in the corresponding index class is specifically judged as follows;
if element A ij Not less than 0, then B ij ＝int(A ij )+1；
If element A ij <0, then B ij ＝1/(int(-A ij )+1)；
int is a rounding function;
the AHP comparison matrix adjustment unit is used for adjusting the AHP comparison matrix of each index class;
the index weight vector construction unit is used for carrying out normalization processing on the AHP comparison matrix and calculating based on the normalized matrix to obtain weight vectors of all indexes of each index class;
the checking unit is used for calculating the consistency index CI and the random consistency ratio CR of the AHP judgment matrix of each index class, checking whether the consistency of the AHP judgment matrix of each index class meets the requirement, and if not, readjusting the corresponding AHP judgment matrix until the requirement is met.
As a preferred embodiment, the index score calculating module specifically includes:
the first calculation unit is used for analyzing scoring summarization conditions of expert experiences obtained by the index evaluation system construction module to obtain weight vectors of all index major categories;
summarizing and counting the weight vectors of the indexes obtained by the index weight calculation module to obtain weight vectors of the indexes occupying the corresponding index major class;
the weight vector of each index accounting for all indexes is obtained through the following calculation;
b ki ＝a ki *a k ；
wherein: a, a k A weight vector which is the kth index major class;
a ki the ith index of the kth index large class occupies the weight vector of the kth index large class;
b ki the ith index which is the kth index major class occupies weight vectors of all indexes;
k＝1,2…,K；
i＝1,2,…,n k ；
a setting unit for setting the upper limit E of the score card max Lower score limit E of score card min Score card score weight E, e=e max -E min ；
The second calculation unit is used for calculating each index allocation score weight, an index allocation score lower limit and an index allocation score upper limit through the following steps;
e ki ＝b ki *E；
e min-ki ＝E min /N；
e max-ki ＝e ki +e min-ki ；
wherein: e, e ki Assigning a score weight to the ith index of the kth index major class;
e min-ki assigning a lower score limit to the ith index of the kth index class;
e max-ki assigning an upper score limit to the ith index of the kth index class;
k＝1,2…,K；
i＝1,2,…,n k ；
the horizontal sequencing unit is used for subdividing each index value into a plurality of intervals, and performing index value horizontal sequencing according to the credit quality of the index, wherein the better the credit quality of the index is, the smaller the sequencing value of the corresponding interval is;
and the third calculation unit is used for calculating the index value horizontal score of the interval corresponding to the index by using a linear interpolation method according to the index distribution score lower limit, the index distribution score upper limit and the index value horizontal sequencing.
In a third aspect, the present invention also discloses a storage medium storing one or more computer readable programs, the one or more computer readable programs comprising instructions adapted to be loaded by a memory and to perform any of the above-described methods of scoring a scientific enterprise.
The method, the device and the storage medium for scoring the scientific enterprises based on the AHP method have the following beneficial effects:
first, the complex evaluation problem is decomposed into a plurality of layers and a plurality of factors, and an index evaluation system with the characteristics of a scientific enterprise is constructed, so that the evaluation process is more systematic and layered.
Secondly, the weight of each index is determined by introducing an AHP method, so that the subjectivity of expert evaluation is reduced, and the objectivity and accuracy of the evaluation are improved.
Thirdly, the score of each index is determined by using a linear interpolation method, and the qualitative evaluation of the expert is converted into a quantitative score, so that the subsequent scoring calculation and result display are facilitated.
Fourth, through technical means such as data acquisition and processing, scoring calculation, etc., comprehensive, objective and accurate evaluation of the scientific and creative enterprises is realized.
In conclusion, the method and the device can comprehensively, objectively and accurately score the scientific and creative enterprises.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a scoring method for a scientific-created enterprise according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of each weight vector of a class a indicator according to an embodiment of the present invention.
FIG. 3 is a graph of a class A indicator large class indicator value horizontal score distribution according to an embodiment of the present invention.
Fig. 4 is a comprehensive score display diagram of a scientific enterprise provided by an embodiment of the invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The use of ordinal terms "first," "second," "third," etc., to describe a generic object merely denotes different instances of like objects, and is not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.
In addition, the expression "comprising" an element is an "open" expression which merely means that there is a corresponding component or step and should not be interpreted as excluding the existence of additional components or steps.
To achieve the object of the present invention, in some embodiments of an AHP-based method for scoring a science-created enterprise, as shown in fig. 1, the method for scoring a science-created enterprise includes:
step S1: constructing an index evaluation system with characteristics of a scientific enterprise;
step S2: calculating the weight of each index in the index evaluation system by adopting an analytic hierarchy process;
step S3: calculating each index score in the index evaluation system by adopting a linear interpolation method based on the index weight in the step S2;
step S4: collecting related data related to scoring of a scientific and creative enterprise;
step S5: and calculating the comprehensive score of the scientific enterprise according to the weight and the score of each index in the index evaluation system, and displaying the result.
Further, each step is described in detail below.
The step S1 specifically comprises the following steps:
step S1.1: establishing a preliminary evaluation system with 40 evaluation indexes;
step S1.2: aiming at the importance degree and the true belief degree of 40 evaluation indexes, the experience of an expert is scored and summarized;
step S1.3: 21 evaluation indexes are selected from 40 evaluation indexes according to scoring summarization conditions of expert experience, and an index evaluation system with characteristics of a scientific enterprise is constructed.
Step S1 is to comprehensively consider multiple aspects of technical innovation capability, research and development capability, market competitiveness, enterprise operation capability and the like aiming at the characteristics of scientific enterprises, and construct a set of comprehensive and objective index evaluation system. Specific indicators include, but are not limited to: development investment ratio, patent number, market share, liability rate, main service growth rate, net profit growth rate, etc.
The step S2 specifically comprises the following steps:
step S2.1: calculating importance scores of all indexes in the index evaluation system according to scoring summarization conditions of expert experience;
step S2.2: according to the importance scores of the indexes, a scoring difference matrix of each index major class is constructed, and 5 index major classes are arranged in an index evaluation systemAnd the kth index subclass has n k A number of indices, k=1, 2 …,5;
element A in scoring difference matrix ij The importance score difference value between the ith index and the jth index in the corresponding index large class is obtained;
wherein: i=1, 2 …, n k ；
j＝1,2…,n k ；
Step S2.3: according to the scoring difference matrix of each index major class, constructing an AHP comparison matrix of each index major class, and constructing an element B in the AHP comparison matrix ij The comparison value between the ith index and the jth index in the corresponding index class is specifically judged as follows;
if element A ij Not less than 0, then B ij ＝int(A ij )+1；
If element A ij <0, then B ij ＝1/(int(-A ij )+1)；
int is a rounding function;
step S2.4: adjusting an AHP comparison matrix of each index major class according to expert experience;
step S2.4: carrying out normalization processing on the AHP comparison matrix, and calculating based on the normalized matrix to obtain weight vectors of all indexes of each index major class;
step S2.5: and calculating the consistency index CI and the random consistency ratio CR of the AHP judgment matrix of each index class, checking whether the consistency of the AHP judgment matrix of each index class meets the requirement, and if not, readjusting the corresponding AHP judgment matrix until the requirement is met.
Specifically, the index evaluation system has 5 index categories, A, B, C, D, E respectively. Taking the class a business capability as an example, the class a index major class has 6 indices, which are respectively an asset liability rate, a sales liability rate, an revenue growth rate, a net profit growth rate, a total asset growth rate, and a liquidity rate.
The step S2 comprises the following steps:
step S2.1: calculating importance scores of all indexes of the class A index major class according to scoring summarization conditions of expert experience, as shown in a table 1;
table 1 importance score table of A class index categories
Step S2.2: constructing a scoring difference matrix of a class A index major class according to the importance scores of the indexes, as shown in a table 2;
table 2 score difference matrix table (showing partial values) for the class A index categories
| Scoring difference matrix | A-3 | A-4 | A-5 | A-6 | A-7 | A-8 |
| A-3 | 1.5000 | -0.5000 | -1.0000 | 0.5000 | 1.0000 | |
| A-4 | -2.0000 | -2.5000 | -1.0000 | -0.5000 | ||
| A-5 | -0.5000 | 1.0000 | 1.5000 | |||
| A-6 | 1.5000 | 2.0000 | ||||
| A-7 | 0.5000 | |||||
| A-8 |
Step S2.3: constructing an AHP comparison matrix of the class A index major class according to the grading difference matrix of each index major class, as shown in Table 3;
table 3 AHP comparison matrix table of A class index categories
| Calculation based on raw data | A-3 | A-4 | A-5 | A-6 | A-7 | A-8 |
| A-3 | 1 | 2 | 1 | 0.5 | 1 | 2 |
| A-4 | 0.5 | 1 | 0.33333333 | 0.333333 | 0.5 | 1 |
| A-5 | 1 | 3 | 1 | 1 | 2 | 2 |
| A-6 | 2 | 3 | 1 | 1 | 2 | 3 |
| A-7 | 1 | 2 | 0.5 | 0.5 | 1 | 1 |
| A-8 | 0.5 | 1 | 0.5 | 0.333333 | 1 | 1 |
Step S2.4: adjusting an AHP comparison matrix of the class A index major class according to expert experience, as shown in a table 4;
TABLE 4 AHP comparison matrix Table of class A index categories after adjustment
| Expert experience adjustment | A-3 | A-4 | A-5 | A-6 | A-7 | A-8 |
| A-3 | 1 | 2 | 1 | 0.5 | 1 | 2 |
| A-4 | 0.5 | 1 | 0.33333333 | 0.5 | 0.5 | 1 |
| A-5 | 1 | 3 | 1 | 1 | 2 | 2 |
| A-6 | 2 | 2 | 1 | 1 | 2 | 3 |
| A-7 | 1 | 2 | 0.5 | 0.5 | 1 | 1 |
| A-8 | 0.5 | 1 | 0.5 | 0.333333 | 1 | 1 |
Step S2.4: normalizing the AHP comparison matrix to make the sum of each column be 1, obtaining a column normalization matrix, as shown in table 5, dividing the sum of each row of the column normalization matrix by the index variable number, and obtaining weight vectors of all indexes of class A indexes, as shown in table 6;
table 5 normalized matrix table of A class index categories
| Column normalization matrix | A-3 | A-4 | A-5 | A-6 | A-7 | A-8 |
| A-3 | 0.1667 | 0.1818 | 0.2308 | 0.1304 | 0.1333 | 0.2000 |
| A-4 | 0.0833 | 0.0909 | 0.0769 | 0.1304 | 0.0667 | 0.1000 |
| A-5 | 0.1667 | 0.2727 | 0.2308 | 0.2609 | 0.2667 | 0.2000 |
| A-6 | 0.3333 | 0.1818 | 0.2308 | 0.2609 | 0.2667 | 0.3000 |
| A-7 | 0.1667 | 0.1818 | 0.1154 | 0.1304 | 0.1333 | 0.1000 |
| A-8 | 0.0833 | 0.0909 | 0.1154 | 0.0870 | 0.1333 | 0.1000 |
Table 6 and A index weight vector table for index major class
Step S2.5: and calculating the consistency index CI and the random consistency ratio CR of the AHP judgment matrix of the class A index class, and checking whether the consistency meets the requirement, if CR is smaller than 0.1, considering that the AHP judgment matrix has satisfactory consistency, otherwise, readjusting the AHP judgment matrix is needed, as shown in tables 7 and 8.
TABLE 7 uniformity index CI and random uniformity ratio CR table for A class index broad
Table 8 table of consistency check reference values
Further, step S3 includes:
step S3.1: analyzing scoring summarization conditions of expert experience in the step S1 to obtain weight vectors of all index major categories;
the weight vector of the index major class is obtained by scoring and summarizing according to expert questionnaires, and then the weight vector is obtained by calculating according to the AHP method. (calculation method of weight vector of each index in class A index);
summarizing and counting the index weight vectors in the step S2 to obtain weight vectors of which each index occupies a corresponding index major class;
the weight vector of each index accounting for all indexes is obtained through the following calculation;
b ki ＝a ki *a k ；
wherein: a, a k A weight vector which is the kth index major class;
a ki the ith index of the kth index large class occupies the weight vector of the kth index large class;
b ki the ith index which is the kth index major class occupies weight vectors of all indexes;
k＝1,2…,5；
i＝1,2,…,n k ；
taking the first index (A-3) of class A as an example, b ki ＝a ki *a k ＝17.38％*32.30％＝5.61％；
Step S3.2: setting the upper limit E of score card score max (e.g. 800), lower score E min (e.g., 400), scoring card score weight E (e.g., 400);
step S3.3: calculating the weight of each index distribution score, the lower limit of the index distribution score and the upper limit of the index distribution score through the following steps;
e ki ＝b ki *E；
e min-ki ＝E min /21；
e max-ki ＝e ki +e min-ki ；
wherein: e, e ki Assigning a score weight to the ith index of the kth index major class;
e min-ki assigning a lower score limit to the ith index of the kth index class;
e max-ki assigning an upper score limit to the ith index of the kth index class;
k＝1,2…,5；
i＝1,2,…,n k ；
taking the first index of class a (a-3) as an example,
e ki ＝b ki *E＝5.61％*400＝22.46；
e min-ki ＝E min /21＝19.35；
e max-ki ＝e ki +e min-ki ＝22.46+19.25＝41.15；
step S3.4: subdividing each index value into 6 intervals, and carrying out index value horizontal sorting according to the credit quality of the index, wherein the better the credit quality of the index is, the smaller the sorting value of the corresponding interval is;
step S3.5: and calculating the index value horizontal score of the index corresponding section by using a linear interpolation method according to the index distribution score lower limit, the index distribution score upper limit and the index value horizontal sequencing.
The index value horizontal score of the corresponding interval is as follows:
upper limit of index allocation score- (upper limit of index allocation score-lower limit of index allocation score)/(maximum value of index sequencing number-1) in the corresponding interval sequencing number-1;
specifically, taking the class a enterprise business capability as an example, each weight vector is shown in fig. 2, and the index value horizontal score is distributed as shown in fig. 3.
Taking the interval that the index value level of A-3 is less than 0.2 as an example, the index value level score is as follows:
34.47-(34.47-19.05)*0≈34；
taking the index value level [0.2,0.30 interval of A-3 as an example, the index value level score is:
34.47- (34.47-19.05) 1/4-31, etc.
Further, step S4 specifically includes: and collecting relevant data such as financial reports, patents, market competitiveness and the like of the scientific enterprises, and cleaning, processing and standardizing the data so as to comprehensively evaluate the data. Specific methods include data cleaning, data transformation, data normalization, and the like.
Further, step S5 specifically includes: and after the data acquisition is completed, calculating the comprehensive score of the scientific and creative enterprise according to the weight and the score of each index. And the comprehensive score is displayed in the form of a radar chart, so that a user can conveniently understand and compare evaluation results of different department and creation enterprises, as shown in fig. 4, comprehensive, objective and accurate evaluation of the department and creation enterprises is realized, and beneficial reference and guidance are provided for investment decision-making, risk management, policy formulation and the like.
In some embodiments of the AHP method-based scientific-enterprise scoring apparatus, the scientific-enterprise scoring apparatus includes:
the index evaluation system construction module is used for constructing an index evaluation system with the characteristics of a scientific enterprise;
the index weight calculation module is used for calculating the weight of each index in the index evaluation system by adopting an analytic hierarchy process;
the index score calculating module is used for calculating each index score in the index evaluation system by adopting a linear interpolation method based on the index weight obtained by the index weight calculating module;
the data acquisition module is used for acquiring related data related to scoring of the scientific and invasive enterprises;
and the comprehensive scoring module is used for calculating the comprehensive score of the scientific enterprise according to the weight and the score of each index in the index evaluation system and displaying the result.
Further, the index evaluation system construction module specifically includes:
a preliminary evaluation system establishing unit for establishing a preliminary evaluation system having M evaluation indexes;
the scoring summarization unit is used for scoring summarization of expert experience aiming at the importance degree and the true belief degree of the M evaluation indexes;
the index selection unit is used for selecting N evaluation indexes from M evaluation indexes according to scoring summarization conditions of expert experience, and constructing an index evaluation system with characteristics of a scientific enterprise, wherein N is less than or equal to M.
Further, the index weight calculation module specifically includes:
the importance score calculating unit is used for calculating the importance scores of the indexes in the index evaluation system according to the scoring summarization condition of expert experience;
a scoring difference matrix construction unit for constructing a scoring difference matrix of each index major class according to the importance scores of the indexes, K index major classes are arranged in the index evaluation system,and the kth index subclass has n k A number of indices, k=1, 2 …, K;
element A in scoring difference matrix ij The importance score difference value between the ith index and the jth index in the corresponding index large class is obtained;
wherein: i=1, 2 …, n k ；
j＝1,2…,n k ；
An AHP comparison matrix construction unit for constructing an AHP comparison matrix of each index major class according to the scoring difference matrix of each index major class, wherein the elements B in the AHP comparison matrix are as follows ij The comparison value between the ith index and the jth index in the corresponding index class is specifically judged as follows;
if element A ij Not less than 0, then B ij ＝int(A ij )+1；
If element A ij <0, then B ij ＝1/(int(-A ij )+1)；
int is a rounding function;
the AHP comparison matrix adjustment unit is used for adjusting the AHP comparison matrix of each index class;
the index weight vector construction unit is used for carrying out normalization processing on the AHP comparison matrix and calculating based on the normalized matrix to obtain weight vectors of all indexes of each index class;
the checking unit is used for calculating the consistency index CI and the random consistency ratio CR of the AHP judgment matrix of each index class, checking whether the consistency of the AHP judgment matrix of each index class meets the requirement, and if not, readjusting the corresponding AHP judgment matrix until the requirement is met.
Further, the index score calculating module specifically includes:
the first calculation unit is used for analyzing scoring summarization conditions of expert experiences obtained by the index evaluation system construction module to obtain weight vectors of all index major categories;
summarizing and counting the weight vectors of the indexes obtained by the index weight calculation module to obtain weight vectors of the indexes occupying the corresponding index major class;
the weight vector of each index accounting for all indexes is obtained through the following calculation;
b ki ＝a ki *a k ；
wherein: a, a k A weight vector which is the kth index major class;
a ki the ith index of the kth index large class occupies the weight vector of the kth index large class;
b ki the ith index which is the kth index major class occupies weight vectors of all indexes;
k＝1,2…,K；
i＝1,2,…,n k ；
a setting unit for setting the upper limit E of the score card max Lower score limit E of score card min Score card score weight E, e=e max -E min ；
The second calculation unit is used for calculating each index allocation score weight, an index allocation score lower limit and an index allocation score upper limit through the following steps;
e ki ＝b ki *E；
e min-ki ＝E min /N；
e max-ki ＝e ki +e min-ki ；
wherein: e, e ki Assigning a score weight to the ith index of the kth index major class;
e min-ki assigning a lower score limit to the ith index of the kth index class;
e max-ki assigning an upper score limit to the ith index of the kth index class;
k＝1,2…,K；
i＝1,2,…,n k ；
the horizontal sequencing unit is used for subdividing each index value into a plurality of intervals, and performing index value horizontal sequencing according to the credit quality of the index, wherein the better the credit quality of the index is, the smaller the sequencing value of the corresponding interval is;
and the third calculation unit is used for calculating the index value horizontal score of the interval corresponding to the index by using a linear interpolation method according to the index distribution score lower limit, the index distribution score upper limit and the index value horizontal sequencing.
Note that, the technical scheme related to the scoring device of the scientific and invasive enterprise is similar to the scoring method of the scientific and invasive enterprise, and will not be described here again.
The embodiment of the invention also discloses a storage medium, which stores one or more computer readable programs, wherein the one or more computer readable programs comprise instructions, and the instructions are suitable for loading by a memory and executing the grading method of the scientific and invasive enterprises disclosed in any embodiment.
The method, the device and the storage medium for scoring the scientific enterprises based on the AHP method have the following beneficial effects:
first, the complex evaluation problem is decomposed into a plurality of layers and a plurality of factors, and an index evaluation system with the characteristics of a scientific enterprise is constructed, so that the evaluation process is more systematic and layered.
Secondly, the weight of each index is determined by introducing an AHP method, so that the subjectivity of expert evaluation is reduced, and the objectivity and accuracy of the evaluation are improved.
Thirdly, the score of each index is determined by using a linear interpolation method, and the qualitative evaluation of the expert is converted into a quantitative score, so that the subsequent scoring calculation and result display are facilitated.
Fourth, through technical means such as data acquisition and processing, scoring calculation, etc., comprehensive, objective and accurate evaluation of the scientific and creative enterprises is realized.
In conclusion, the method and the device can comprehensively, objectively and accurately score the scientific and creative enterprises.
It should be understood that the various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions of the methods and apparatus of the present invention, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
While the basic principles and main features of the present invention and advantages of the present invention have been shown and described, it will be understood by those skilled in the art that the present invention is not limited by the foregoing embodiments, which are described in the foregoing specification merely illustrate the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined in the appended claims and their equivalents.
Claims (9)
1. The scoring method for the scientific and creative enterprises based on the AHP method is characterized by comprising the following steps of:
step S1: constructing an index evaluation system with characteristics of a scientific enterprise;
step S2: calculating the weight of each index in the index evaluation system by adopting an analytic hierarchy process;
step S3: calculating each index score in the index evaluation system by adopting a linear interpolation method based on the index weight in the step S2;
step S4: collecting related data related to scoring of a scientific and creative enterprise;
step S5: and calculating the comprehensive score of the scientific enterprise according to the weight and the score of each index in the index evaluation system, and displaying the result.
2. The method for scoring a scientific enterprise according to claim 1, wherein the step S1 specifically comprises:
step S1.1: establishing a preliminary evaluation system with M evaluation indexes;
step S1.2: aiming at the importance degree and the true belief degree of M evaluation indexes, the experience of an expert is scored and summarized;
step S1.3: according to scoring summarization conditions of expert experience, N evaluation indexes are selected from M evaluation indexes, and an index evaluation system with characteristics of a scientific enterprise is constructed, wherein N is less than or equal to M.
3. The method for scoring a scientific enterprise according to claim 2, wherein the step S2 specifically comprises:
step S2.1: calculating importance scores of all indexes in the index evaluation system according to scoring summarization conditions of expert experience;
step S2.2: according to the importance scores of the indexes, a scoring difference matrix of each index major class is constructed, K index major classes are arranged in an index evaluation system, and the kth index major class is provided with n k A number of indices, k=1, 2 …, K;
element A in scoring difference matrix ij The importance score difference value between the ith index and the jth index in the corresponding index large class is obtained;
wherein: i=1, 2 …, n k ；
j＝1,2…,n k ；
Step S2.3: according to the scoring difference matrix of each index major class, constructing an AHP comparison matrix of each index major class, and constructing an element B in the AHP comparison matrix ij The comparison value between the ith index and the jth index in the corresponding index class is specifically judged as follows;
if element A ij Not less than 0, then B ij ＝int(A ij )+1；
If element A ij <0, then B ij ＝1/(int(-A ij )+1)；
int is a rounding function;
step S2.4: adjusting an AHP comparison matrix of each index major class;
step S2.4: carrying out normalization processing on the AHP comparison matrix, and calculating based on the normalized matrix to obtain weight vectors of all indexes of each index major class;
step S2.5: and calculating the consistency index CI and the random consistency ratio CR of the AHP judgment matrix of each index class, checking whether the consistency of the AHP judgment matrix of each index class meets the requirement, and if not, readjusting the corresponding AHP judgment matrix until the requirement is met.
4. The method for scoring a scientific enterprise according to claim 3, wherein the step S3 specifically includes:
step S3.1: analyzing scoring summarization conditions of expert experience in the step S1 to obtain weight vectors of all index major categories;
summarizing and counting the index weight vectors in the step S2 to obtain weight vectors of which each index occupies a corresponding index major class;
the weight vector of each index accounting for all indexes is obtained through the following calculation;
b ki ＝a ki *a k ；
wherein: a, a k A weight vector which is the kth index major class;
a ki the ith index of the kth index large class occupies the weight vector of the kth index large class;
b ki the ith index which is the kth index major class occupies weight vectors of all indexes;
k＝1,2…,K；
i＝1,2,…,n k ；
step S3.2: setting the upper limit E of score card score max Lower score limit E of score card min Score card score weight E, e=e max -E min ；
Step S3.3: calculating the weight of each index distribution score, the lower limit of the index distribution score and the upper limit of the index distribution score through the following steps;
e ki ＝b ki *E；
e min-ki ＝E min /N；
e max-ki ＝e ki +e min-ki ；
wherein: e, e ki Assigning a score weight to the ith index of the kth index major class;
e min-ki assigning a lower score limit to the ith index of the kth index class;
e max-ki assigning an upper score limit to the ith index of the kth index class;
k＝1,2…,K；
i＝1,2,…,n k ；
step S3.4: subdividing each index value into a plurality of intervals, and carrying out index value horizontal sorting according to the credit quality of the index, wherein the better the credit quality of the index is, the smaller the sorting numerical value of the corresponding interval is;
step S3.5: and calculating the index value horizontal score of the index corresponding section by using a linear interpolation method according to the index distribution score lower limit, the index distribution score upper limit and the index value horizontal sequencing.
5. The scoring equipment for the scientific enterprises based on the AHP method is characterized by comprising the following steps:
the index evaluation system construction module is used for constructing an index evaluation system with the characteristics of a scientific enterprise;
the index weight calculation module is used for calculating the weight of each index in the index evaluation system by adopting an analytic hierarchy process;
the index score calculating module is used for calculating each index score in the index evaluation system by adopting a linear interpolation method based on the index weight obtained by the index weight calculating module;
the data acquisition module is used for acquiring related data related to scoring of the scientific and invasive enterprises;
and the comprehensive scoring module is used for calculating the comprehensive score of the scientific enterprise according to the weight and the score of each index in the index evaluation system and displaying the result.
6. The grading apparatus of claim 5, wherein the index evaluation system construction module specifically comprises:
a preliminary evaluation system establishing unit for establishing a preliminary evaluation system having M evaluation indexes;
the scoring summarization unit is used for scoring summarization of expert experience aiming at the importance degree and the true belief degree of the M evaluation indexes;
the index selection unit is used for selecting N evaluation indexes from M evaluation indexes according to scoring summarization conditions of expert experience, and constructing an index evaluation system with characteristics of a scientific enterprise, wherein N is less than or equal to M.
7. The device for scoring a scientific enterprise according to claim 6, wherein the index weight calculation module specifically comprises:
the importance score calculating unit is used for calculating the importance scores of the indexes in the index evaluation system according to the scoring summarization condition of expert experience;
the scoring difference matrix construction unit is used for constructing the scoring difference matrix of each index major class according to the importance scores of the indexes, K index major classes are arranged in the index evaluation system, and the kth index major class is provided with n k A number of indices, k=1, 2 …, K;
element A in scoring difference matrix ij The importance score difference value between the ith index and the jth index in the corresponding index large class is obtained;
wherein: i=1, 2 …, n k ；
j＝1,2…,n k ；
An AHP comparison matrix construction unit for constructing an AHP comparison matrix of each index major class according to the scoring difference matrix of each index major class, wherein the elements B in the AHP comparison matrix are as follows ij The comparison value between the ith index and the jth index in the corresponding index class is specifically judged as follows;
if element A ij Not less than 0, then B ij ＝int(A ij )+1；
If element A ij <0, then B ij ＝1/(int(-A ij )+1)；
int is a rounding function;
the AHP comparison matrix adjustment unit is used for adjusting the AHP comparison matrix of each index class;
the index weight vector construction unit is used for carrying out normalization processing on the AHP comparison matrix and calculating based on the normalized matrix to obtain weight vectors of all indexes of each index class;
the checking unit is used for calculating the consistency index CI and the random consistency ratio CR of the AHP judgment matrix of each index class, checking whether the consistency of the AHP judgment matrix of each index class meets the requirement, and if not, readjusting the corresponding AHP judgment matrix until the requirement is met.
8. The device for scoring a scientific-invasive enterprise according to claim 7, wherein the index score calculation module specifically comprises:
the first calculation unit is used for analyzing scoring summarization conditions of expert experiences obtained by the index evaluation system construction module to obtain weight vectors of all index categories;
summarizing and counting the weight vectors of the indexes obtained by the index weight calculation module to obtain weight vectors of the indexes occupying the corresponding index major class;
the weight vector of each index accounting for all indexes is obtained through the following calculation;
b ki ＝a ki *a k ；
wherein: a, a k A weight vector which is the kth index major class;
a ki the ith index of the kth index large class occupies the weight vector of the kth index large class;
b ki the ith index which is the kth index major class occupies weight vectors of all indexes;
k＝1,2…,K；
i＝1,2,…,n k ；
a setting unit for setting the upper limit E of the score card max Lower score limit E of score card min Score card score weight E, e=e max -E min ；
The second calculation unit is used for calculating each index allocation score weight, an index allocation score lower limit and an index allocation score upper limit through the following steps;
e ki ＝b ki *E；
e min-ki ＝E min /N；
e max-ki ＝e ki +e min-ki ；
wherein: e, e ki Assigning a score weight to the ith index of the kth index major class;
e min-ki assigning a lower score limit to the ith index of the kth index class;
e max-ki assigning an upper score limit to the ith index of the kth index class;
k＝1,2…,K；
i＝1,2,…,n k ；
the horizontal sequencing unit is used for subdividing each index value into a plurality of intervals, and performing index value horizontal sequencing according to the credit quality of the index, wherein the better the credit quality of the index is, the smaller the sequencing value of the corresponding interval is;
and the third calculation unit is used for calculating the index value horizontal score of the interval corresponding to the index by using a linear interpolation method according to the index distribution score lower limit, the index distribution score upper limit and the index value horizontal sequencing.
9. A storage medium having one or more computer-readable programs stored thereon, the one or more computer-readable programs comprising instructions adapted to be loaded from a memory and to perform the method of scoring a scientific enterprise as claimed in any one of claims 1 to 4.
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