Method and device for providing decision reference information of scientific and technological innovation project, equipment, storage medium and program product

Abstract

The invention relates to a method and device for providing decision reference information of a scientific and technological innovation project, equipment, a storage medium and a program product, and relates to the technical field of computers. By adopting the method, the scientificity and reliability of scientific and technological innovation project evaluation decision can be improved. The method comprises the steps of obtaining a project data set of a scientific and technological innovation project, and performing data cleaning on the project data set to obtain to-be-analyzed target project data; analyzing the target project data through a data analysis algorithm to obtain an analysis result, and performing quantitative scoring on each evaluation index in the evaluation index system according to the analysis result to generate a quantitative scoring result; determining a weight vector of each evaluation index by adopting an analytic hierarchy process, and comprehensively evaluating the scientific and technological innovation project by adopting a fuzzy comprehensive evaluation method according to the weight vector and the quantitative scoring result to obtain a comprehensive evaluation result vector; and generating decision reference information of the scientific and technological innovation project according to the comprehensive evaluation result vector.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for providing decision-making reference information for scientific and technological innovation projects. Background Art

[0002] Currently, there are significant deficiencies in data mining and application during the evaluation and decision-making process for scientific and technological innovation projects. For example, in market data analysis, most evaluations rely solely on a small amount of publicly available data or simple market research, failing to fully integrate data from multiple channels, such as market research platforms and industry databases. This results in a partial and limited understanding of market size and competitor performance.

[0003] This makes it difficult for evaluators to accurately understand the real dynamics and potential trends of the market, and unable to provide comprehensive and accurate market dimension assessments for scientific and technological innovation projects, which in turn affects the scientific nature and reliability of project evaluation decisions. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for providing decision-making reference information for scientific and technological innovation projects in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for providing decision-making reference information for scientific and technological innovation projects, comprising:

[0006] Acquire a project data set of a scientific and technological innovation project, and perform data cleaning on the project data set to obtain target project data to be analyzed;

[0007] Analyzing the target project data using a data analysis algorithm to obtain an analysis result, and quantifying and scoring each evaluation indicator in the evaluation indicator system based on the analysis result to generate a quantitative scoring result;

[0008] A hierarchical analysis method is used to determine the weight vector of each evaluation indicator, and a fuzzy comprehensive evaluation method is used to comprehensively evaluate the scientific and technological innovation project according to the weight vector and the quantitative scoring result to obtain a comprehensive evaluation result vector;

[0009] Decision-making reference information for the scientific and technological innovation project is generated based on the comprehensive evaluation result vector.

[0010] In one embodiment, analyzing the target project data using a data analysis algorithm to obtain an analysis result includes:

[0011] The market data in the target project data is analyzed by a cluster analysis algorithm to obtain a market segmentation identification result; the potential correlation between the R&D data in the target project data and the market data is analyzed by an association rule mining algorithm to obtain potential correlation information; the future income value of the scientific and technological innovation project is predicted by a regression analysis algorithm; the analysis result is obtained based on the market segmentation identification result, the potential correlation information and the future income value.

[0012] In one embodiment, analyzing the potential association between the R&D data in the target project data and the market data using an association rule mining algorithm to obtain potential association information includes:

[0013] According to the total number of transactions of the R&D data, the market data and the target project data, the minimum support and the minimum confidence of the target project data are determined; according to the minimum support, the target project data is scanned to obtain frequent item sets, and candidate association rules are generated according to the frequent item sets; according to the minimum confidence, the candidate association rules are screened to obtain the target association rule as the potential association information.

[0014] In one embodiment, the step of performing data cleaning on the project data set to obtain target project data to be analyzed includes:

[0015] A mean filtering method is used to remove noise interference in the project data set; outliers in the project data set are identified and eliminated based on a preset data range threshold; missing values ​​in the project data set are detected and filled.

[0016] In one embodiment, the method further comprises:

[0017] The technical innovation index, market potential index, team capability index and project feasibility index are used as multiple evaluation indexes for evaluating the scientific and technological innovation project; and the evaluation index system is constructed based on the technical innovation index, market potential index, team capability index and project feasibility index.

[0018] In one embodiment, the method further comprises:

[0019] The market growth rate forecast value is obtained by fitting and predicting the historical market data through the time series analysis algorithm, and the market growth rate forecast value is used as the first sub-indicator; the target market share estimate value is obtained by calculating the market share allocation model based on the project product competitiveness analysis results, and the target market share estimate value is used as the second sub-indicator; based on the first sub-indicator and the second sub-indicator, the market potential indicator is determined.

[0020] In a second aspect, the present application further provides a device for providing decision-making reference information for scientific and technological innovation projects, comprising:

[0021] A data acquisition module is used to obtain a project data set of a scientific and technological innovation project, and perform data cleaning on the project data set to obtain target project data to be analyzed;

[0022] A data analysis module is used to analyze the target project data using a data analysis algorithm to obtain analysis results, and to quantify and score each evaluation indicator in the evaluation indicator system based on the analysis results to generate a quantitative scoring result;

[0023] A comprehensive evaluation module is used to determine the weight vector of each evaluation indicator using the hierarchical analysis method, and to perform a comprehensive evaluation on the scientific and technological innovation project according to the weight vector and the quantitative scoring result using the fuzzy comprehensive evaluation method to obtain a comprehensive evaluation result vector;

[0024] A result generation module is used to generate decision-making reference information for the scientific and technological innovation project based on the comprehensive evaluation result vector.

[0025] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0026] Acquire a project data set of a scientific and technological innovation project, perform data cleaning on the project data set to obtain target project data to be analyzed; analyze the target project data using a data analysis algorithm to obtain an analysis result, perform quantitative scoring on each evaluation indicator in an evaluation indicator system according to the analysis result, and generate a quantitative scoring result; use a hierarchical analysis method to determine a weight vector of each evaluation indicator, and use a fuzzy comprehensive evaluation method to comprehensively evaluate the scientific and technological innovation project according to the weight vector and the quantitative scoring result, and obtain a comprehensive evaluation result vector; and generate decision-making reference information for the scientific and technological innovation project based on the comprehensive evaluation result vector.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0028] Acquire a project data set of a scientific and technological innovation project, perform data cleaning on the project data set to obtain target project data to be analyzed; analyze the target project data using a data analysis algorithm to obtain an analysis result, perform quantitative scoring on each evaluation indicator in an evaluation indicator system according to the analysis result, and generate a quantitative scoring result; use a hierarchical analysis method to determine a weight vector of each evaluation indicator, and use a fuzzy comprehensive evaluation method to comprehensively evaluate the scientific and technological innovation project according to the weight vector and the quantitative scoring result, and obtain a comprehensive evaluation result vector; and generate decision-making reference information for the scientific and technological innovation project based on the comprehensive evaluation result vector.

[0029] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0030] Acquire a project data set of a scientific and technological innovation project, perform data cleaning on the project data set to obtain target project data to be analyzed; analyze the target project data using a data analysis algorithm to obtain an analysis result, perform quantitative scoring on each evaluation indicator in an evaluation indicator system according to the analysis result, and generate a quantitative scoring result; use a hierarchical analysis method to determine a weight vector of each evaluation indicator, and use a fuzzy comprehensive evaluation method to comprehensively evaluate the scientific and technological innovation project according to the weight vector and the quantitative scoring result, and obtain a comprehensive evaluation result vector; and generate decision-making reference information for the scientific and technological innovation project based on the comprehensive evaluation result vector.

[0031] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for providing decision-making reference information for scientific and technological innovation projects analyze the target project data through a data analysis algorithm to obtain an analysis result, and then quantitatively score each evaluation indicator in the evaluation index system based on the analysis result to generate a quantitative scoring result, and then use the hierarchical analysis method to determine the weight vector of each evaluation indicator, and use the fuzzy comprehensive evaluation method to comprehensively evaluate the scientific and technological innovation project according to the weight vector and the quantitative scoring result to obtain a comprehensive evaluation result vector, and finally generate decision-making reference information for the scientific and technological innovation project based on the comprehensive evaluation result vector. This application analyzes multi-dimensional project data sets and can comprehensively integrate various market information resources, making up for the single and one-sided defects of market data in traditional evaluation methods, and providing evaluators with a new decision-making perspective, so that when constructing an evaluation index system and generating decision-making reference information, they no longer rely solely on experience judgment, but are based on objective and accurate data mining results, so that evaluators can understand the overall market picture from a more macro and detailed perspective, and provide rich and detailed market data support for project evaluation. This greatly improves the scientificity and reliability of decision-making in the evaluation of scientific and technological innovation projects, reduces decision-making risks, and increases the probability of project success. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A diagram of an application environment for a method of providing decision-making reference information for scientific and technological innovation projects in one embodiment;

[0034] Figure 2 A flowchart of a method for providing decision-making reference information for scientific and technological innovation projects in one embodiment;

[0035] Figure 3 A flowchart of an association rule mining step in one embodiment;

[0036] Figure 4 A flowchart of a method for providing decision-making reference information for scientific and technological innovation projects in another embodiment;

[0037] Figure 5 A structural block diagram of an apparatus for providing decision-making reference information for scientific and technological innovation projects in one embodiment;

[0038] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0040] The method for providing decision-making reference information for scientific and technological innovation projects provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown in FIG. , the terminal can communicate with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server or placed on the cloud or other network servers. Figure 1 In the application environment shown, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, and tablet computers. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0041] In one embodiment, Figure 2As shown, a method for providing decision-making reference information for scientific and technological innovation projects is provided. Figure 1 The following steps are used as an example to illustrate the terminal in the figure:

[0042] Step S201: Acquire a project data set of scientific and technological innovation projects, and perform data cleaning on the project data set to obtain target project data to be analyzed.

[0043] Among them, the project data sets of scientific and technological innovation projects include but are not limited to basic project information, R&D data, market data, and industry dynamics data.

[0044] Specifically, the terminal extracts basic information such as the project name, person in charge, and project expectations from the project application documents, obtains R&D data such as R&D progress and technological breakthroughs from the project R&D logs and experimental reports, collects market data such as market size and competitor situation through market research platforms and industry databases, and uses data collection technology to collect industry dynamic data such as industry news, policies and regulations.

[0045] It's important to note that the evaluation of scientific and technological innovation projects requires comprehensive information from multiple sources. Basic information reflects the project's fundamentals, R&D data reflects its technological progress, market data reflects its market prospects, and industry dynamics data helps understand the project's macroeconomic environment. By collecting this data, a comprehensive information set can be constructed. This comprehensive data source enables multi-dimensional evaluation, avoiding biases caused by insufficient information and improving its accuracy and reliability.

[0046] Step S202: Analyze the target project data using a data analysis algorithm to obtain analysis results, and quantify and score each evaluation indicator in the evaluation indicator system based on the analysis results to generate a quantitative scoring result.

[0047] Among them, data analysis algorithms include but are not limited to cluster analysis algorithms, association rule mining algorithms, and regression analysis algorithms.

[0048] Specifically, the terminal uses a data analysis algorithm to analyze the preprocessed target project data to obtain an analysis result, and based on the analysis result, quantifies and scores each evaluation indicator in the evaluation indicator system to generate a quantitative scoring result.

[0049] In this embodiment, the data analysis algorithm can be used to mine the potential information and patterns in the data. By analyzing the pre-processed target project data, specific numerical values ​​related to the evaluation indicators can be obtained. Then, based on these numerical values, each evaluation indicator can be quantitatively scored according to certain standards, and the characteristics of the project can be converted into specific scores, making the evaluation results more intuitive and comparable, and providing a more scientific and reliable basis for decision-making.

[0050] Step S203: A hierarchical analysis method is used to determine the weight vector of each evaluation indicator, and a fuzzy comprehensive evaluation method is used to conduct a comprehensive evaluation of the scientific and technological innovation project based on the weight vector and the quantitative scoring result to obtain a comprehensive evaluation result vector.

[0051] Specifically, the terminal first determines the weight of each evaluation indicator through the hierarchical analysis method, constructs a judgment matrix and performs consistency test, and calculates the weight vector of each indicator; then uses the fuzzy comprehensive evaluation method to comprehensively evaluate the scientific and technological innovation project and generate a comprehensive evaluation result vector. The calculation formula of the fuzzy comprehensive evaluation is:

[0052]

[0053] In the above formula, is the comprehensive evaluation result vector, is the weight vector, is the fuzzy relationship matrix, is the fuzzy composition operator.

[0054] It should be noted that the core task of the AHP approach is to scientifically determine the weights of each evaluation indicator. First, the complex evaluation problem is decomposed into multiple hierarchical structures, and a judgment matrix is ​​constructed. Elements in the judgment matrix are derived through expert judgment and comparison, representing the relative importance of two indicators. Once constructed, the judgment matrix undergoes rigorous consistency checks to ensure the rationality and accuracy of the judgments. Through a series of processing steps, a weight vector for each indicator is ultimately obtained, clarifying the relative importance of each evaluation indicator within the overall evaluation system. In the fuzzy comprehensive evaluation approach, a fuzzy relationship matrix is ​​constructed based on the quantitative scores of each indicator and the weight vectors derived through the AHP approach. Fuzzy synthesis operators are then applied to generate a comprehensive evaluation result vector. This process fully accounts for the ambiguity and uncertainty inherent in the evaluation process, achieving a comprehensive and integrated evaluation of the project. The organic combination of the AHP and the fuzzy comprehensive evaluation method leverages the strengths of both approaches. The AHP approach scientifically determines the weights of each evaluation indicator, ensuring that the evaluation results accurately reflect the differences in importance among indicators and avoiding subjectivity and arbitrariness in weight assignment. The fuzzy comprehensive evaluation method effectively handles fuzzy information during the evaluation process and comprehensively considers various uncertainties, making the evaluation results more relevant to the actual project situation. The combination of the two greatly improves the accuracy and reliability of evaluation decisions.

[0055] Step S204: Generate decision-making reference information for scientific and technological innovation projects based on the comprehensive evaluation result vector.

[0056] Specifically, in response to an instruction to provide decision-making reference information for a scientific and technological innovation project, the terminal generates and displays decision-making reference information including a comprehensive evaluation result vector.

[0057] In the above-mentioned method for providing decision-making reference information for scientific and technological innovation projects, the target project data is analyzed by a data analysis algorithm to obtain an analysis result, and then the evaluation indicators in the evaluation index system are quantitatively scored according to the analysis result to generate a quantitative scoring result, and then the hierarchical analysis method is used to determine the weight vector of each evaluation indicator, and the fuzzy comprehensive evaluation method is used to comprehensively evaluate the scientific and technological innovation project according to the weight vector and the quantitative scoring result to obtain a comprehensive evaluation result vector, and finally, based on the comprehensive evaluation result vector, the decision-making reference information for the scientific and technological innovation project is generated. This application analyzes multi-dimensional project data sets and can comprehensively integrate various market information resources, making up for the defects of single and one-sided market data in traditional evaluation methods, and providing evaluators with a new decision-making perspective, so that when constructing an evaluation index system and generating decision-making reference information, they no longer rely solely on experience judgment, but are based on objective and accurate data mining results, so that evaluators can understand the overall market picture from a more macro and detailed perspective, and provide rich and detailed market data support for project evaluation. This greatly improves the scientificity and reliability of decision-making in the evaluation of scientific and technological innovation projects, reduces decision-making risks, and increases the probability of project success.

[0058] In one embodiment, in the above step S202, analyzing the target project data using a data analysis algorithm to obtain an analysis result specifically includes the following steps:

[0059] The market data in the target project data is analyzed through a cluster analysis algorithm to obtain the market segmentation identification results; the potential correlation between the R&D data and market data in the target project data is analyzed through an association rule mining algorithm to obtain potential correlation information; the future income value of the scientific and technological innovation project is predicted through a regression analysis algorithm; the analysis results are obtained based on the market segmentation identification results, potential correlation information and future income values.

[0060] Among them, the cluster analysis algorithm can adopt the K-Means clustering algorithm, and its calculation formula is:

[0061]

[0062] In the above formula, is the sum of squared distances from a data point to its cluster center, is the number of clusters, is the data point, For the clusters, For the The center of the cluster, For data points To cluster center distance.

[0063] Specifically, the K-Means clustering algorithm's primary goal is to accurately partition given data points into K clusters, using the principle of minimizing the sum of the squared distances from each data point to its cluster center. During the actual operation, the algorithm dynamically adjusts the positions of cluster centers based on the distribution of data points through continuous iteration until the preset convergence criteria are met, thereby achieving efficient data clustering. By optimizing the sum of squared distances, the algorithm accurately groups data points with similar characteristics into the same cluster, thereby clearly identifying different market segments. When processing large-scale market data, it can quickly and accurately perform cluster analysis on the data, helping companies efficiently identify market segments.

[0064] In this embodiment, the integrated application of multiple data analysis algorithms unlocks multiple keys for data mining, enabling comprehensive and in-depth exploration of hidden information and patterns within the data. This rich data base provides more detailed support for the quantification of evaluation indicators, making the evaluation results more accurate and reliable. Furthermore, it provides comprehensive and in-depth information for project decision-making, helping decision-makers comprehensively consider multiple perspectives, such as market segmentation, R&D-market linkages, and project revenue forecasts. This enhances the scientific and comprehensive nature of evaluation decisions and provides a strong guarantee for the successful implementation of projects.

[0065] In one embodiment, Figure 3 As shown, the above-mentioned analysis of the potential association between the R&D data and the market data in the target project data through the association rule mining algorithm to obtain the potential association information specifically includes the following steps:

[0066] Based on the total number of transactions of R&D data, market data and target project data, the minimum support and minimum confidence of the target project data are determined; the target project data is scanned according to the minimum support to obtain frequent item sets, and candidate association rules are generated based on the frequent item sets; the candidate association rules are screened according to the minimum confidence to obtain the target association rule as potential association information.

[0067] Among them, the association rule mining algorithm can adopt the Apriori algorithm, whose core steps are: generating frequent item sets by scanning the data set, and then generating association rules from the frequent item sets. The minimum support and minimum confidence are the key parameters of the algorithm, and their calculation formulas are as follows:

[0068] Support:

[0069] Confidence:

[0070] In the above formula, is the association rule, For item sets and The number of times it appears at the same time, is the total number of transactions in the dataset, For item sets Number of occurrences.

[0071] Specifically, the Apriori algorithm employs a layer-by-layer search strategy, starting with a single itemset and gradually generating larger itemsets. During this generation process, the algorithm scans the dataset and selects frequent itemsets that meet the criteria based on a key parameter, minimum support. Once these frequent itemsets are obtained, association rules are generated based on these frequent itemsets. These association rules are then filtered using a parameter, minimum confidence, to retain those with practical significance and reliability. This approach effectively mines potential correlations between R&D and market data from large datasets. The Apriori algorithm, with its efficient data mining capabilities, accurately discovers hidden association rules within datasets. These association rules provide valuable information for companies in their R&D and marketing decision-making processes. By understanding the potential correlations between R&D and market data, companies can more scientifically adjust their R&D directions to better align them with market demand, optimize marketing strategies, and enhance the relevance and effectiveness of their marketing efforts.

[0072] In one embodiment, in step S201, data cleaning is performed on the project data set to obtain target project data to be analyzed, which specifically includes the following steps:

[0073] The mean filtering method is used to remove noise interference in the project data set; outliers in the project data set are identified and eliminated based on the preset data range threshold; missing values ​​in the project data set are detected and filled.

[0074] Specifically, during the data collection process, various objective factors inevitably introduce noise and outliers. These data impurities can seriously interfere with the accuracy of subsequent data analysis and evaluation. To address this issue, we first employ the mean filtering method. This method, based on signal processing principles, calculates the average value of the data within a certain range around a data point and uses it to replace the original data point value. This effectively smooths the data curve, removes noise interference, and makes the data more stable and reliable. Furthermore, based on the data distribution characteristics and business logic, a data range threshold is scientifically and rationally set. When a data point exceeds this threshold, it is accurately identified as an outlier and decisively removed to prevent it from misleading subsequent analysis. Furthermore, for the inevitable missing values ​​in the data, interpolation or multiple imputation methods are flexibly used based on the data's inherent characteristics. Interpolation cleverly estimates the value of a missing data point based on the distribution of known data points. Multiple imputation generates multiple possible imputation values ​​through multiple sampling and imputation operations, and then combines these values ​​to obtain the final accurate imputation result, thus ensuring data integrity.

[0075] Through this series of data cleaning and preprocessing operations, we effectively removed noise and outliers from the data, successfully filled in missing data, and significantly improved data quality. High-quality data laid a solid foundation for subsequent data analysis and evaluation, avoiding biased evaluation results caused by data quality issues and greatly enhancing the credibility of the evaluation results.

[0076] In one embodiment, the method of the present application further includes:

[0077] Technological innovation indicators, market potential indicators, team capability indicators and project feasibility indicators are used as multiple evaluation indicators for evaluating scientific and technological innovation projects; an evaluation indicator system is constructed based on technological innovation indicators, market potential indicators, team capability indicators and project feasibility indicators.

[0078] Specifically, the technological innovation dimension includes sub-indicators such as the number of patent applications and the degree of technological leadership, comprehensively assessing a project's technological innovation capabilities from the perspectives of both the number of patents and the quality of its technological advancement. The market potential dimension includes sub-indicators such as the market growth rate forecast and the target market share estimate. This comprehensively assesses a project's market prospects and commercial potential by forecasting market growth trends and estimating the project's market share in the target market. The team capability dimension includes sub-indicators such as the core members' educational background and project experience, comprehensively assessing the team's execution capabilities based on their knowledge base and practical project experience. The project feasibility dimension includes sub-indicators such as the budget rationality score and the timeline rationality score, assessing the feasibility of project implementation from the perspective of the rationality of the project's funding and timeline planning. This clear, comprehensive, and quantifiable evaluation indicator system provides clear and specific standards and direction for the evaluation of scientific and technological innovation projects. During the evaluation process, evaluators can use these indicators to conduct objective and standardized assessments, reducing subjectivity and making the evaluation process more scientific and accurate.

[0079] Among them, the quantitative calculation method for the number of patent applications in the technological innovation index is: counting the total number of patents submitted and authorized for the project; the degree of technological leadership is scored by expert scoring method, based on the comparison between the project technology and the technology of the same industry, with a full score of 10 points.

[0080] This quantitative calculation method transforms the degree of technological innovation, which is originally relatively abstract and difficult to directly measure, into a concrete and comparable value. On the one hand, the statistics on the number of patent applications provide a clear quantitative basis for evaluation, facilitating horizontal comparisons of different projects in terms of patent achievements. On the other hand, the expert scoring method ensures the professionalism and objectivity of the evaluation of the complex indicator of technological leadership. The combination of the two makes the evaluation of technological innovation more comprehensive and accurate, providing strong support for the assessment of the project's technical strength. The market growth rate forecast within the market potential indicator is obtained by fitting and forecasting historical market data using a time series analysis algorithm. The target market share estimate is calculated based on an analysis of the project's product competitiveness and a market share distribution model.

[0081] In this embodiment, the quantitative approach based on data models and analytical methods frees market potential assessment from subjective assumptions and makes it more scientific and forward-looking. Accurate market growth rate forecasts and target market share estimates provide project decision makers with precise data support, helping them to fully understand the market prospects and commercial value of the project. This helps them to rationally plan resource investment and formulate scientific market strategies during the project decision-making process, thereby improving the project's competitiveness and success probability in the market.

[0082] In one embodiment, the method of the present application further includes:

[0083] The market growth rate forecast is obtained by fitting and predicting the historical market data through the time series analysis algorithm, and the market growth rate forecast is used as the first sub-indicator; the target market share estimate is calculated through the market share allocation model based on the project product competitiveness analysis results, and the target market share estimate is used as the second sub-indicator; based on the first and second sub-indicators, the market potential indicator is determined.

[0084] Specifically, in the market potential dimension, it covers sub-indicators such as market growth rate forecast and target market share estimate. By predicting market growth trends and estimating the market share of project products in the target market, it comprehensively evaluates the market prospects and business potential of the project and determines the market potential indicators.

[0085] In one embodiment, Figure 4 As shown, a method for providing decision-making reference information for scientific and technological innovation projects in a specific embodiment is provided, which specifically includes the following steps:

[0086] Step S401: obtain the project data set of scientific and technological innovation projects, use the mean filtering method to remove noise interference in the project data set; identify and eliminate outliers in the project data set according to the preset data range threshold; detect missing values ​​in the project data set and fill in the missing values.

[0087] Step S402: Using technology innovation indicators, market potential indicators, team capability indicators, and project feasibility indicators as multiple evaluation indicators for evaluating scientific and technological innovation projects; constructing an evaluation indicator system based on technology innovation indicators, market potential indicators, team capability indicators, and project feasibility indicators.

[0088] Step S403: Analyze the market data in the target project data through a cluster analysis algorithm to obtain the market segmentation field identification result; determine the minimum support and minimum confidence of the target project data based on the total number of transactions of the R&D data, market data and target project data; scan the target project data based on the minimum support to obtain frequent item sets, and generate candidate association rules based on the frequent item sets; filter the candidate association rules based on the minimum confidence to obtain the target association rule as potential association information.

[0089] Step S404: predict the future revenue value of the scientific and technological innovation project through a regression analysis algorithm; obtain the analysis result based on the market segmentation identification result, potential correlation information and future revenue value.

[0090] Step S405: quantify and score each evaluation indicator in the evaluation indicator system according to the analysis results to generate a quantitative scoring result; use the hierarchical analysis method to determine the weight vector of each evaluation indicator, and use the fuzzy comprehensive evaluation method to comprehensively evaluate the scientific and technological innovation project according to the weight vector and the quantitative scoring result to obtain a comprehensive evaluation result vector; generate decision-making reference information for the scientific and technological innovation project based on the comprehensive evaluation result vector.

[0091] The beneficial effects brought about by the above embodiment are as follows:

[0092] 1) By collecting multi-dimensional market data such as market size and competitor status from market research platforms and industry databases, it is possible to comprehensively integrate various market information resources, making up for the defects of single and one-sided market data in traditional evaluation methods, allowing evaluators to understand the overall market picture from a more macro and detailed perspective, and providing rich and detailed market data support for project evaluation.

[0093] 2) By cleaning and preprocessing multi-channel big data and conducting in-depth data analysis using algorithms such as cluster analysis and association rule mining, potential connections and patterns within the data can be uncovered. For example, by analyzing the potential connections between R&D data and market data through association rule mining algorithms, evaluators can gain a new perspective for decision-making. This allows them to build evaluation indicators and make decisions based on objective and accurate data mining results, rather than relying solely on empirical judgment. This significantly improves the scientific nature and reliability of scientific and technological innovation project evaluation decisions, reduces decision-making risks, and increases the probability of project success.

[0094] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0095] Based on the same inventive concept, an embodiment of the present application further provides a device for providing decision-making reference information for scientific and technological innovation projects, which is used to implement the aforementioned method for providing decision-making reference information for scientific and technological innovation projects. The implementation solution provided by the device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of the one or more embodiments of the device for providing decision-making reference information for scientific and technological innovation projects provided below can be found in the above-mentioned limitations of the method for providing decision-making reference information for scientific and technological innovation projects, and will not be repeated here.

[0096] In an exemplary embodiment, Figure 5 As shown, a device for providing decision-making reference information for scientific and technological innovation projects is provided, and the device may include:

[0097] The data acquisition module 501 is used to obtain a project data set of a scientific and technological innovation project and perform data cleaning on the project data set to obtain target project data to be analyzed;

[0098] The data analysis module 502 is used to analyze the target project data using a data analysis algorithm to obtain analysis results, and to quantify and score each evaluation indicator in the evaluation indicator system based on the analysis results to generate a quantitative scoring result;

[0099] Comprehensive evaluation module 503, used to determine the weight vector of each evaluation indicator using the hierarchical analysis method, and use the fuzzy comprehensive evaluation method to comprehensively evaluate the scientific and technological innovation project based on the weight vector and the quantitative scoring result to obtain a comprehensive evaluation result vector;

[0100] The result generation module 504 is used to generate decision-making reference information for scientific and technological innovation projects based on the comprehensive evaluation result vector.

[0101] In one embodiment, the data analysis module 502 is also used to analyze the market data in the target project data through a cluster analysis algorithm to obtain market segmentation identification results; analyze the potential correlation between the R&D data and the market data in the target project data through an association rule mining algorithm to obtain potential correlation information; predict the future income value of the scientific and technological innovation project through a regression analysis algorithm; and obtain analysis results based on the market segmentation identification results, potential correlation information and future income values.

[0102] In one embodiment, the data analysis module 502 is further used to determine the minimum support and minimum confidence of the target project data based on the total number of transactions of the R&D data, market data, and target project data; scan the target project data according to the minimum support to obtain frequent item sets, and generate candidate association rules based on the frequent item sets; and screen the candidate association rules according to the minimum confidence to obtain the target association rules as potential association information.

[0103] In one embodiment, the data acquisition module 501 is further used to remove noise interference in the project data set by using a mean filtering method; identify and eliminate outliers in the project data set according to a preset data range threshold; detect missing values ​​in the project data set, and fill in the missing values.

[0104] In one embodiment, the device may also include: a system construction module, which is used to use technology innovation indicators, market potential indicators, team capability indicators and project feasibility indicators as multiple evaluation indicators for evaluating scientific and technological innovation projects; and construct an evaluation indicator system based on technology innovation indicators, market potential indicators, team capability indicators and project feasibility indicators.

[0105] In one embodiment, the device may also include: an indicator determination module, which is used to fit and predict historical market data through a time series analysis algorithm to obtain a market growth rate forecast value, and use the market growth rate forecast value as the first sub-indicator; according to the project product competitiveness analysis results, the market share distribution model is used to calculate the target market share estimate value, and use the target market share estimate value as the second sub-indicator; based on the first sub-indicator and the second sub-indicator, determine the market potential indicator.

[0106] Each module in the aforementioned apparatus for providing decision-making reference information for scientific and technological innovation projects may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0107] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for providing decision-making reference information for scientific and technological innovation projects. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0108] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0109] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0110] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0111] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0112] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0113] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0114] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0115] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for providing decision-making reference information for scientific and technological innovation projects, characterized in that: The method comprises: Acquire a project data set of a scientific and technological innovation project, and perform data cleaning on the project data set to obtain target project data to be analyzed; Analyzing the target project data using a data analysis algorithm to obtain an analysis result, and quantifying and scoring each evaluation indicator in the evaluation indicator system based on the analysis result to generate a quantitative scoring result; A hierarchical analysis method is used to determine the weight vector of each evaluation indicator, and a fuzzy comprehensive evaluation method is used to comprehensively evaluate the scientific and technological innovation project according to the weight vector and the quantitative scoring result to obtain a comprehensive evaluation result vector; Decision-making reference information for the scientific and technological innovation project is generated based on the comprehensive evaluation result vector.

2. The method according to claim 1, characterized in that The analyzing the target project data by using a data analysis algorithm to obtain an analysis result includes: Analyze the market data in the target project data using a cluster analysis algorithm to obtain market segmentation identification results; analyze the potential correlation between the R&D data in the target project data and the market data using an association rule mining algorithm to obtain potential correlation information; and predict the future revenue value of the scientific and technological innovation project using a regression analysis algorithm; The analysis result is obtained based on the market segmentation identification result, the potential correlation information and the future profit value.

3. The method according to claim 2, characterized in that The analyzing the potential association between the R&D data in the target project data and the market data by an association rule mining algorithm to obtain potential association information includes: Determining a minimum support and a minimum confidence of the target project data based on the R&D data, the market data, and the total number of transactions of the target project data; Scanning the target item data according to the minimum support to obtain frequent item sets, and generating candidate association rules according to the frequent item sets; The candidate association rules are screened according to the minimum confidence to obtain a target association rule as the potential association information.

4. The method according to claim 1, wherein The step of performing data cleaning on the project data set to obtain target project data to be analyzed includes: The mean filtering method is used to remove noise interference in the project data set; Identify and remove outliers in the project data set based on a preset data range threshold; Missing values ​​in the project data set are detected and filled.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Using technical innovation indicators, market potential indicators, team capability indicators and project feasibility indicators as multiple evaluation indicators for evaluating the scientific and technological innovation project; The evaluation index system is constructed based on the technical innovation index, the market potential index, the team capability index and the project feasibility index.

6. The method according to claim 5, characterized in that The method further comprises: Perform fitting and forecasting of historical market data using a time series analysis algorithm to obtain a predicted market growth rate, and use the predicted market growth rate as the first sub-indicator; Calculate the estimated target market share based on the project product competitiveness analysis results using the market share allocation model, and use the estimated target market share as the second sub-indicator; The market potential indicator is determined based on the first sub-indicator and the second sub-indicator.

7. A device for providing decision-making reference information for scientific and technological innovation projects, characterized in that: The device comprises: A data acquisition module is used to obtain a project data set of a scientific and technological innovation project, and perform data cleaning on the project data set to obtain target project data to be analyzed; A data analysis module is used to analyze the target project data using a data analysis algorithm to obtain analysis results, and to quantify and score each evaluation indicator in the evaluation indicator system based on the analysis results to generate a quantitative scoring result; A comprehensive evaluation module is used to determine the weight vector of each evaluation indicator using the hierarchical analysis method, and to perform a comprehensive evaluation on the scientific and technological innovation project according to the weight vector and the quantitative scoring result using the fuzzy comprehensive evaluation method to obtain a comprehensive evaluation result vector; A result generation module is used to generate decision-making reference information for the scientific and technological innovation project based on the comprehensive evaluation result vector.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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