CN111159630A - Park policy matching and evaluating method based on multi-standard decision model - Google Patents
Park policy matching and evaluating method based on multi-standard decision model Download PDFInfo
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Abstract
The invention discloses a campus policy matching and evaluating method based on a multi-standard decision model, which comprises the following steps: acquiring policy data, enterprise data and park data; preprocessing the policy data and the enterprise data, and respectively extracting a policy label and an enterprise label, wherein the policy label and the enterprise label comprise a quantitative label and a qualitative label; defining a matching rule and endowing a policy tag with a weight value; establishing a policy matching analysis model and calculating a matching result based on a matching rule and a weight value, wherein the policy matching analysis model comprises an Additive MAVF model, an effective label matching degree model and an evidence reasoning model; aiming at the park data, defining initial weights of all indexes, and obtaining park scores under the initial weights; establishing a DEA-WEI model to reflect the maximum expectation of each park; and optimizing the weight by using a minimum maximum method.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a park policy matching and evaluating method based on a multi-standard decision model.
Background
For the enterprise park, the system is used as an important basic support for scientific and technological innovation, and plays an important role in supporting the development of small and medium-sized enterprises and promoting the development of social economy. Regional policy guidance and support of the technology park are critical to enterprise development. At present, certain research is carried out on whether the regional policy can effectively promote innovation and the industry analysis of enterprises in the scientific and technological park. However, with the gradual development of big data analysis technology, the matching of enterprises and relevant policies and the comprehensive performance of a park are not evaluated, so that the problem to be solved is urgently needed.
Disclosure of Invention
In order to solve the problems, the invention provides a park policy matching and evaluating method based on a multi-standard decision model.
The invention adopts the following technical scheme:
a campus policy matching and evaluation method based on a multi-criteria decision model, comprising the steps of:
s1, acquiring policy data, enterprise data and park data;
s2, preprocessing the policy data and the enterprise data, and respectively extracting policy labels and enterprise labels, wherein the policy labels and the enterprise labels comprise quantitative labels and qualitative labels;
s3, defining a matching rule and giving a weight value to the policy label;
s4, establishing a policy matching analysis model and calculating a matching result based on the matching rule and the weight value, wherein the policy matching analysis model comprises an Additive MAVF model, an effective label matching degree model and an evidence reasoning model;
s5, aiming at the garden data, defining the initial weight of each index, and obtaining the garden score under the initial weight;
s6, establishing a DEA-WEI model to reflect the maximum expectation of each park;
and S7, optimizing the weight by using a minimum maximum method, and calculating the final park score.
Preferably, the Additive MAVF model is specifically:
preferably, the valid tag matching degree model is:
the effective matching degree is the total score/the number of effective labels.
Preferably, the evidence reasoning model is established by:
firstly defining label attribute grade, then respectively defining attribute for quantitative label and qualitative label, finally giving weight to quantitative label and qualitative label.
Preferably, the step S6 is specifically realized by the following method:
firstly, a DEA-WEI model is established, and the formula is as follows:
(u1, u2, …, us) T reflects the maximum expectation of the campus;
then define
The formula is as follows:
final normalized gj，
After adopting the technical scheme, compared with the background technology, the invention has the following advantages:
according to the method, whether the policies of the enterprises and the relevant areas are matched or not can be evaluated by establishing the policy matching analysis model, the park performance result can be accurately obtained by evaluating and analyzing the park indexes, and the final evaluation result is more accurate and transparent.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
Referring to fig. 1, the invention discloses a campus policy matching and evaluation method based on a multi-standard decision model, which comprises the following steps:
s1, acquiring policy data, enterprise data and park data;
s2, preprocessing the policy data and the enterprise data, and respectively extracting a policy label and an enterprise label, wherein the policy label and the enterprise label comprise a quantitative label and a qualitative label;
s3, defining a matching rule and giving a weight value to the policy label;
s4, establishing a policy matching analysis model and calculating a matching result based on the matching rules and the weight values, wherein the policy matching analysis model comprises an Additive MAVF model, an effective label matching degree model and an evidence reasoning model.
The Additive MAVF model specifically comprises the following steps:
the method for establishing the Additive MAVF model comprises the following steps:
adopting a subjective assignment legal meaning model:
wherein, scorejScoring the campus, wrIs the weight value of the attribute r, yjrThe converted value for property r for campus j.
The effective tag matching degree model is as follows:
the effective matching degree is the total score/the number of effective labels.
The evidence reasoning model is established by the following method:
firstly defining label attribute grade, then respectively defining attribute for quantitative label and qualitative label, finally giving weight to quantitative label and qualitative label.
S5, aiming at the garden data, defining the initial weight of each index, and obtaining the garden score under the initial weight;
and S6, establishing a DEA-WEI model to reflect the maximum expectation of each park. Step S6 is specifically implemented by the following method:
firstly, a DEA-WEI model is established, and the formula is as follows:
(u1, u2, …, us) T reflects the maximum expectation of the campus;
then define
The formula is as follows:
final normalized gj，
And S7, optimizing the weight by using a minimum maximum method, and calculating the final park score.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A campus policy matching and evaluation method based on a multi-standard decision model is characterized by comprising the following steps:
s1, acquiring policy data, enterprise data and park data;
s2, preprocessing the policy data and the enterprise data, and respectively extracting policy labels and enterprise labels, wherein the policy labels and the enterprise labels comprise quantitative labels and qualitative labels;
s3, defining a matching rule and giving a weight value to the policy label;
s4, establishing a policy matching analysis model and calculating a matching result based on the matching rule and the weight value, wherein the policy matching analysis model comprises an Additive MAVF model, an effective label matching degree model and an evidence reasoning model;
s5, aiming at the garden data, defining the initial weight of each index, and obtaining the garden score under the initial weight;
s6, establishing a DEA-WEI model to reflect the maximum expectation of each park;
and S7, optimizing the weight by using a minimum maximum method, and calculating the final park score.
2. The campus policy matching and evaluation method based on multi-criteria decision model as claimed in claim 1, wherein the Additive MAVF model is specifically:
3. the campus policy matching and evaluation method based on multi-criteria decision model as claimed in claim 1, wherein the valid tag matching degree model is:
the effective matching degree is the total score/the number of effective labels.
4. The multi-criteria decision model-based campus policy matching and evaluation method of claim 1 wherein said evidence reasoning model is built by:
firstly defining label attribute grade, then respectively defining attribute for quantitative label and qualitative label, finally giving weight to quantitative label and qualitative label.
5. The multi-criteria decision model-based campus policy matching and evaluation method of claim 1, wherein the step S6 is implemented by the following method:
firstly, a DEA-WEI model is established, and the formula is as follows:
(u1, u2, …, us) T reflects the maximum expectation of the campus;
then define
The formula is as follows:
final normalized gj，
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| CN201911413276.1A CN111159630A (en) | 2019-12-31 | 2019-12-31 | Park policy matching and evaluating method based on multi-standard decision model |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112380264A (en) * | 2020-11-23 | 2021-02-19 | 政和科技股份有限公司 | Policy analysis and matching method and device based on personal full life cycle |
| CN112418600A (en) * | 2020-10-15 | 2021-02-26 | 重庆市科学技术研究院 | Enterprise policy scoring method and system based on index set |
| CN112418601A (en) * | 2020-10-15 | 2021-02-26 | 重庆市科学技术研究院 | Policy matching method and system based on index set |
| CN112541740A (en) * | 2020-12-18 | 2021-03-23 | 苏州晨功侠科技有限公司 | Enterprise policy matching and evaluating algorithm |
| CN112765441A (en) * | 2021-04-07 | 2021-05-07 | 北京零号窗网络信息技术有限公司 | Enterprise policy information multiple dynamic intelligent matching recommendation method for digital government affairs |
| CN113627784A (en) * | 2021-08-09 | 2021-11-09 | 浙江天能优品网络科技有限公司 | Enterprise asset management intelligent decision-making system based on industrial internet |
| CN114201973A (en) * | 2022-02-15 | 2022-03-18 | 深圳博士创新技术转移有限公司 | Resource pool object data mining method and system based on artificial intelligence |
-
2019
- 2019-12-31 CN CN201911413276.1A patent/CN111159630A/en active Pending
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112418600A (en) * | 2020-10-15 | 2021-02-26 | 重庆市科学技术研究院 | Enterprise policy scoring method and system based on index set |
| CN112418601A (en) * | 2020-10-15 | 2021-02-26 | 重庆市科学技术研究院 | Policy matching method and system based on index set |
| CN112380264A (en) * | 2020-11-23 | 2021-02-19 | 政和科技股份有限公司 | Policy analysis and matching method and device based on personal full life cycle |
| CN112541740A (en) * | 2020-12-18 | 2021-03-23 | 苏州晨功侠科技有限公司 | Enterprise policy matching and evaluating algorithm |
| CN112765441A (en) * | 2021-04-07 | 2021-05-07 | 北京零号窗网络信息技术有限公司 | Enterprise policy information multiple dynamic intelligent matching recommendation method for digital government affairs |
| CN113627784A (en) * | 2021-08-09 | 2021-11-09 | 浙江天能优品网络科技有限公司 | Enterprise asset management intelligent decision-making system based on industrial internet |
| CN114201973A (en) * | 2022-02-15 | 2022-03-18 | 深圳博士创新技术转移有限公司 | Resource pool object data mining method and system based on artificial intelligence |
| CN114201973B (en) * | 2022-02-15 | 2022-06-07 | 深圳博士创新技术转移有限公司 | Resource pool object data mining method and system based on artificial intelligence |
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Application publication date: 20200515 |