Enterprise scientific and technological achievement optimization method based on comprehensive evaluation and accurate matching

Abstract

The invention belongs to the technical field of big data analysis and application, and particularly relates to an enterprise scientific and technological achievement optimization method based on comprehensive evaluation and accurate matching, and the method comprises the following steps: data collection and preprocessing; establishing a comprehensive evaluation model; and demand prediction and optimization and automatic screening and matching. By comprehensively evaluating the growth and demand of the enterprise, optimizing the original demand, improving the evaluation level and providing scientific and technological achievements with higher fitting degree, the problem that the enterprise is difficult to obtain the most fitting achievement matching under the condition of numerous scientific and technological achievement resources is solved, the time cost is reduced, and the efficiency is improved. Therefore, the technical scheme has wide application prospects in the fields of science and technology evaluation and management, industry optimization and matching, big data analysis and application and the like.

Description

Technical Field

[0001] The present invention belongs to the field of big data analysis and application technology, and specifically relates to a method for optimizing enterprise scientific and technological achievements based on comprehensive evaluation and precise matching. Background Art

[0002] With the continuous advancement of science and technology, more and more scientific and technological achievements have emerged in enterprises. How to effectively transform these achievements into productivity and promote the development of enterprises is an urgent problem to be solved. At the same time, enterprise-side optimization and matching are becoming more and more important. How to optimally configure limited resources and maximize the potential of enterprises is a problem that needs to be solved in the field of enterprise optimization and matching.

[0003] Existing technologies mainly use big data analysis technology to comprehensively evaluate the growth and demand of enterprises. By analyzing a large amount of data, we can get the growth and demand of enterprises, thus providing a reference for the transformation of scientific and technological achievements and the optimization of enterprises. At the same time, some technical solutions also propose to predict and optimize the needs of enterprises through artificial intelligence technology to improve the fit and practicality of scientific and technological achievements.

[0004] Although existing technologies can evaluate and predict the growth and needs of enterprises to a certain extent, there are still some problems. In addition, when matching scientific and technological achievements with enterprise needs, existing technologies often rely on manual screening and matching, which is inefficient and cannot meet the needs of a large number of scientific and technological achievements and enterprise needs. Summary of the invention

[0005] The purpose of the present invention is to provide a method for optimizing enterprise scientific and technological achievements based on comprehensive evaluation and precise matching to solve the problems existing in the above-mentioned background technology.

[0006] To achieve the above objectives, this application is implemented through the following technical solutions:

[0007] A method for optimizing enterprise scientific and technological achievements based on comprehensive evaluation and precise matching adopts the following steps:

[0008] S1. Data collection and preprocessing:

[0009] By collecting relevant data of the enterprise, and cleaning, sorting and preprocessing the collected relevant data, the enterprise data set is obtained;

[0010] S2. Establishment of comprehensive evaluation model:

[0011] Based on machine learning and deep learning technologies, a comprehensive evaluation model is established to conduct a comprehensive and accurate assessment of the growth and demand of enterprises;

[0012] S3. Demand forecasting and optimization:

[0013] Use comprehensive evaluation models to predict and optimize enterprise needs;

[0014] S4. Automatic screening and matching:

[0015] Utilize the automatic screening and matching system to automatically screen and match the most suitable scientific and technological achievements and enterprise needs based on the comprehensive evaluation model, demand and optimization results.

[0016] Furthermore, in step S1, relevant data of the enterprise is collected, including but not limited to market data, technical data and financial data.

[0017] Furthermore, in step S2, the specific steps of establishing a comprehensive evaluation model based on machine learning and deep learning technology are as follows:

[0018] S21, obtaining data information of N historical set time periods, and cleaning the obtained data information to obtain an enterprise historical data set, where N ≥ 3;

[0019] S22, based on the data information of any set time period in step S21, set the evaluation standard M0, and determine the preliminary evaluation standard data set P0;

[0020] S23, based on the preliminary evaluation standard data set P0, evaluate the data information of the remaining set time periods respectively to obtain evaluation results P1-P N-1 ;

[0021] S24, the data information of the remaining set time periods is evaluated again using the set evaluation standard M0 to obtain evaluation results K1 to K2 respectively. N-1 ;

[0022] S25, respectively calculate the value of K1-P1 Δ1, the value of K2-P2 Δ2, until K N-1 -P N-1 The value ΔN-1 is determined, and if Δ1, Δ2, and ΔN-1 are all beyond the set range, the evaluation standard M0 is reset, and then steps S22 to S25 are repeated. If it is within the set range, the preliminary evaluation standard data set P0 is determined to be used for the comprehensive evaluation model.

[0023] Furthermore, when the preliminary evaluation standard dataset P0 is used for the comprehensive evaluation model, it is compared with the relevant data in step S1. If the categories of the relevant data are more than the data categories of the preliminary evaluation standard dataset P0, deep learning technology is used to repeat steps S21 to S25 to obtain a new evaluation standard dataset for the comprehensive evaluation model.

[0024] Furthermore, the comprehensive evaluation model of step S2 is implemented by dynamically adjusting parameters to meet the needs of different enterprises.

[0025] Furthermore, in step S3, the comprehensive evaluation model is used to predict and optimize the needs of the enterprise, and future changes in demand are predicted by analyzing historical data and market trends, and the demand is optimized based on the prediction results.

[0026] Furthermore, in step S4, the automatic screening and matching system is used to automatically screen and match the most suitable scientific and technological achievements and enterprise needs based on the comprehensive evaluation model and the needs and optimization results. The specific steps are as follows:

[0027] The growth and demand data of enterprises that meet the set requirements and standards screened out based on the comprehensive evaluation model are matched with the data based on enterprise needs and optimization results, and the data whose matching degree reaches the set value are sorted according to the matching degree and output the sorting.

[0028] Furthermore, it also includes a human-computer interaction interface, through which enterprises can easily use the present technical solution, submit demand assessment and matching requests, view assessment results and matching results, and provide feedback and suggestions.

[0029] Furthermore, it also includes security and privacy protection. During the process of data collection, storage and processing, we strictly abide by relevant laws, regulations and industry standards, and take necessary security measures to protect users' data security and privacy.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This technical solution can effectively solve the shortcomings of existing technologies in terms of enterprise growth and demand assessment, demand forecasting and optimization, and matching of scientific and technological achievements with enterprise needs, improve the accuracy of evaluation results, optimize demand, improve matching efficiency and accuracy, and achieve high-quality enterprise empowerment. DETAILED DESCRIPTION

[0032] The technical solution of the present invention is described in detail below. The following embodiments are merely exemplary and can only be used to explain and illustrate the technical solution of the present invention, but cannot be construed as limiting the technical solution of the present invention.

[0033] This application provides a method for optimizing enterprise scientific and technological achievements based on comprehensive evaluation and precise matching, which adopts the following steps:

[0034] S1. Data collection and preprocessing:

[0035] Collect relevant data of enterprises through various channels, and clean, organize and pre-process the collected relevant data to obtain enterprise data sets; collect relevant data of enterprises through various channels, including but not limited to market data, technical data and financial data. And clean, organize and pre-process these data to ensure the quality and comprehensiveness of the data. Specifically, market data includes market size, market growth rate, market competition status, etc.; technical data includes technical level, technical innovation ability, technical application status, etc.; financial data includes income, profit, assets and liabilities, etc.

[0036] S2. Establishment of comprehensive evaluation model:

[0037] Based on machine learning and deep learning technologies, a comprehensive evaluation model is established to conduct a comprehensive and accurate assessment of the growth and demand of enterprises.

[0038] Based on machine learning and deep learning technology, the specific steps to establish a comprehensive evaluation model are:

[0039] S21, obtaining data information of N historical set time periods, and cleaning the obtained data information to obtain an enterprise historical data set, where N ≥ 3;

[0040] S22, based on the data information of any set time period in step S21, set the evaluation standard M0, and determine the preliminary evaluation standard data set P0;

[0041] S23, based on the preliminary evaluation standard data set P0, evaluate the data information of the remaining set time periods respectively to obtain evaluation results P1-P N-1 ;

[0042] S24, the data information of the remaining set time periods is evaluated again using the set evaluation standard M0 to obtain evaluation results K1 to K2 respectively. N-1 ;

[0043] S25, respectively calculate the value of K1-P1 Δ1, the value of K2-P2 Δ2, until K N-1 -P N-1 The value ΔN-1 is determined, and if Δ1, Δ2, and ΔN-1 are all beyond the set range, the evaluation standard M0 is reset, and then steps S22 to S25 are repeated. If it is within the set range, the preliminary evaluation standard data set P0 is determined to be used for the comprehensive evaluation model.

[0044] In the present application, when the preliminary evaluation standard dataset P0 is used for the comprehensive evaluation model, it is compared with the relevant data of step S1. If the categories of the relevant data are more than the data categories of the preliminary evaluation standard dataset P0, deep learning technology is used to repeat steps S21 to S25 to obtain a new evaluation standard dataset for the comprehensive evaluation model.

[0045] In this application, a comprehensive and accurate assessment of the growth and needs of the enterprise is conducted. The model can not only consider multiple factors, but also dynamically adjust parameters to meet the needs of different enterprises.

[0046] Specifically, the model includes a data preprocessing module, a feature extraction module, an evaluation model module, and a prediction model module. The data preprocessing module is used to clean, organize, and preprocess the collected data; the feature extraction module is used to extract key features from the preprocessed data; the evaluation model module is used to evaluate the growth and demand of the enterprise based on the extracted features; and the prediction model module is used to predict future demand changes.

[0047] S3. Demand forecasting and optimization:

[0048] The comprehensive evaluation model is used to predict and optimize the needs of enterprises; by analyzing historical data and market trends, future changes in demand are predicted, and based on the predicted results, demand is optimized to improve the fit and practicality of scientific and technological achievements. Specifically, demand prediction is achieved through the prediction model module, and demand optimization is achieved through the evaluation model module and the prediction model module.

[0049] S4. Automatic screening and matching:

[0050] Utilize the automatic screening and matching system to automatically screen and match the most suitable scientific and technological achievements and enterprise needs based on the comprehensive evaluation model, demand and optimization results.

[0051] Using the automatic screening and matching system, based on the comprehensive evaluation model and demand and optimization results, the most suitable scientific and technological achievements and enterprise needs are automatically screened and matched. The specific steps are as follows:

[0052] The growth and demand data of enterprises that meet the set requirements and standards selected based on the comprehensive evaluation model are matched with the data based on enterprise needs and optimization results, and the data with matching degrees reaching the set value are sorted by matching degrees and output. Automatically screen and match the most suitable scientific and technological achievements with enterprise needs to improve matching efficiency and accuracy.

[0053] This application also includes a human-computer interaction interface, through which enterprise users can easily use this technical solution, submit demand assessment and matching requests, view assessment results and matching results, and provide feedback and suggestions.

[0054] This application also includes security and privacy protection. During data collection, storage and processing, relevant laws, regulations and industry standards are strictly followed, and necessary security measures are taken to protect user data security and privacy. Specifically, during data collection, data desensitization technology is used to desensitize sensitive information; during data storage, data encryption technology is used to encrypt and store data; during data processing, data access control technology is used to control data access rights.

[0055] The present invention comprehensively evaluates the growth and needs of enterprises, accurately determines whether the needs proposed by them are in line with the current positioning, thereby optimizing the original needs, improving the evaluation level, and providing more appropriate scientific and technological achievements. This not only solves the problem of incomplete and objective enterprise needs, but also avoids cognitive bias or errors caused by incomplete information and slow updates.

[0056] The present invention solves the problem that enterprises have difficulty in obtaining the most suitable results when there are many scientific and technological achievements resources, reduces time costs and improves efficiency. Compared with the existing technology that relies on manual screening and matching, the automatic matching method of the present invention is more efficient and can meet the needs of a large number of scientific and technological achievements and enterprise needs.

[0057] Based on big data analysis, the present invention can make more comprehensive use of data and improve the accuracy of evaluation results by deeply mining and analyzing data. Compared with the prior art, the present invention not only relies on a large amount of data, but also pays attention to the quality and comprehensiveness of the data, thereby improving the accuracy of the evaluation results. 4. Smarter demand forecasting: The present invention uses artificial intelligence technology to predict and optimize the needs of enterprises, thereby improving the fit and practicality of scientific and technological achievements. Compared with the prior art, the demand forecasting and optimization results of the present invention are more accurate and can better reflect the needs of enterprises.

[0058] The present invention optimizes the original demand, improves the evaluation level, provides more appropriate scientific and technological achievements, and realizes high-quality enterprise empowerment. Compared with the existing technology, the enterprise empowerment effect of the present invention is better, and it can better promote the development of enterprises and the transformation of scientific and technological achievements.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail according to the preferred embodiments, it should be understood that the above description is only a specific implementation mode of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing enterprise scientific and technological achievements based on comprehensive evaluation and precise matching, characterized in that: Use the following steps: S1. Data collection and preprocessing: By collecting relevant data of the enterprise, and cleaning, sorting and preprocessing the collected relevant data, the enterprise data set is obtained; S2. Establishment of comprehensive evaluation model: Based on machine learning and deep learning technologies, a comprehensive evaluation model is established to conduct a comprehensive and accurate assessment of the growth and demand of enterprises; S3. Demand forecasting and optimization: Use comprehensive evaluation models to predict and optimize enterprise needs; S4. Automatic screening and matching: Utilize the automatic screening and matching system to automatically screen and match the most suitable scientific and technological achievements and enterprise needs based on the comprehensive evaluation model, demand and optimization results.

2. The enterprise scientific and technological achievement optimization method based on comprehensive evaluation and precise matching according to claim 1 is characterized in that: In step S1, relevant data of the enterprise is collected through various channels, including but not limited to market data, technical data and financial data.

3. The enterprise scientific and technological achievement optimization method based on comprehensive evaluation and precise matching according to claim 1 is characterized in that: In step S2, the specific steps of establishing a comprehensive evaluation model based on machine learning and deep learning technology are as follows: S21, obtaining data information of N historical set time periods, and cleaning the obtained data information to obtain an enterprise historical data set, where N ≥ 3; S22, based on the data information of any set time period in step S21, set the evaluation standard M0, and determine the preliminary evaluation standard data set P0; S23, based on the preliminary evaluation standard data set P0, evaluate the data information of the remaining set time periods respectively to obtain evaluation results P1-P N-1 ; S24, the data information of the remaining set time periods is evaluated again using the set evaluation standard M0 to obtain evaluation results K1 to K2 respectively. N-1 ; S25, calculate the value of K1-P1 Δ1, the value of K2-P2 Δ2 respectively, until K N-1 -P N-1 The value ΔN-1 is determined, and if Δ1, Δ2, and ΔN-1 are all beyond the set range, the evaluation standard M0 is reset, and then steps S22 to S25 are repeated. If it is within the set range, the preliminary evaluation standard data set P0 is determined to be used for the comprehensive evaluation model.

4. The enterprise scientific and technological achievement optimization method based on comprehensive evaluation and precise matching according to claim 3 is characterized in that: When using the preliminary evaluation standard dataset P0 for the comprehensive evaluation model, it is compared with the relevant data in step S1. If the categories of the relevant data are more than the data categories of the preliminary evaluation standard dataset P0, deep learning technology is used to repeat steps S21 to S25 to obtain a new evaluation standard dataset for the comprehensive evaluation model.

5. The enterprise scientific and technological achievement optimization method based on comprehensive evaluation and precise matching according to claim 1 is characterized in that: The comprehensive evaluation model in step S2 is implemented by dynamically adjusting parameters to meet the needs of different enterprises.

6. The enterprise scientific and technological achievement optimization method based on comprehensive evaluation and precise matching according to claim 1 is characterized in that: In step S3, the comprehensive evaluation model is used to predict and optimize the needs of the enterprise. By analyzing historical data and market trends, future changes in demand are predicted, and based on the prediction results, the demand is optimized.

7. The enterprise scientific and technological achievement optimization method based on comprehensive evaluation and precise matching according to claim 1 is characterized in that: In step S4, the automatic screening and matching system is used to automatically screen and match the most suitable scientific and technological achievements and enterprise needs based on the comprehensive evaluation model and the needs and optimization results. The specific steps are as follows: The growth and demand data of enterprises that meet the set requirements and standards screened out based on the comprehensive evaluation model are matched with the data based on enterprise needs and optimization results, and the data whose matching degree reaches the set value are sorted according to the matching degree and output the sorting.

8. The enterprise scientific and technological achievement optimization method based on comprehensive evaluation and precise matching according to claim 1 is characterized in that: It also includes a human-computer interaction interface, through which enterprise users can easily use the technical solution, submit demand assessment and matching requests, view assessment results and matching results, and provide feedback and suggestions.

9. The enterprise scientific and technological achievement optimization method based on comprehensive evaluation and precise matching according to claim 1 is characterized in that: It also includes security and privacy protection. During the process of data collection, storage and processing, we strictly abide by relevant laws, regulations and industry standards, and take necessary security measures to protect users' data security and privacy.