A method, system and medium for constructing a large database for international achievement transformation services

Abstract

The present invention relates to the field of data processing technology, and in particular to a method, system and medium for constructing a large database for international achievement transformation services. The method comprises: collecting international achievement transformation service data and constructing a training database; determining subject categories, generating dynamic reports according to data access conditions, and obtaining data attention; constructing a coordinate system to define clusters, calculate the concentration degree and condition factors of project establishment, and obtain the probability of international achievement transformation; calculating the production rate, and obtaining the storage priority in combination with the probability of international achievement transformation; clustering according to the storage priority, and storing the achievement data in a storage medium 1 and a storage medium 2 respectively; avoiding the situation in which the same storage medium is still used to affect the system response speed when the data scale is large, and solving the problem of data quality affected by the diversification of data sources by a one-to-one classification and reconstruction method, thereby improving the overall system processing efficiency of international achievement transformation data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system and medium for constructing a large database for international achievement transformation services. Background Art

[0002] In the current era of information development, International Technology Transfer Services (ITTS) has not only promoted the process of scientific and technological innovation, but also greatly promoted economic development; its core task is to transfer the results of scientific and technological research and development from one region or organization to another, to maximize the utilization and commercial application of technology, and in this process, it is necessary to build an efficient, reliable and powerful big database as the basis and guarantee of ITTS goals.

[0003] However, there are some shortcomings in the construction of existing big databases. The diversity of data sources leads to uneven data quality, resulting in errors, omissions or inconsistencies in the data. The amount of data stored in the ITTS big database is huge, and the huge amount of data will lead to low processing efficiency in actual application, which in turn affects the response speed of the system. In addition, there is no division of data by importance. For data that requires a quick response, the expected response speed cannot be achieved due to the performance bottleneck of the storage medium. The response speed and processing capacity cannot be improved according to the importance of the data, and thus the real-time processing needs cannot be met. Therefore, traditional database systems cannot meet the actual needs of quick response and high efficiency. Summary of the invention

[0004] In order to solve the technical problems of low response speed and low processing efficiency of existing large databases for data, the purpose of the present invention is to provide a large database construction method for international achievement transformation services. The technical solution adopted is as follows:

[0005] Collect data from international achievement transformation services and form a training database;

[0006] Determine the subject category based on the training database, obtain different types of data access through the subject category to generate dynamic reports, and obtain the attention of each type of data through dynamic reports;

[0007] Construct a coordinate system of all the published results data in the training database and the time of publication and mark them to define different clusters, obtain the concentration of project establishment, obtain the number of published results data in different clusters, calculate the conditional factors in the process of international results transformation, and obtain the probability of international results transformation through data attention and conditional factors;

[0008] Calculate the production rate of international achievement transformation data based on the training database, and obtain the storage priority by combining the international achievement transformation probability and production rate;

[0009] The data types in the subject category are clustered according to the storage priority, and the achievement data with the first storage priority is stored in the storage medium one, and the remaining achievement data are stored in the storage medium two.

[0010] Furthermore, data from international achievement transformation services is collected and a training database is formed, including:

[0011] The data of the international achievement transformation service includes proprietary data and real-time data;

[0012] Identify different data sources, obtain proprietary data based on web crawler technology, obtain real-time data based on the API interface of the data source, integrate proprietary data with real-time data and import them into the database system to form a training database.

[0013] Furthermore, subject categories are determined based on the training database, and dynamic reports are generated by obtaining different types of data access conditions through subject categories, including:

[0014] Based on the training database, classification is performed according to classification rules to obtain different types of data access situations, and the classification rules include:

[0015] Classify and define the data category of international achievement transformation services based on data domains and application scenarios;

[0016] Secondary classification is made to determine the use category based on the data of international achievement transformation services in the same field category;

[0017] The subject categories are divided into three categories according to the data of international achievement transformation services in the same purpose category;

[0018] Deploy a real-time monitoring system to track different types of data access within a single subject category and generate dynamic reports.

[0019] Furthermore, the attention paid to each type of data is obtained through dynamic reports, including:

[0020] The number of data accesses within the short-term monitoring is obtained through dynamic reporting, and the data access frequency is obtained based on the monitoring time and the number of data accesses of the short-term monitoring. The data access frequency of each short-term monitoring within the monitoring period is calculated in turn, and then the data access frequency ratio of any two adjacent short-term monitoring within the monitoring period is calculated. The average value of the data access frequency ratio is obtained to obtain the data attention.

[0021] Furthermore, a coordinate system is constructed for all the published results data in the training database and the time of publication, and different clusters are marked and delineated to obtain the concentration of project establishment and the number of published results data in different clusters, including:

[0022] The time of publication in the coordinate system is clustered by K-means, and the optimal K value is obtained using the elbow method to delineate different clusters. The size of the cluster represents the degree of concentration of the project establishment. The geographical location of the clustered data after conversion is obtained, and the number of public achievement data of the projects established in different clusters is obtained.

[0023] Furthermore, the conditional factors in the process of international achievement transformation are calculated, and the corresponding calculation formula is:

[0024] ,

[0025] in, represents the conditional factor; Indicates the degree of centralization of project establishment; Indicates the number of achievement data.

[0026] Furthermore, the probability of international achievement transformation is obtained through the data attention and conditional factors. The corresponding calculation formula is:

[0027] ,

[0028] in, Representative Category of subject The probability of international achievement conversion of achievement data; It means the Conditional factors of the achievement data of the subject category in the actual transformation process; Indicates Category of subject The data of each outcome data is of high attention.

[0029] Furthermore, the production rate of international achievement transformation data is calculated based on the training database, and the storage priority is obtained by combining the international achievement transformation probability and the production rate, including:

[0030] The number of achievement transformation data in all subject categories in the training database is obtained as the total achievement transformation number, and the number of achievement transformation data in a single subject category is obtained as the single achievement transformation number. The ratio of the single achievement transformation number to the total achievement transformation number is the production rate of international achievement transformation data.

[0031] The calculation formula for storage priority is:

[0032] ,

[0033] in, Indicates Category of subject Storage priority of individual achievement data; Indicates Category of subject The probability of international achievement conversion of achievement data; represents the hyperbolic tangent function; Indicates The result data belongs to The production rate of international results transformation data in the subject category.

[0034] The present invention also provides a large database construction system for international achievement transformation services, including the following modules:

[0035] Data collection module, used to: collect data of international achievement transformation services and form a training database;

[0036] The data analysis module is used to: determine the subject category based on the training database, obtain different types of data access conditions through the subject category to generate a dynamic report, and obtain the attention of each type of data through the dynamic report; construct a coordinate system with all the publicly available achievement data in the training database and the release time and mark them to delineate different clusters, obtain the concentration of project establishment, obtain the number of publicly available achievement data in different clusters, calculate the conditional factors in the process of international achievement transformation, and obtain the probability of international achievement transformation through data attention and conditional factors; calculate the production rate of international achievement transformation data based on the training database, and obtain the storage priority by combining the international achievement transformation probability and production rate;

[0037] The data storage module is used to cluster the data types in the subject category according to the storage priority, obtain the achievement data of the first storage priority and store it in the storage medium one, and store the remaining achievement data in the storage medium two.

[0038] The present invention also provides a medium storing program data, which, when executed, implements the method for constructing a large database of international achievement transformation services as described in any of the above items.

[0039] The present invention has the following beneficial effects:

[0040] 1. By collecting different data to form a training database, and then classifying and determining different subject categories, analyzing different data types within any subject category to obtain data attention; then constructing different clusters for data analysis to obtain the actual situation of achievement transformation taking into account the time span and specific area, so as to determine the probability of international achievement transformation under high attention, and then calculate the productivity of achievement transformation, that is, the actual application value of the data. The two are combined and calculated to obtain the storage priority, and clustered, and stored in storage medium 1 and storage medium 2 respectively, avoiding the situation where the same storage medium is still used to affect the system response speed when the data scale is large, and solving the problem of data quality affected by diversified data sources through one-to-one classification and reconstruction. Category-based analysis reduces the occurrence of data errors, improves the overall system processing efficiency of international achievement transformation data, and effectively improves the response speed of the large-scale database system of international achievement transformation services and the reliability of data processing, so as to ensure that the constructed large database can quickly respond to large-scale data and efficiently operate, thereby meeting real-time requirements.

[0041] 2. The big database construction system and medium for international achievement transformation services provided by the present invention have the same beneficial effects as the big database construction method for international achievement transformation services provided by the present invention, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flowchart of the steps of a method for constructing a large database for international achievement transformation services provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a method for constructing a large database for international achievement transformation services provided by an embodiment of the present invention, in which different clusters are delineated by a coordinate system;

[0045] Figure 3 A schematic diagram of the aggregation of achievement data of clusters determined by K-means clustering in a method for constructing a large database of international achievement transformation services provided by an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of hierarchical clustering based on storage priority in a method for constructing a large database of international achievement transformation services provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method, system and medium for building a large database of international achievement transformation services proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The following specifically describes a method, system and medium for constructing a large database for international achievement transformation services provided by the present invention in conjunction with the accompanying drawings.

[0050] The existing ITTS big database construction method has data errors and the like due to the diverse data sources and uneven data quality, and the large amount of data. In the actual application process, the big database processing efficiency is low, the response speed is slow, and efficient operation cannot be achieved. In one embodiment of the present invention, a big database construction method for international achievement transformation services is provided. The method collects different data to form a training database, and then classifies and determines different subject categories, analyzes different data types in any subject category, and obtains the data attention. Then, different clusters are constructed for data analysis to obtain the actual situation of achievement transformation considering the time span and specific area, so as to determine the probability of international achievement transformation under high attention, and then calculate the production rate of achievement transformation, that is, the actual application value of the data, and the two are combined and calculated to obtain the storage priority, and clustered, and stored in storage medium one and storage medium two respectively, which effectively improves the response speed and credibility of the big database of international achievement transformation services. In order to realize a big database construction method for international achievement transformation services, a big database construction system and medium for international achievement transformation services are provided. The system and medium are essentially a software system, which is composed of modules that realize corresponding functions. The specific steps in the method are now introduced in detail.

[0051] As an optional implementation method, the data involved in this embodiment are all records of projects or achievement conversion supported by public funds, etc. Such data can be obtained through official channels such as scientific research management agencies, scientific research funding agencies or patent databases, thereby simplifying the complexity of obtaining individual non-public data.

[0052] See also Figure 1, which shows a flowchart of the steps of a method for building a large database for international achievement transformation services provided by an embodiment of the present invention, the method comprising:

[0053] Step S1: Collect data of international achievement transformation services and form a training database;

[0054] Step S2: Determine the subject category based on the training database, obtain different types of data access conditions through the subject category to generate a dynamic report, and obtain the attention of each type of data through the dynamic report;

[0055] Step S3: Construct a coordinate system of all the published achievement data in the training database and the time of publication and mark them to define different clusters, obtain the concentration of project establishment, obtain the number of achievement data published in different clusters, calculate the conditional factors in the process of international achievement transformation, and obtain the probability of international achievement transformation through data attention and conditional factors;

[0056] Step S4: Calculate the production rate of international achievement transformation data based on the training database, and obtain the storage priority by combining the international achievement transformation probability and the production rate;

[0057] Step S5: Cluster the data types in the subject category according to the storage priority, obtain the achievement data of the first storage priority and store them in the storage medium one, and store the remaining achievement data in the storage medium two.

[0058] It is explained that international achievement transformation services refer to the process of transforming intangible assets such as scientific research results, technological innovations and intellectual property rights into actual productivity and commercial value on a global scale. It not only involves technology transfer and commercialization, but also includes market research, project evaluation, financial support, policy consultation, legal protection and other aspects. Through international achievement transformation services, the flow and application of scientific and technological achievements on a global scale can be promoted, which can promote the economic development and scientific and technological progress of various countries. Big database refers to a database that can store and manage massive data, which can support complex data processing and analysis tasks.

[0059] The construction of a large database for international achievement transformation services allows researchers, entrepreneurs, investors and policymakers to easily obtain and share various types of information, promoting the efficient transformation and application of scientific and technological achievements on a global scale. It is used to break information silos, achieve optimal resource allocation and cross-regional and cross-industry collaborative innovation. It not only provides a storage space for massive data, but also has powerful data processing and analysis capabilities, which can help users quickly find the information they need and provide users with personalized services through intelligent recommendation systems. Due to its international characteristics, it supports multi-language interfaces, which is convenient for users in different regions to use, further promoting international scientific and technological cooperation and exchanges, and building an efficient and convenient information bridge for global scientific and technological innovation and industrial development.

[0060] Preferably, step S1 includes:

[0061] The data of international achievement transformation services include proprietary data and real-time data;

[0062] Identify different data sources, obtain proprietary data based on web crawler technology, obtain real-time data based on the API interface of the data source, integrate proprietary data with real-time data and import them into the database system to form a training database.

[0063] To be clear, proprietary data refers to information that is unique and exclusive, owned by specific institutions or individuals, and protected by law. It is not easily accessible to the outside world, such as patent information, trade secrets, technical know-how, and other intellectual property-related data; real-time data refers to information that can reflect the current and latest status, and is highly timely and quickly updated, including market dynamics, industry trends, technological progress, and other information that can be obtained instantly.

[0064] Web crawler technology is a program or script that automatically crawls information from the Internet. It simulates the behavior of human users and visits web pages and extracts the required data and information from them according to certain rules and algorithms. First, the crawler program starts with one or more initial URLs (Uniform Resource Locators), which are called seed URLs. The crawler visits the web pages corresponding to these URLs, parses the web page content, and extracts the links therein. Then, according to preset rules and strategies, it selects a part of the links to visit and continues to parse the content of the newly visited web pages. This process will be repeated until a specific stop condition is met, such as reaching the set crawling depth, crawling quantity or crawling time.

[0065] The API interface of a data source refers to the Application Programming Interface, which allows different software systems to interact and exchange data. Through these interfaces, users can obtain, send and process data to achieve various functions and applications. The API interface of a data source usually provides standardized request and response formats, making data acquisition and processing more efficient and convenient.

[0066] It can be explained that the data sources include academic databases, patent databases, market research institutions, scientific research institutions and technology transfer platforms.

[0067] Specifically, we can identify different data sources such as academic databases, patent databases, market research institutions, scientific research institutions and technology transfer platforms, and utilize open data platforms and public databases, such as Google Scholar, PubMed (Public MEDLINE), IEEE Xplore (IEEE Xplore Digital Library), and WIPO (World Intellectual Property Organization), which provide a large number of documents, papers, patents and conference records, covering the latest research results in various disciplines and fields. We can also use crawler technology to automatically crawl information and obtain proprietary data from them. We can also obtain real-time data by calling the API interfaces of various data sources, for example, using the API of the patent database to obtain the latest patent information, or using the API of academic articles to retrieve the latest research results. We can also download available data files directly from the data source website. These files are usually in the format of CSV (Comma-Separated Values), JSON (JavaScript Object Notation), XML (Extensible Markup Language), etc. The proprietary data and real-time data are integrated and then imported into the database system to form a training database for subsequent data analysis.

[0068] Understandably, when constructing a big database for international achievement transformation services, advanced data integration, analysis and management technologies are mainly used to enhance the efficiency and reliability of ITTS. In the process of building a big database, there are challenges in storing and managing large amounts of data. Traditional storage methods often make all data rely on a single storage medium, which may lead to a large amount of redundant information for specific needs when the database system executes queries, making it difficult to accurately obtain specific categories of achievement transformation information, thereby reducing the response speed of the big database system. However, in the construction of a big database, different types of data may present different access patterns. The difference in access frequency reflects the importance of each type of data to a certain extent. Generally, data with a higher access frequency tends to be more important.

[0069] Preferably, in step S2, the subject category is determined based on the training database, and different types of data access situations are obtained through the subject category to generate a dynamic report, including:

[0070] Step S21: Classify according to classification rules based on the training database to obtain different types of data access conditions, wherein the classification rules include:

[0071] Classify and define the data category of international achievement transformation services based on data domains and application scenarios;

[0072] Secondary classification is made to determine the use category based on the data of international achievement transformation services in the same field category;

[0073] The subject categories are divided into three categories according to the data of international achievement transformation services in the same purpose category;

[0074] Step S22: deploy a real-time monitoring system to track different types of data access in a single subject category and generate dynamic reports.

[0075] It can be explained that the field category refers to scientific research data, business data, medical data, financial data, educational data, etc.; the purpose category refers to patent information, scientific research papers, market data, etc.; the subject category refers to natural sciences, engineering technology, social sciences, humanities, life sciences, etc.

[0076] Specifically, the data in the training database is first classified according to the classification rules, where three classifications are made in the classification rules, and the classification is assisted by a machine learning algorithm. The machine learning algorithm is a technology that simulates the human learning process through computer programs, enabling the computer to automatically improve its performance through data and experience without explicit instructions; when a large amount of data is input, statistical and probability models are used to identify patterns and regularities in the data, thereby achieving tasks such as prediction, classification, and clustering.

[0077] After defining the field categories, the data in the same field category are divided twice to obtain the purpose categories; then the same purpose category is divided three times to obtain the subject categories. Among them, the subject categories can be further divided according to the year or region of the data of the international achievement transformation service to obtain easier-to-index data. Different subject categories also include different types of data such as video, audio, documents, and pictures according to their unique characteristics and formats.

[0078] Finally, by deploying a real-time monitoring system, we track the real-time access to different types of data under a single subject category, including but not limited to real-time monitoring of key indicators such as data access frequency, access time, access source, and access duration, so as to better understand the performance and value of data in practical applications; and generate dynamic reports to display key indicators and trends such as data access statistics, analysis of peak access periods, and distribution of access sources in graphical or tabular form, so that users can quickly grasp the real-time usage of data.

[0079] Preferably, in step S2, the attention level of each type of data is obtained through dynamic reporting, including:

[0080] The number of data accesses within the short-term monitoring is obtained through dynamic reporting, and the data access frequency is obtained based on the monitoring time and the number of data accesses of the short-term monitoring. The data access frequency of each short-term monitoring within the monitoring period is calculated in turn, and then the data access frequency ratio of any two adjacent short-term monitoring within the monitoring period is calculated. The average value of the data access frequency ratio is obtained to obtain the data attention.

[0081] It should be noted that short-term monitoring refers to real-time monitoring within a short period of time. By analyzing the number of data accesses in short-term monitoring, the error in the data access process can be effectively reduced; data attention can reflect the changes in the popularity and attention of the data in the training database.

[0082] Specifically, the number of data accesses G in short-term monitoring is obtained through dynamic reporting. The monitoring time t of short-term monitoring can be 4 hours. The data access frequency is obtained based on the monitoring time and the number of data accesses, that is, , the larger the value, the more times the data of this type is accessed during the monitoring time; then the data access frequency of each short-term monitoring within the monitoring time is calculated in turn, and the monitoring time can be half a year, and then the data access frequency ratio of any two adjacent short-term monitoring within the monitoring time is calculated, that is, the ratio of the data access frequency of the current short-term monitoring to the data access frequency of the previous short-term monitoring; then the average value is calculated based on all the ratios and recorded as JU, which means the data attention, which refers to the level of attention paid to this type of data during the monitoring time. The larger the value, the higher the data attention JU of this type of data under the subject category.

[0083] Understandably, data access conditions cannot well express the efficiency of data conversion under this subject category, that is, relying solely on data access conditions cannot fully and accurately reflect the problem; for example, a scientific document has a high access frequency and data attention within a specific period of time, which does not necessarily mean that the actual conversion rate of the scientific document is also high. High access frequency and high data attention only indicate that there is a high level of interest and attention to this type of data, but it is not directly equivalent to the actual conversion effect; therefore, it is necessary to combine other specific indicators and data for comprehensive analysis and evaluation.

[0084] Please combine Figure 2 and Figure 3 Preferably, in step S3, a coordinate system is constructed for all the published achievement data in the training database and the time of publication, and different clusters are marked and demarcated to obtain the concentration of project establishment, and the number of achievement data published by the project establishment in different clusters is obtained, including:

[0085] The time of publication in the coordinate system is clustered by K-means, and the optimal K value is obtained using the elbow method to delineate different clusters. The size of the cluster represents the degree of concentration of the project establishment. The geographical location of the clustered data after conversion is obtained, and the number of public achievement data of the projects established in different clusters is obtained.

[0086] Optionally, in this embodiment, all the disclosed achievement data in the training database are the proprietary data in the aforementioned step S1 and the disclosed achievement data in the real-time data, such as a research result that has been published in an academic journal, or proprietary data such as disclosed patent information; real-time data such as charts and reports from the real-time monitoring system that are processed and organized and published on a public platform.

[0087] To explain, K-means clustering is an unsupervised machine learning algorithm, which is mainly used to divide the samples in the data set into K clusters. Its core idea is to make the distance between the sample points in each cluster as small as possible, and the distance between the sample points in different clusters as large as possible through iterative optimization; specifically, the K-means algorithm first randomly selects K initial center points, and then assigns each sample point to the cluster represented by the nearest center point; then the algorithm recalculates the center point position of each cluster, that is, the mean of all sample points in the cluster, and repeats this process until the center point position no longer changes or reaches the preset number of iterations.

[0088] The elbow method finds a compromise point by calculating the clustering effect under different K values, so that the clustering effect and computational complexity achieve a good balance; first, select a suitable distance measurement method, such as Euclidean distance, and then calculate the distance from each point in the data set to each cluster center; then gradually increase the number of clusters K, starting from the smallest K value, usually starting from 1, and then gradually increase to obtain the clustering result, and calculate the sum of the squares of the distance from each point to its nearest cluster center, that is, SSE (Sum of Squared Errors); by drawing a graph, the horizontal axis represents the number of clusters K, and the vertical axis represents the corresponding SSE value. As the K value increases, the SSE value gradually decreases; when the K value increases to a certain extent, the rate of decrease of the SSE value will significantly slow down, or even tend to be flat. At this time, the shape of the curve on the graph is similar to an elbow, and the K value corresponding to the elbow is the optimal number of clusters, that is, the optimal K value.

[0089] It can be explained that the K-means algorithm is used to cluster the release time in the coordinate system, and the data points consisting of the achievement data and the release time in the coordinate system are assigned to K clusters, so that the sum of the distances between each data point and its cluster center is minimized; the optimal K value is determined by the elbow method, so that the sum of squared errors within the cluster is minimized, while avoiding overfitting, so as to divide the data into different clusters. The size of each cluster can reflect the concentration of the project, and further understand the geographical location of the achievement data after transformation. Specifically, we obtained the geographical location information after the achievement data was transformed and mapped it to the corresponding cluster; the number of achievement data published by the project in different clusters was obtained, and the project activity and achievement output of each cluster were evaluated for subsequent data processing.

[0090] Specifically, all the published achievement data in the training database are constructed into a coordinate system according to the release time and marked. The release time in the coordinate system, that is, the publicly available project establishment time, is clustered by K-means, and the elbow method is used to obtain the optimal K value to delineate different clusters. The size of the cluster, that is, the number of achievement data in the cluster, is expressed as the concentration of the project establishment. It can be explained that the performance time of the cluster in the coordinate system is that in the actual public project establishment time, there are different levels of clustering, and different clusters represent the aggregation of the public project establishment time, that is, there may be relatively intensive project establishment behavior in a period of time. Then the actual project establishment results in all clusters are matched with the release time of the results. The release time of the results in the matching process is earlier than the project establishment time, because in the process of achievement transformation, the actual results must be made public first in order to allow The public understands and recognizes these achievements. Only after the achievements are fully demonstrated and recognized can the process of achievement transformation be further promoted through project establishment, that is, by matching the accurate data correspondence between the release time of the achievement data and the project establishment time, the two information can be cross-compared to ensure that each project achievement can find its corresponding release time to obtain the corresponding record, so as to have a clearer understanding of the distribution of achievements in each cluster and improve the efficiency of cluster size acquisition; based on this, the division results of the project establishment time that have been made public are used to obtain the size of different clusters, that is, the number of achievement data with the same time point contained in the cluster, recorded as Y, that is, the number of achievement data with the same time point in any cluster is Y, which represents the concentration of project establishment, and quantitatively understands the density of project establishment in this time period. The larger Y is, the higher the concentration of project establishment in the cluster currently analyzed.

[0091] Then, the geographical location clustering of the converted achievement data is obtained, wherein the geographical location clustering refers to the distribution of the converted achievement data in a specific geographical area and / or space, which is used to understand the distribution characteristics of the achievement data and facilitate the subsequent similarity judgment; it can be explained that, for a single type of achievement data in the analyzed subject category, a similarity judgment is made between this type of achievement data and the already published achievement data, wherein a large language model is used to make a similarity judgment between the name or specific summary description of this type of achievement data and the name or specific summary description of the already published achievement data; the large language model is used to understand and generate natural language text, and through a large amount of data training, it captures the complexity and diversity of language to achieve efficient similarity judgment.

[0092] Optionally, for the published achievement data of the specific cluster, the published achievement data with the highest similarity is identified, and the size of the cluster to which the corresponding achievement data belongs is the beneficial effect of achievement transformation of this type of achievement data, that is, the concentration degree of project establishment; the regions where all achievement data in the same subject category are converted are marked, and based on the above-mentioned division, the geographical location clustering of the results after the conversion of different types of data in the same subject category is obtained to reveal the geographical distribution characteristics of the achievement data transformation.

[0093] Then, based on the different defined clusters, the number of project results data that have been made public is obtained by statistics. That is, based on the results of matching the actual project results in all clusters with the release time of the results, the specific number of project results data that have been made public in any cluster can be statistically obtained, denoted as H. The number of projects disclosed in the corresponding cluster can indirectly measure the results conversion capacity in the cluster, that is, the more project results data that have been made public in the cluster, the stronger the results conversion capacity of the cluster. Then, the regional location of any type of data is matched with all cluster results in distance. The results conversion capacity of the cluster that is closest can also provide data support for the results conversion capacity of the current analysis of this type of data.

[0094] It can be understood that in step S2, when any type of achievement data in the subject category has a relatively concentrated project establishment behavior within a certain period of time and within a certain area, this indicates that this type of achievement data has better incubation conditions and environment in the process of achievement transformation; for example, including phased favorable policy support, increased market demand, optimization of industrial layout and rapid development of related technologies; therefore, only when the scientific research results published and established within such a period of time with better incubation conditions and within a geographical location are visited with high frequency, can they truly represent the access value or effectiveness of each achievement data, that is, through high-frequency visits, it can be determined which achievement data have higher value and effectiveness in practical applications, thereby providing strong support for the transformation of achievement data.

[0095] Preferably, the conditional factors in the process of international achievement transformation are calculated, and the corresponding calculation formula is:

[0096] ,

[0097] in, represents the conditional factor; Indicates the degree of centralization of project establishment; Indicates the number of achievement data.

[0098] It should be explained that conditional factors refer to the relevant factors that influence and restrict the success of transformation during the process of international achievement transformation.

[0099] It can be explained that the larger the condition factor is, the more likely it is that the data of the subject category currently being analyzed has better conditions and environment for concentrated project establishment and incubation of results within a certain time period and a certain geographical area. The larger the condition factor is, the more credible the access to this type of data is.

[0100] Preferably, the probability of international achievement transformation is obtained through data attention and conditional factors, and the corresponding calculation formula is:

[0101] ,

[0102] in, Representative Category of subject The probability of international achievement conversion of achievement data; It means the Conditional factors of the achievement data of the subject category in the actual transformation process; Indicates Category of subject The data of each outcome data is of high attention.

[0103] It is clarified that the probability of international achievement transformation refers to the successful conversion rate of achievement data from the laboratory stage to the commercial application stage on a global scale. It reflects the transformation efficiency from theoretical research to practical application, and involves multiple links, including technology development, market verification, capital investment, policy support, etc.; a high probability of international achievement transformation means that more research results can smoothly enter the market and promote economic development and social progress.

[0104] It can be understood that, combined with the calculation formula of the conditional factor, when When the incubation environment of the subject category is good, the The result data has a high access frequency, which means Category of subject The greater the probability of achievement conversion of individual achievement data.

[0105] Preferably, step S4 includes:

[0106] The number of achievement transformation data in all subject categories in the training database is obtained as the total achievement transformation number, and the number of achievement transformation data in a single subject category is obtained as the single achievement transformation number. The ratio of the single achievement transformation number to the total achievement transformation number is the production rate of international achievement transformation data.

[0107] The calculation formula for storage priority is:

[0108] ,

[0109] in, Indicates Category of subject Storage priority of individual achievement data; Indicates Category of subject The probability of international achievement conversion of achievement data; represents the hyperbolic tangent function; Indicates The result data belongs to The production rate of international results transformation data in the subject category.

[0110] To clarify, productivity refers to the efficiency of converting data from a storage state to an actual application state, that is, the actual application value; a high productivity means that data can be more effectively applied to business processes, decision support or user services, thereby bringing higher value and benefits.

[0111] Specifically, we collect the achievement transformation data in all subject categories from the training database, that is, the total number of achievement transformations successfully transformed into commercial products, denoted as H; then we obtain the number of achievement transformation data in a single subject category, that is, the number of single achievement transformations, denoted as g, and calculate the production rate of this type of achievement data under this subject category based on the data of both, denoted as JL, that is, The larger the value of JL is, the higher the actual application value of the achievement data in this subject category is, and it should have a higher status in the international achievement transformation service and a faster response speed of the database system.

[0112] It is understandable that in the existing big database built based on international achievement transformation services, because all data use the same storage medium, inefficient access may occur in the actual data access process, resulting in waste of resources in the data storage process, so it is necessary to calculate the storage priority of the achievement data.

[0113] To clarify, storage priority refers to a method of classifying and sorting data of different types or importance in a computer system or data management process; by prioritizing, space can be reasonably allocated within limited storage resources to ensure that key data is stored and processed first, which helps to improve the overall performance and efficiency of the system, enable faster reading and writing, and reduce data access delays.

[0114] Indicates Category of subject The international achievement conversion probability of the achievement data and the The result data belongs to The Euclidean norm of the production rate of the achievement data in the subject category, where the Euclidean norm is used to measure the comprehensive degree of these two important indicators. It can reflect the comprehensive reference value between the actual conversion probability and the actual application value of the achievement data. The larger the value, the higher the storage priority, that is, it is worthy of priority consideration and storage.

[0115] It can be explained that after determining the storage priority of all achievement data in the same subject category based on the above data analysis, a large database is then built. Based on the storage priority, the database architecture, including table structure, indexes, and relationships, is designed. High-priority data should be designed as a more efficient storage and access mode to facilitate efficient storage and fast retrieval; for example, for fields that are frequently used in query conditions, consider using appropriate data types and creating indexes for them to reduce the amount of calculation during querying and improve response speed.

[0116] Then, based on the data access situation obtained above, select the appropriate database technology, such as relational database, NoSQL (Not Only SQL) database, etc. Among them, relational databases, such as MySQL (My Structured Query Language), Oracle (Oracle Database) and SQL Server (Microsoft SQL Server), etc., are based on the relational model and use tables to organize data. Each table consists of rows and columns, rows represent records, and columns represent fields. It uses SQL (Structured Query Language) to query, insert, update and delete data.

[0117] NoSQL databases, such as MongoDB (Mongo Database), Redis (Remote Dictionary Server) and Cassandra (Apache Cassandra, an open source distributed NoSQL database system), are a general term for non-relational databases that do not use traditional tables to store data. They are divided into multiple types, such as key-value storage, document storage, column storage and graph databases. Each type is suitable for different application scenarios. They usually have the characteristics of high scalability and high performance, and can handle large-scale data and high-concurrency requests.

[0118] Then, all the data in the training database are migrated to the constructed large database according to the storage priority, ensuring that the high storage priority achievement data are imported first. Then, different types of achievement data in the same subject category are divided into their corresponding storage media, and necessary data cleaning and format conversion are performed; among them, data cleaning includes steps such as removing duplicate data, correcting erroneous data, and filling in missing data, while format conversion involves converting data from one format to another to ensure the accuracy and consistency of the data, which is convenient for subsequent data processing and analysis.

[0119] See also Figure 4 In step S5, the data types in the subject category are clustered according to the storage priority, and the achievement data of the first storage priority is obtained and stored in the storage medium one, and the remaining achievement data are stored in the storage medium two.

[0120] To explain, clustering is an unsupervised learning technique used to group objects in a data set so that objects in the same group (cluster) have a higher similarity, while objects in different groups have a lower similarity. It is to divide the data set into multiple clusters by analyzing the inherent structure and characteristics of the data, thereby revealing patterns or relationships in the data.

[0121] Storage media refers to various physical devices or materials used to store data and information. They can be internal, such as a computer's hard disk drive (HDD) or solid-state drive (SSD); or external, such as USB flash drives, external hard disks, optical disks (such as CDs and DVDs), and cloud storage services.

[0122] Specifically, all achievement data under the same subject category are clustered according to the size of storage priority, and then all achievement data are classified in order from large to small according to the average storage priority of all achievement data in each cluster: type 1, type 2..., that is, a ladder-like achievement data display diagram is obtained, and the achievement data at the top of the ladder has a higher storage priority, that is, after the clustering is completed, a ladder-like clustering diagram is generated, in which each ladder represents a cluster, and the achievement data at the top of the ladder corresponds to the highest level of clustering results in the entire clustering process. In this embodiment, type 1 is at the top of the ladder, and from top to bottom are type 2, type 3... The achievement data of the first storage priority is obtained and stored in storage medium one, that is, the achievement data of the first storage priority, referring to type 1, is stored in a separate storage medium; then the remaining achievement data are all stored in storage medium two, that is, the remaining types 2, type 3... are stored in the same storage medium.

[0123] It can be explained that the storage medium only stores the achievement data of the first storage priority. The data volume is small, and the system load rate is relatively low. It can bear the response requirements of high access frequency, so that the constructed big database responds to the achievement data in the storage medium one relatively quickly; and in the process of the big database responding to the achievement data request, it will provide priority recommendations and provide the achievement data stored in the storage medium one according to the actual subject query requirements, so that the response results of the big database are the most important and prominent achievement data for different subject categories, so as to better meet the needs of users.

[0124] As an optional implementation method, data access permissions and security measures are set for access to the big database to protect the security of high-priority achievement data. Specifically, strict permission management will be implemented for these achievement data to ensure that only authorized users can access and operate these achievement data, such as encryption, firewalls and intrusion detection systems, to prevent unauthorized access and data leakage; then comprehensive testing will be carried out to verify the functions, performance and data integrity of the big database to ensure that all high storage priority achievement data can be processed normally and quickly, that is, to establish an efficient, secure and reliable large-scale database system to provide users with a stable data access experience while ensuring the security and integrity of high-priority achievement data.

[0125] It can be understood that by collecting different data to form a training database, and then classifying and determining different subject categories, different data types within any subject category are analyzed to obtain the data attention; then different clusters are constructed for data analysis to obtain the actual situation of achievement transformation taking into account the time span and specific area, so as to determine the probability of international achievement transformation under high attention, and then calculate the productivity of achievement transformation, that is, the actual application value of the data. The two are combined and calculated to obtain the storage priority, and clustered, and stored in storage medium one and storage medium two respectively, avoiding the situation where the same storage medium is still used to affect the system response speed when the data scale is large, and the problem of data quality being affected by the diversification of data sources is solved through one-to-one classification and reconstruction. Category-based analysis reduces the occurrence of data errors, improves the overall system processing efficiency of international achievement transformation data, and effectively improves the response speed and data processing reliability of the large-scale database system of international achievement transformation services, so as to ensure that the constructed large database can quickly respond to large-scale data and efficiently operate, thereby meeting real-time requirements.

[0126] An embodiment of the present invention further provides a large database construction system for international achievement transformation services, comprising the following modules:

[0127] Data collection module, used to: collect data of international achievement transformation services and form a training database;

[0128] The data analysis module is used to: determine the subject category based on the training database, obtain different types of data access conditions through the subject category to generate a dynamic report, and obtain the attention of each type of data through the dynamic report; construct a coordinate system with all the publicly available achievement data in the training database and the release time and mark them to delineate different clusters, obtain the concentration of project establishment, obtain the number of publicly available achievement data in different clusters, calculate the conditional factors in the process of international achievement transformation, and obtain the probability of international achievement transformation through data attention and conditional factors; calculate the production rate of international achievement transformation data based on the training database, and obtain the storage priority by combining the international achievement transformation probability and production rate;

[0129] The data storage module is used to cluster the data types in the subject category according to the storage priority, obtain the achievement data of the first storage priority and store it in the storage medium one, and store the remaining achievement data in the storage medium two.

[0130] It is explained that the data collection module allows users to obtain various data to ensure the integrity and real-time nature of the data, which is convenient for subsequent processing; the data analysis module is used to perform in-depth processing and analysis of the data, extract valuable information from massive data, and assist users in analyzing the relationship between data; the data storage module is responsible for persistent storage of the analyzed data, and should be based on the data retrieval requirements to ensure the security, consistency and scalability of the data, so that it can quickly respond to various queries and data access requests, and improve the overall reliability of the system.

[0131] It can be understood that when the modules of a big database construction system for international achievement transformation services are in operation, it is necessary to utilize a big database construction method for international achievement transformation services provided in the aforementioned embodiment. Therefore, whether the data collection module, the data analysis module and the data storage module are integrated or different hardware is configured to produce functions similar to the effects achieved by the present invention, they all fall within the scope of protection of the present invention.

[0132] An embodiment of the present invention further proposes a medium, which stores program data. When the program data is executed, it implements the method for building a big database of international achievement transformation services as described in the aforementioned embodiment; the medium has the same beneficial effects as the method for building a big database of international achievement transformation services provided above, and will not be repeated here.

[0133] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0134] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for constructing a large database for international achievement transformation services, characterized in that: The method comprises: Collect data from international achievement transformation services and form a training database; Determine the subject category based on the training database, obtain different types of data access through the subject category to generate dynamic reports, and obtain the attention of each type of data through dynamic reports; Construct a coordinate system of all the published results data in the training database and the time of publication and mark them to define different clusters, obtain the concentration of project establishment, obtain the number of published results data in different clusters, calculate the conditional factors in the process of international results transformation, and obtain the probability of international results transformation through data attention and conditional factors; Calculate the production rate of international achievement transformation data based on the training database, and obtain the storage priority by combining the international achievement transformation probability and production rate; Clustering the data types in the subject category according to the storage priority, obtaining the achievement data with the first storage priority and storing them in the storage medium 1, and storing the remaining achievement data in the storage medium 2; Collect data from international achievement transformation services and form a training database, including: The data of the international achievement transformation service includes proprietary data and real-time data; Identify different data sources, obtain proprietary data based on web crawler technology, obtain real-time data based on the API interface of the data source, integrate proprietary data with real-time data and import them into the database system to form a training database; The coordinate system of all the published results data in the training database and the release time is constructed and marked to define different clusters, the concentration of project establishment is obtained, and the number of published results data in different clusters is obtained, including: K-means clustering is performed on the time of publication in the coordinate system, and the optimal K value is obtained using the elbow method to delineate different clusters. The size of the cluster represents the concentration of the project establishment, and the clustering of the achievement data in geographical locations after conversion is obtained, and the number of achievement data published by the project establishment in different clusters is obtained; To calculate the conditional factors in the process of international achievement transformation, the corresponding calculation formula is: ； in, represents the conditional factor; Indicates the degree of centralization of project establishment; Indicates the number of achievement data; The probability of international achievement transformation is obtained through data attention and conditional factors. The corresponding calculation formula is: ； in, Representative Category of subject The probability of international achievement conversion of achievement data; It means the Conditional factors of the achievement data of the subject category in the actual transformation process; Indicates Category of subject The data of each outcome data is of high attention.

2. The method for constructing a large database for international achievement transformation services according to claim 1, characterized in that: Based on the training database, the subject categories are determined, and dynamic reports are generated based on different types of data access conditions obtained through subject categories, including: Based on the training database, classification is performed according to classification rules to obtain different types of data access situations, and the classification rules include: Classify and define the data category of international achievement transformation services based on data domain and application scenarios; Secondary classification is made to determine the use category based on the data of international achievement transformation services in the same field category; The subject categories are divided into three categories according to the data of international achievement transformation services in the same purpose category; Deploy a real-time monitoring system to track different types of data access within a single subject category and generate dynamic reports.

3. The method for constructing a large database for international achievement transformation services according to claim 2, characterized in that: Get the attention of each type of data through dynamic reports, including: The number of data accesses within the short-term monitoring is obtained through dynamic reporting, and the data access frequency is obtained based on the monitoring time and the number of data accesses of the short-term monitoring. The data access frequency of each short-term monitoring within the monitoring period is calculated in turn, and then the data access frequency ratio of any two adjacent short-term monitoring within the monitoring period is calculated. The average value of the data access frequency ratio is obtained to obtain the data attention.

4. The method for constructing a large database for international achievement transformation services according to claim 1, characterized in that: The production rate of international achievement transformation data is calculated based on the training database, and the storage priority is obtained by combining the international achievement transformation probability and the production rate, including: The number of achievement transformation data in all subject categories in the training database is obtained as the total achievement transformation number, and the number of achievement transformation data in a single subject category is obtained as the single achievement transformation number. The ratio of the single achievement transformation number to the total achievement transformation number is the production rate of international achievement transformation data. The calculation formula for storage priority is: ； in, Indicates Category of subject Storage priority of individual achievement data; Indicates Category of subject The probability of international achievement conversion of achievement data; represents the hyperbolic tangent function; Indicates The result data belongs to The production rate of international results transformation data in the subject category.

5. A large database construction system for international achievement transformation services, characterized in that: Includes the following modules: Data collection module, used to: collect data of international achievement transformation services and form a training database; The data analysis module is used to: determine the subject category based on the training database, obtain different types of data access conditions through the subject category to generate a dynamic report, and obtain the attention of each type of data through the dynamic report; construct a coordinate system with all the publicly available achievement data in the training database and the release time and mark them to delineate different clusters, obtain the concentration of project establishment, obtain the number of publicly available achievement data in different clusters, calculate the conditional factors in the process of international achievement transformation, and obtain the probability of international achievement transformation through data attention and conditional factors; calculate the production rate of international achievement transformation data based on the training database, and obtain the storage priority by combining the international achievement transformation probability and production rate; The data storage module is used to: cluster the data types in the subject category according to the storage priority, obtain the achievement data of the first storage priority and store them in the storage medium 1, and store the remaining achievement data in the storage medium 2; Collect data from international achievement transformation services and form a training database, including: The data of the international achievement transformation service includes proprietary data and real-time data; Identify different data sources, obtain proprietary data based on web crawler technology, obtain real-time data based on the API interface of the data source, integrate proprietary data with real-time data and import them into the database system to form a training database; The coordinate system of all the published results data in the training database and the release time is constructed and marked to define different clusters, the concentration of project establishment is obtained, and the number of published results data in different clusters is obtained, including: K-means clustering is performed on the time of publication in the coordinate system, and the optimal K value is obtained using the elbow method to delineate different clusters. The size of the cluster represents the concentration of the project establishment, and the clustering of the achievement data in geographical locations after conversion is obtained, and the number of achievement data published by the project establishment in different clusters is obtained; To calculate the conditional factors in the process of international achievement transformation, the corresponding calculation formula is: ； in, represents the conditional factor; Indicates the degree of centralization of project establishment; Indicates the number of achievement data; The probability of international achievement transformation is obtained through data attention and conditional factors. The corresponding calculation formula is: ； in, Representative Category of subject The probability of international achievement conversion of achievement data; It means the Conditional factors of the achievement data of the subject category in the actual transformation process; Indicates Category of subject The data of each outcome data is of high attention.

6. A medium, characterized in that The medium stores program data, and when the program data is executed, the method for constructing a large database for international achievement transformation services as described in any one of claims 1 to 4 is implemented.

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