CN119006142A - Construction enterprise credit evaluation method and system - Google Patents
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Abstract
本申请涉及施工企业信用技术领域，公开了一种施工企业信用评价方法及系统，该方法包括：建立多个信用评价指标；根据信用评价指标生成多个初始信用评价模型，并建立信用评价指标与初始信用评价模型的对应关系；根据对应关系确定每个信用评价指标在预设时段的初始信用评价值，根据多个初始信用评价值生成信用评价值，根据信用评价值生成对应施工企业的信用等级，本申请通过多个信用评价指标对应的信用相关数据生成多个初始信用评价模型，根据初始信用评价模型得到初始信用评价值，根据多个初始信用评价值得到对应施工企业的信用评价值，并得到对应的信用等级，避免人为误差，提高施工企业的信用评价的精确性，从而保证施工的工作质量和水平。
The present application relates to the technical field of construction enterprise credit, and discloses a construction enterprise credit evaluation method and system, the method comprising: establishing a plurality of credit evaluation indicators; generating a plurality of initial credit evaluation models according to the credit evaluation indicators, and establishing a corresponding relationship between the credit evaluation indicators and the initial credit evaluation models; determining the initial credit evaluation value of each credit evaluation indicator in a preset time period according to the corresponding relationship, generating a credit evaluation value according to the plurality of initial credit evaluation values, and generating a credit grade of the corresponding construction enterprise according to the credit evaluation value. The present application generates a plurality of initial credit evaluation models through credit-related data corresponding to a plurality of credit evaluation indicators, obtains an initial credit evaluation value according to the initial credit evaluation model, obtains a credit evaluation value of the corresponding construction enterprise according to the plurality of initial credit evaluation values, and obtains a corresponding credit grade, thereby avoiding human errors, improving the accuracy of the credit evaluation of the construction enterprise, and thus ensuring the quality and level of construction work.
Description
Technical Field
The application relates to the technical field of construction enterprise credit, in particular to a construction enterprise credit evaluation method and system.
Background
By greatly promoting the informatization work of railway engineering construction, the construction informatization management of the on-site railway projects in China is basically realized. Based on the actual application condition of the railway engineering management platform, the problems existing in the pushing process of the railway engineering construction informatization are summarized, and further, the credit evaluation management requirements of construction enterprises are analyzed, so that the credit level of the construction enterprises of the railway construction projects is improved.
In the prior art, the credit evaluation management system and tools for construction enterprises are lacking, the credit evaluation of the construction enterprises is mostly manually checked and supervised, the objectivity and authority of the evaluation are lacking, the working quality and the level of the construction cannot be ensured, and the construction quality of railway construction projects is reduced.
Disclosure of Invention
In order to solve the technical problems, the application provides a credit evaluation method and a credit evaluation system for construction enterprises, wherein a plurality of credit evaluation indexes are established, a plurality of initial credit evaluation models are generated according to the credit evaluation indexes and corresponding credit related data, the initial credit evaluation values are obtained according to the initial credit evaluation models corresponding to the credit evaluation indexes, the credit evaluation values of the corresponding construction enterprises are obtained according to the plurality of initial credit evaluation values, the corresponding credit grades are obtained, human errors are avoided, the accuracy of the credit evaluation of the construction enterprises is improved, and therefore the working quality and the working level of construction are guaranteed.
In some embodiments of the present application, there is provided a construction enterprise credit evaluation method, including:
Establishing a plurality of credit evaluation indexes;
Generating a plurality of initial credit evaluation models according to the credit evaluation indexes, and establishing corresponding relations between the credit evaluation indexes and the initial credit evaluation models;
And determining an initial credit evaluation value of each credit evaluation index in a preset period according to the corresponding relation, generating credit evaluation values of corresponding construction enterprises according to the plurality of initial credit evaluation values, and generating credit grades of the corresponding construction enterprises according to the credit evaluation values.
In some embodiments of the application, generating a plurality of initial credit rating models includes:
Acquiring credit related data of a single credit evaluation index in a preset period, and screening out characteristic parameters of each credit related data;
generating training set data and testing set data according to the historical credit related data and the characteristic parameters;
Performing iterative training according to the training set data to generate an initial credit evaluation model, and generating the credibility of the initial credit evaluation model according to the testing set data;
Presetting a credibility threshold;
If the credibility of the initial credit evaluation model is smaller than the credibility threshold value, iterating again;
if the credibility of the initial credit evaluation model is larger than the credibility threshold, generating an index label of the initial evaluation model;
And establishing a credit evaluation index-index label mapping table, and generating a corresponding relation between the credit evaluation index and an initial credit evaluation model according to the credit evaluation index-index label mapping table.
In some embodiments of the present application, determining an initial credit rating value of each credit rating index in a preset period according to a correspondence relation includes:
Obtaining a credit evaluation index number sequence M, M= (M1, M2, … Mn), wherein n is the credit evaluation index number, and M i is the ith credit evaluation index;
acquiring an initial credit evaluation model array C, C= (C1, C2, … Cn), wherein Ci is the ith initial credit evaluation model;
setting a credit evaluation direction according to a preset performance rule corresponding to a construction enterprise, and acquiring a corresponding credit evaluation index;
If the credit evaluation index is M i, setting an initial credit evaluation model corresponding to the credit evaluation index M i as a first credit evaluation model;
acquiring a first credibility e of a first credit evaluation model, and setting the sampling group number r of credit related data according to the first credibility e;
Preprocessing each group of credit related data to generate a plurality of groups of evaluation data;
Generating a first credit evaluation value a1 according to a plurality of groups of evaluation data and the first credit evaluation model, and setting a first weight coefficient e1 according to the first credibility e;
an initial credit evaluation value D, d=e1×a1, is generated from the first credit evaluation value a1 and the first weight coefficient e 1.
In some embodiments of the application, setting the number of samples r of credit-related data according to the first confidence level e comprises:
presetting a first preset credibility interval, a second preset credibility interval and a third preset credibility interval;
if the first confidence level e is in the first preset confidence level interval, setting the sampling group number r as a first preset sampling group number r1, namely r=r1;
if the first confidence level e is in the second preset confidence level interval, setting the sampling group number r as a second preset sampling group number r2, namely r=r2;
if the first confidence level e is in the third preset confidence level interval, the number r of sample sets is set to be the third preset number r3 of sample sets, i.e. r=r3, and r1> r2> r3.
In some embodiments of the present application, generating multiple sets of evaluation data includes:
generating a repetition degree evaluation value H of each group of credit-related data, and establishing a repetition degree evaluation value sequence H, H= (H1, H2 … hm), wherein m is the sampling group number, and H i is the repetition degree evaluation value of the ith group of credit-related data;
Presetting a repetition degree evaluation value threshold;
If h i is larger than the repetition degree evaluation value threshold, eliminating the ith group of credit related data;
and generating multiple groups of evaluation data according to the eliminating result.
In some embodiments of the present application, generating a credit rating value for a construction enterprise from a plurality of initial credit rating values includes:
Generating an initial credit evaluation value sequence D, D= (D1, D2, … Dn) according to all the evaluation data and all the initial credit evaluation models, wherein D i is an ith initial credit evaluation value;
establishing a weighting coefficient array G, G= (G1, G2 … gn) according to the initial credit evaluation model, wherein G i is the weighting coefficient of the ith initial credit evaluation value, and
Generating a credit evaluation value K according to the initial credit evaluation value sequence D and the weighting coefficient sequence G;
In some embodiments of the present application, a second weight coefficient e2 is set according to a preset evaluation factor, the credit rating value is modified according to the second weight coefficient e2, and the credit rating of the corresponding construction enterprise is generated according to the modified credit rating value.
In some embodiments of the present application, setting the second weight coefficient e2 according to a preset evaluation factor includes:
quantifying the preset evaluation factors related to the construction enterprises according to the influence degree of the preset evaluation factors and the weight coefficients corresponding to the preset evaluation factors to generate quantized values;
Presetting a first preset quantized value interval, a second preset quantized value interval, a third preset quantized value interval and a fourth preset quantized value interval;
If the quantized value is in the first preset quantized value interval, selecting the first preset coefficient s1 as the second weight coefficient e2, namely e2=s1, and the corrected credit evaluation value is s1 x K;
If the quantized value is in the second preset quantized value interval, selecting a second preset coefficient s2 as a second weight coefficient e2, namely e2=s2, and the corrected credit evaluation value is s2 x K;
if the quantized value is in the third preset quantized value interval, selecting a third preset coefficient s3 as a second weight coefficient e2, namely e2=s3, and the corrected credit evaluation value is s3 x K;
If the quantized value is in the fourth preset quantized value interval, selecting a fourth preset coefficient s4 as a second weight coefficient e2, namely e2=s4, and modifying the credit evaluation value to be s4 x K;
wherein s1 is more than 1 and s2 is more than 3 and s4 is more than 1.25.
In some embodiments of the present application, generating a credit rating for a construction enterprise according to the modified credit rating value includes:
presetting a first preset credit evaluation threshold and a second preset credit evaluation threshold, wherein the first preset credit evaluation threshold is smaller than the second preset credit evaluation threshold;
When the credit evaluation value is smaller than a first preset credit evaluation threshold value, setting the credit grade of the construction enterprise as the first preset credit grade;
setting the credit rating of the construction enterprise as a second preset credit rating when the credit rating value is between the first preset credit rating threshold and the second preset credit rating threshold;
and when the credit evaluation value is larger than the second preset credit evaluation threshold value, setting the credit grade of the construction enterprise as a third preset credit grade.
In some embodiments of the present application, a construction enterprise credit rating system is further included:
the establishing module is used for establishing a plurality of credit evaluation indexes;
the generation module is used for generating a plurality of initial credit evaluation models according to the credit evaluation indexes and establishing the corresponding relation between the credit evaluation indexes and the initial credit evaluation models;
And the evaluation module is used for determining initial credit evaluation values of each credit evaluation index in a preset period according to the corresponding relation, generating credit evaluation values of corresponding construction enterprises according to the plurality of initial credit evaluation values, and generating credit grades of the corresponding construction enterprises according to the credit evaluation values.
Compared with the prior art, the credit evaluation method and system for construction enterprises have the beneficial effects that:
by establishing a plurality of credit evaluation indexes, generating a plurality of initial credit evaluation models according to the credit evaluation indexes and corresponding credit related data, obtaining initial credit evaluation values according to the initial credit evaluation models corresponding to the credit evaluation indexes, obtaining credit evaluation values of corresponding construction enterprises according to the plurality of initial credit evaluation values, obtaining corresponding credit grades, avoiding human errors, improving the accuracy of credit evaluation of the construction enterprises, and further guaranteeing the working quality and level of construction.
Drawings
Fig. 1 is a schematic flow chart of a construction enterprise credit evaluation method in a preferred embodiment of the application.
Detailed Description
The following describes in further detail the embodiments of the present application with reference to the drawings and examples. The following examples are illustrative of the application and are not intended to limit the scope of the application.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
As shown in fig. 1, a construction enterprise credit evaluation method according to a preferred embodiment of the present application includes:
step S101: establishing a plurality of credit evaluation indexes;
step S102: generating a plurality of initial credit evaluation models according to the credit evaluation indexes, and establishing corresponding relations between the credit evaluation indexes and the initial credit evaluation models;
Step S103: and determining an initial credit evaluation value of each credit evaluation index in a preset period according to the corresponding relation, generating credit evaluation values of corresponding construction enterprises according to the plurality of initial credit evaluation values, and generating credit grades of the corresponding construction enterprises according to the credit evaluation values.
In the present embodiment, the credit evaluation index includes a daily check index, a bad behavior index, a performance assessment index, a complaint index, and the like.
In this embodiment, daily operation data, bad behavior data, performance assessment data, complaint data and the like in a preset period of a construction enterprise are automatically collected, and daily inspection, recording, displaying and publishing are performed on the construction enterprise, so that the credit of the construction enterprise is evaluated, a credit evaluation value is obtained, the credit grade of the construction enterprise is generated, construction responsibility is carefully fulfilled, and the working quality and level of construction are improved.
In some embodiments of the application, generating a plurality of initial credit rating models includes:
Acquiring credit related data of a single credit evaluation index in a preset period, and screening out characteristic parameters of each credit related data;
generating training set data and testing set data according to the historical credit related data and the characteristic parameters;
Performing iterative training according to the training set data to generate an initial credit evaluation model, and generating the credibility of the initial credit evaluation model according to the testing set data;
Presetting a credibility threshold;
If the credibility of the initial credit evaluation model is smaller than the credibility threshold value, iterating again;
if the credibility of the initial credit evaluation model is larger than the credibility threshold, generating an index label of the initial evaluation model;
And establishing a credit evaluation index-index label mapping table, and generating a corresponding relation between the credit evaluation index and an initial credit evaluation model according to the credit evaluation index-index label mapping table.
In this embodiment, by setting the credit evaluation index as the evaluation direction, performing evaluation modeling on the credit evaluation index one by one, establishing a plurality of construction evaluation scenes, completing the evaluation of each evaluation index of the construction enterprise credit, and generating an initial credit evaluation value according to the initial credit evaluation model, the initial credit evaluation of each credit evaluation index of the construction enterprise in a preset period is realized.
In this embodiment, corresponding feature parameters are generated according to different credit indexes, corresponding credit indexes and historical credit evaluation values are determined according to feature parameters related to historical credit related data, iterative training data are generated according to the historical credit related data feature parameters and the historical credit evaluation values, when the reliability of the model is greater than a preset reliability threshold, a corresponding initial credit evaluation model is generated, and the initial credit evaluation model corresponding to the different credit evaluation indexes is determined according to index labels.
In some embodiments of the present application, determining an initial credit rating value of each credit rating index in a preset period according to a correspondence relation includes:
Obtaining a credit evaluation index number sequence M, M= (M1, M2, … Mn), wherein n is the credit evaluation index number, and M i is the ith credit evaluation index;
acquiring an initial credit evaluation model array C, C= (C1, C2, … Cn), wherein Ci is the ith initial credit evaluation model;
setting a credit evaluation direction according to a preset performance rule corresponding to a construction enterprise, and acquiring a corresponding credit evaluation index;
If the credit evaluation index is M i, setting an initial credit evaluation model corresponding to the credit evaluation index M i as a first credit evaluation model;
acquiring a first credibility e of a first credit evaluation model, and setting the sampling group number r of credit related data according to the first credibility e;
Preprocessing each group of credit related data to generate a plurality of groups of evaluation data;
Generating a first credit evaluation value a1 according to a plurality of groups of evaluation data and the first credit evaluation model, and setting a first weight coefficient e1 according to the first credibility e;
an initial credit evaluation value D, d=e1×a1, is generated from the first credit evaluation value a1 and the first weight coefficient e 1.
In this embodiment, the preset performance rule is set according to the contract performance signed by the construction enterprise, the credit evaluation index is obtained according to the preset performance rule of the construction enterprise, the corresponding index label is obtained, so that the corresponding first credit evaluation model is determined, and the initial credit evaluation value of the corresponding credit evaluation index is generated according to the sampling array and the first credit evaluation model.
In this embodiment, when the initial credit evaluation value is larger, the credit evaluation value of the corresponding credit evaluation index of the current construction enterprise is higher, and when the initial credit evaluation value is smaller, the credit evaluation value of the corresponding credit evaluation index of the current construction enterprise is lower, and when the initial credit evaluation value is lower than a preset value, the construction enterprise needs to be reminded in time and the construction enterprise needs to be adjusted in time, so that the working quality and level of construction are improved.
In some embodiments of the application, setting the number of samples r of credit-related data according to the first confidence level e comprises:
presetting a first preset credibility interval, a second preset credibility interval and a third preset credibility interval;
if the first confidence level e is in the first preset confidence level interval, setting the sampling group number r as a first preset sampling group number r1, namely r=r1;
if the first confidence level e is in the second preset confidence level interval, setting the sampling group number r as a second preset sampling group number r2, namely r=r2;
if the first confidence level e is in the third preset confidence level interval, the number r of sample sets is set to be the third preset number r3 of sample sets, i.e. r=r3, and r1> r2> r3.
In this embodiment, the first preset confidence interval < the second preset confidence interval < the third preset confidence interval.
In this embodiment, when the preset confidence interval where the first confidence is located is lower, the number of sampling groups should be increased, so as to improve accuracy of credit related data, lay a foundation for obtaining a credit evaluation value later, improve accuracy of the credit evaluation value, and improve management level of construction enterprises.
In some embodiments of the present application, generating multiple sets of evaluation data includes:
generating a repetition degree evaluation value H of each group of credit-related data, and establishing a repetition degree evaluation value sequence H, H= (H1, H2 … hm), wherein m is the sampling group number, and H i is the repetition degree evaluation value of the ith group of credit-related data;
Presetting a repetition degree evaluation value threshold;
If h i is larger than the repetition degree evaluation value threshold, eliminating the ith group of credit related data;
and generating multiple groups of evaluation data according to the eliminating result.
In this embodiment, the evaluation process of each initial credit evaluation model is completed by adopting a slice calculation mode, the sample data space in the slice is dynamically adjusted, and the abnormal data is removed by adjusting the number of groups of the sampling array and preprocessing the data, so that the overall calculation accuracy is ensured.
In some embodiments of the present application, generating a credit rating value for a construction enterprise from a plurality of initial credit rating values includes:
Generating an initial credit evaluation value sequence D, D= (D1, D2, … Dn) according to all the evaluation data and all the initial credit evaluation models, wherein D i is an ith initial credit evaluation value;
establishing a weighting coefficient array G, G= (G1, G2 … gn) according to the initial credit evaluation model, wherein G i is the weighting coefficient of the ith initial credit evaluation value, and
Generating a credit evaluation value K according to the initial credit evaluation value sequence D and the weighting coefficient sequence G;
in this embodiment, a weighting coefficient array is generated according to the importance degree of the credit evaluation index corresponding to each initial credit evaluation model, so that weighting processing is performed on each initial credit evaluation value, comprehensive evaluation on the credit of the construction enterprise is realized, and the final evaluation precision is improved.
In some embodiments of the present application, a second weight coefficient e2 is set according to a preset evaluation factor, the credit rating value is modified according to the second weight coefficient e2, and the credit rating of the corresponding construction enterprise is generated according to the modified credit rating value.
In this embodiment, the preset evaluation factors include factors such as participation in rescue and relief work, special project construction, technological innovation, and high-speed rail opening standard evaluation.
In some embodiments of the present application, setting the second weight coefficient e2 according to a preset evaluation factor includes:
quantifying the preset evaluation factors related to the construction enterprises according to the influence degree of the preset evaluation factors and the weight coefficients corresponding to the preset evaluation factors to generate quantized values;
Presetting a first preset quantized value interval, a second preset quantized value interval, a third preset quantized value interval and a fourth preset quantized value interval;
If the quantized value is in the first preset quantized value interval, selecting the first preset coefficient s1 as the second weight coefficient e2, namely e2=s1, and the corrected credit evaluation value is s1 x K;
If the quantized value is in the second preset quantized value interval, selecting a second preset coefficient s2 as a second weight coefficient e2, namely e2=s2, and the corrected credit evaluation value is s2 x K;
if the quantized value is in the third preset quantized value interval, selecting a third preset coefficient s3 as a second weight coefficient e2, namely e2=s3, and the corrected credit evaluation value is s3 x K;
If the quantized value is in the fourth preset quantized value interval, selecting a fourth preset coefficient s4 as a second weight coefficient e2, namely e2=s4, and modifying the credit evaluation value to be s4 x K;
wherein s1 is more than 1 and s2 is more than 3 and s4 is more than 1.25.
In this embodiment, the first preset quantization value interval < the second preset quantization value interval < the third preset quantization value interval < the fourth preset quantization value interval.
In some embodiments of the present application, generating a credit rating for a construction enterprise according to the modified credit rating value includes:
presetting a first preset credit evaluation threshold and a second preset credit evaluation threshold, wherein the first preset credit evaluation threshold is smaller than the second preset credit evaluation threshold;
When the credit evaluation value is smaller than a first preset credit evaluation threshold value, setting the credit grade of the construction enterprise as the first preset credit grade;
setting the credit rating of the construction enterprise as a second preset credit rating when the credit rating value is between the first preset credit rating threshold and the second preset credit rating threshold;
and when the credit evaluation value is larger than the second preset credit evaluation threshold value, setting the credit grade of the construction enterprise as a third preset credit grade.
In some embodiments of the present application, a construction enterprise credit rating system is further included:
the establishing module is used for establishing a plurality of credit evaluation indexes;
the generation module is used for generating a plurality of initial credit evaluation models according to the credit evaluation indexes and establishing the corresponding relation between the credit evaluation indexes and the initial credit evaluation models;
And the evaluation module is used for determining initial credit evaluation values of each credit evaluation index in a preset period according to the corresponding relation, generating credit evaluation values of corresponding construction enterprises according to the plurality of initial credit evaluation values, and generating credit grades of the corresponding construction enterprises according to the credit evaluation values.
In summary, the application discloses a construction enterprise credit evaluation method and a construction enterprise credit evaluation system, wherein the method comprises the following steps: establishing a plurality of credit evaluation indexes; generating a plurality of initial credit evaluation models according to the credit evaluation indexes, and establishing corresponding relations between the credit evaluation indexes and the initial credit evaluation models; the method comprises the steps of establishing a plurality of credit evaluation indexes, generating a plurality of initial credit evaluation models according to the credit evaluation indexes and corresponding credit related data, obtaining the initial credit evaluation values according to the initial credit evaluation models corresponding to the credit evaluation indexes, obtaining the credit evaluation values of the corresponding construction enterprises according to the plurality of initial credit evaluation values, obtaining the corresponding credit grades, avoiding human errors, improving the accuracy of the credit evaluation of the construction enterprises, and guaranteeing the working quality and level of construction.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that modifications and substitutions can be made by those skilled in the art without departing from the technical principles of the present application, and these modifications and substitutions should also be considered as being within the scope of the present application.
Claims (10)
1. The construction enterprise credit evaluation method is characterized by comprising the following steps of:
Establishing a plurality of credit evaluation indexes;
Generating a plurality of initial credit evaluation models according to the credit evaluation indexes, and establishing corresponding relations between the credit evaluation indexes and the initial credit evaluation models;
And determining an initial credit evaluation value of each credit evaluation index in a preset period according to the corresponding relation, generating credit evaluation values of corresponding construction enterprises according to the plurality of initial credit evaluation values, and generating credit grades of the corresponding construction enterprises according to the credit evaluation values.
2. The construction enterprise credit evaluation method of claim 1, wherein generating a plurality of initial credit evaluation models comprises:
Acquiring credit related data of a single credit evaluation index in a preset period, and screening out characteristic parameters of each credit related data;
generating training set data and testing set data according to the historical credit related data and the characteristic parameters;
Performing iterative training according to the training set data to generate an initial credit evaluation model, and generating the credibility of the initial credit evaluation model according to the testing set data;
Presetting a credibility threshold;
If the credibility of the initial credit evaluation model is smaller than the credibility threshold value, iterating again;
if the credibility of the initial credit evaluation model is larger than the credibility threshold, generating an index label of the initial evaluation model;
And establishing a credit evaluation index-index label mapping table, and generating a corresponding relation between the credit evaluation index and an initial credit evaluation model according to the credit evaluation index-index label mapping table.
3. The construction enterprise credit evaluation method of claim 2, wherein determining the initial credit evaluation value of each credit evaluation index in the preset period according to the correspondence relation comprises:
Obtaining a credit evaluation index number sequence M, M= (M1, M2, … Mn), wherein n is the number of the credit evaluation indexes, and Mi is the ith credit evaluation index;
acquiring an initial credit evaluation model array C, C= (C1, C2, … Cn), wherein Ci is the ith initial credit evaluation model;
setting a credit evaluation direction according to a preset performance rule corresponding to a construction enterprise, and acquiring a corresponding credit evaluation index;
If the credit evaluation index is Mi, setting an initial credit evaluation model corresponding to the credit evaluation index Mi as a first credit evaluation model;
acquiring a first credibility e of a first credit evaluation model, and setting the sampling group number r of credit related data according to the first credibility e;
Preprocessing each group of credit related data to generate a plurality of groups of evaluation data;
Generating a first credit evaluation value a1 according to a plurality of groups of evaluation data and the first credit evaluation model, and setting a first weight coefficient e1 according to the first credibility e;
an initial credit evaluation value D, d=e1×a1, is generated from the first credit evaluation value a1 and the first weight coefficient e 1.
4. The construction enterprise credit evaluation method of claim 3, wherein setting the sampling group number r of the credit-related data according to the first confidence level e comprises:
presetting a first preset credibility interval, a second preset credibility interval and a third preset credibility interval;
if the first confidence level e is in the first preset confidence level interval, setting the sampling group number r as a first preset sampling group number r1, namely r=r1;
if the first confidence level e is in the second preset confidence level interval, setting the sampling group number r as a second preset sampling group number r2, namely r=r2;
if the first confidence level e is in the third preset confidence level interval, the number r of sample sets is set to be the third preset number r3 of sample sets, i.e. r=r1, and r1> r2> r3.
5. The construction enterprise credit evaluation method of claim 4, wherein generating multiple sets of evaluation data comprises:
Generating a repetition degree evaluation value H of each group of credit-related data, and establishing a repetition degree evaluation value sequence H, H= (H1, H2 … hm), wherein m is the sampling group number, and hi is the repetition degree evaluation value of the ith group of credit-related data;
Presetting a repetition degree evaluation value threshold;
If hi is greater than the threshold value of the repetition evaluation value, eliminating the ith group of credit related data;
and generating multiple groups of evaluation data according to the eliminating result.
6. The construction enterprise credit evaluation method of claim 5, wherein generating the credit evaluation value for the corresponding construction enterprise based on the plurality of initial credit evaluation values comprises:
Generating an initial credit evaluation value sequence D, D= (D1, D2, … Dn) according to all the evaluation data and all the initial credit evaluation models, wherein Di is the ith initial credit evaluation value;
establishing a weighting coefficient array G, G= (G1, G2 … gn) according to the initial credit evaluation model, wherein gi is the weighting coefficient of the ith initial credit evaluation value, and
Generating a credit evaluation value K according to the initial credit evaluation value sequence D and the weighting coefficient sequence G;
7. The construction enterprise credit evaluation method of claim 6, wherein a second weight coefficient e2 is set according to a preset evaluation factor, the credit evaluation value is modified according to the second weight coefficient e2, and the credit rating of the corresponding construction enterprise is generated according to the modified credit evaluation value.
8. The construction enterprise credit evaluation method of claim 7, wherein setting the second weight coefficient e2 according to the preset evaluation factor comprises:
quantifying the preset evaluation factors related to the construction enterprises according to the influence degree of the preset evaluation factors and the weight coefficients corresponding to the preset evaluation factors to generate quantized values;
Presetting a first preset quantized value interval, a second preset quantized value interval, a third preset quantized value interval and a fourth preset quantized value interval;
If the quantized value is in the first preset quantized value interval, selecting the first preset coefficient s1 as the second weight coefficient e2, namely e2=s1, and the corrected credit evaluation value is s1 x K;
If the quantized value is in the second preset quantized value interval, selecting a second preset coefficient s2 as a second weight coefficient e2, namely e2=s2, and the corrected credit evaluation value is s2 x K;
if the quantized value is in the third preset quantized value interval, selecting a third preset coefficient s3 as a second weight coefficient e2, namely e2=s3, and the corrected credit evaluation value is s3 x K;
If the quantized value is in the fourth preset quantized value interval, selecting a fourth preset coefficient s4 as a second weight coefficient e2, namely e2=s4, and modifying the credit evaluation value to be s4 x K;
wherein s1 is more than 1 and s2 is more than 3 and s4 is more than 1.25.
9. The construction enterprise credit evaluation method of claim 2, wherein generating the credit rating for the corresponding construction enterprise based on the corrected credit rating value comprises:
presetting a first preset credit evaluation threshold and a second preset credit evaluation threshold, wherein the first preset credit evaluation threshold is smaller than the second preset credit evaluation threshold;
When the credit evaluation value is smaller than a first preset credit evaluation threshold value, setting the credit grade of the construction enterprise as the first preset credit grade;
setting the credit rating of the construction enterprise as a second preset credit rating when the credit rating value is between the first preset credit rating threshold and the second preset credit rating threshold;
and when the credit evaluation value is larger than the second preset credit evaluation threshold value, setting the credit grade of the construction enterprise as a third preset credit grade.
10. A construction enterprise credit evaluation system, comprising:
the establishing module is used for establishing a plurality of credit evaluation indexes;
the generation module is used for generating a plurality of initial credit evaluation models according to the credit evaluation indexes and establishing the corresponding relation between the credit evaluation indexes and the initial credit evaluation models;
And the evaluation module is used for determining initial credit evaluation values of each credit evaluation index in a preset period according to the corresponding relation, generating credit evaluation values of corresponding construction enterprises according to the plurality of initial credit evaluation values, and generating credit grades of the corresponding construction enterprises according to the credit evaluation values.
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