CN117592943A - Science and technology service data collaboration system based on Internet - Google Patents
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Abstract
The invention relates to the technical field of digital data processing, in particular to an internet-based science and technology service data collaboration system, which comprises a data collection module, a data analysis module, a policy matching module and a capital recommendation module, wherein the data collection module, the data analysis module, the policy matching module and the capital recommendation module are sequentially connected; a data collection module for collecting enterprise data from various channels; the data analysis module is used for calculating an enterprise innovation evaluation value based on the enterprise innovation capability index; the policy matching module is used for matching related policies based on the development condition of enterprises; the social capital recommending module is used for accessing corresponding financial capital based on the innovation evaluation value of the enterprise, and through the cooperative work of the four modules, the internet-based science and technology service data cooperative system can provide one-stop auditing service for the enterprise, so that the enterprise can develop intelligent science and technology projects better.
Description
Technical Field
The invention relates to the technical field of digital data processing, in particular to a science and technology service data collaboration system based on the Internet.
Background
Technical project auditing is an important process that involves a comprehensive assessment of applied technical projects to ensure that they meet regulatory standards and requirements. This process can be divided into multiple stages, including formal and physical reviews.
The method specifically comprises the steps of checking whether targets, tasks, methods, expected achievements and the like of the project accord with the development rules of the science and technology, and whether the project has innovation and practicability and feasibility of project implementation.
The existing scientific and technological service audits that data cannot be shared among departments, so that communication efficiency is reduced, and working efficiency is reduced.
Disclosure of Invention
The invention aims to provide an internet-based science and technology service data collaboration system, which aims to provide one-stop auditing service for enterprises and help the enterprises to develop intelligent science and technology projects better.
In order to achieve the above object, the present invention provides an internet-based science and technology service data collaboration system, which comprises a data collection module, a data analysis module, a policy matching module and a capital recommendation module, wherein the data collection module, the data analysis module, the policy matching module and the capital recommendation module are sequentially connected;
the data collection module is used for collecting enterprise data from various channels;
the data analysis module is used for calculating an enterprise innovation evaluation value based on the enterprise innovation capability index;
the policy matching module is used for matching related policies based on the development condition of enterprises;
the social capital recommending module is used for accessing corresponding financial capital based on the enterprise innovation evaluation value.
The data collection module comprises a data acquisition unit, a data arrangement unit and a data classification unit, wherein the data acquisition unit is used for acquiring data from the data collection module: the information collection module is used for collecting information corresponding to enterprises from the Internet;
the data arrangement unit is used for cleaning the collected enterprise information;
the data classification unit: the processed enterprise data is classified by different categories through the deep learning network.
The enterprise data comprises industry reports, statistical data, social media data and website flow data.
The data classification unit comprises a classification model construction subunit, a data division subunit, a model training subunit and a classification subunit, wherein the classification model construction subunit adopts a cyclic neural network to construct a classification model;
the data dividing subunit is used for dividing the data set into a training set and a testing set;
the model training subunit is used for configuring super parameters of the model, such as learning rate, batch size and iteration times, and training the deep learning model by using a training set;
and the classifying subunit is used for inputting the processed enterprise data into the classifying model for classification.
The data analysis module comprises an index generation unit, a data matching unit, a weight generation unit and a calculation unit, wherein the index generation unit is used for generating innovation ability evaluation indexes;
the data matching unit is used for matching corresponding classified data based on innovation capability indexes;
the weight generation unit is used for generating corresponding innovation evaluation weights based on the classification data;
the computing unit is used for computing innovation evaluation values based on the weights and the corresponding classification data.
The data analysis module further comprises a data visualization unit, wherein the data visualization unit is used for visually displaying analysis results in a chart form.
The policy matching module comprises a policy importing unit, a data processing unit and a policy matching unit, wherein the policy importing unit is used for collecting enterprise policies and generating a policy database;
the data processing unit is used for processing the policy data to obtain tag data;
the matching unit is used for matching the policy based on the tag data and the innovation evaluation value.
The social capital recommendation module comprises a financing demand acquisition unit, a data collection unit and a financing application submitting unit, wherein the financing demand acquisition unit is used for determining the financing demand of an enterprise according to the result of enterprise innovation assessment and development planning; the data collection unit is used for aggregating relevant enterprise data and policies to obtain application data according to the requirements of financial capital; the financing application submitting unit is used for uploading application data to the financing platform.
The internet-based science and technology service data collaboration system of the invention, a data collection module is an entry of the system, which is responsible for collecting data about enterprises from various sources. Such data may come from public databases, partners, investors, social media, and the like. By using advanced data capture and cleansing techniques, the module is able to accurately collect and sort such data. The data analysis module is responsible for in-depth analysis of the collected enterprise data. The module adopts the most advanced machine learning algorithm and can calculate the innovation evaluation value of the enterprise based on the innovation capability index of the enterprise. This evaluation value may reflect the advantages and potential of the enterprise in the field of intelligent technology. The policy matching module is responsible for matching relevant policies according to the development condition of the enterprise. This module quickly matches the business with the policy through intelligent algorithms, enabling the business to better utilize the policy advantages. Finally, the capital recommendation module accesses the corresponding financial capital based on the enterprise innovation valuations. This module can provide a customized financing program for the enterprise, helping it to resolve the funding issue and promoting further development of the project. Through the cooperation of the four modules, the internet-based science and technology service data cooperation system can provide one-stop auditing service for enterprises, and helps the enterprises to develop intelligent science and technology projects better.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a block diagram of an internet-based science and technology service data collaboration system according to a first embodiment of the present invention.
Fig. 2 is a block diagram of a data collection module according to a second embodiment of the present invention.
Fig. 3 is a block diagram of a data classification unit according to a second embodiment of the present invention.
Fig. 4 is a block diagram of a data analysis module according to a second embodiment of the present invention.
Fig. 5 is a block diagram of a data matching unit of a second embodiment of the present invention.
Fig. 6 is a block diagram of a policy matching module according to a second embodiment of the present invention.
Fig. 7 is a block diagram of a policy importing unit according to a second embodiment of the present invention.
Fig. 8 is a block diagram of a social capital recommendation module according to a second embodiment of the invention.
The data collection module 101, the data analysis module 102, the policy matching module 103, the capital recommendation module 104, the data acquisition unit 201, the data sort unit 202, the data sort unit 203, the sort model construction subunit 204, the data division subunit 205, the model training subunit 206, the sort subunit 207, the index generation unit 208, the data matching unit 209, the weight generation unit 210, the calculation unit 211, the keyword generation subunit 212, the association subunit 213, the matching subunit 213, the data visualization unit 215, the policy import unit 216, the data processing unit 217, the policy matching unit 218, the file collection subunit 219, the policy file sort subunit 220, the information extraction subunit 221, the financing demand acquisition unit 222, the data collection unit 223, the financing application submission unit 224.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements with like or similar modules throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Referring to fig. 1, the present invention provides an internet-based science and technology service data collaboration system, which includes a data collection module 101, a data analysis module 102, a policy matching module 103, and a capital recommendation module 104, wherein the data collection module 101, the data analysis module 102, the policy matching module 103, and the capital recommendation module 104 are sequentially connected; the data collection module 101 is configured to collect enterprise data from various channels; the data analysis module 102 is configured to calculate an enterprise innovation evaluation value based on the enterprise innovation capability index; the policy matching module 103 is configured to match related policies based on the enterprise development situation; the social capital recommending module 104 is configured to access corresponding financial capital based on the enterprise innovation evaluation value.
In this embodiment, the data collection module 101 collects data about an enterprise from various sources. Such data may come from public databases, partners, investors, social media, and the like. By using advanced data capture and cleansing techniques, the module is able to accurately collect and sort such data. The data analysis module 102 is responsible for in-depth analysis of the collected enterprise data. The module adopts the most advanced machine learning algorithm and can calculate the innovation evaluation value of the enterprise based on the innovation capability index of the enterprise. This evaluation value may reflect the advantages and potential of the enterprise in the field of intelligent technology. The policy matching module 103 is responsible for matching relevant policies according to the development situation of the enterprise. This module quickly matches the business with the policy through intelligent algorithms, enabling the business to better utilize the policy advantages. Finally, the capital recommendation module 104 accesses the corresponding financial capital based on the enterprise innovation valuations. This module can provide a customized financing program for the enterprise, helping it to resolve the funding issue and promoting further development of the project. Through the cooperation of the four modules, the internet-based science and technology service data cooperation system can provide one-stop auditing service for enterprises, and helps the enterprises to develop intelligent science and technology projects better.
Referring to fig. 2 to 8, on the basis of the first embodiment, the present invention further provides an internet-based science and technology service data collaboration system, where the data collection module 101 includes a data acquisition unit 201, a data sorting unit 202, and a data classification unit 203, and the data acquisition unit 201: the information collection module is used for collecting information corresponding to enterprises from the Internet; the data sorting unit 202 is configured to clean the collected enterprise information; the data classifying unit 203: the processed enterprise data is classified by different categories through the deep learning network.
The data acquisition unit 201 performs extensive information collection via the internet, focusing on acquiring information related to an enterprise. Such information may come from various online sources, such as corporate websites, news stories, social media platforms, and the like. After the enterprise information is collected, the data sort unit 202 performs professional cleaning and sorting. This process may include removing duplicate information, correcting erroneous data, filling in missing information, etc., to ensure accuracy and consistency of the data. Finally, the data classification unit 203 classifies the processed enterprise data through a deep learning network. Such classification is based on different features and attributes of the data, with the aim of classifying similar data into the same class. The classification method not only improves the data processing efficiency, but also provides convenience for subsequent data analysis and decision making. In this way, the enterprise information can be quickly and accurately collected, sorted and classified, thereby providing a clear and organized enterprise information database for users. This clearly provides a powerful support for users in making business decisions, market analysis, competitive intelligence, etc.
The enterprise data includes industry reports, statistics, social media data, website traffic data.
In the foregoing, we can see that enterprise data is described as including industry reports, statistics, social media, and website traffic analysis tools.
Wherein the industry report is generally issued by industry experts or professional institutions, and comprises information of latest dynamics, trends, competition patterns and the like of a specific industry. Statistics are published by government agencies, research organizations or enterprises, including various metrics and data such as sales, market shares, number of users, etc. Social media platforms are an important channel for modern enterprises to obtain user feedback and brand images. The web traffic analysis tool may provide a business with detailed reports on its web site visits, visitor sources, visitor behavior, etc.
The data classification unit 203 comprises a classification model construction subunit 204, a data division subunit 205, a model training subunit 206 and a classification subunit 207, wherein the classification model construction subunit 204 adopts a cyclic neural network to construct a classification model, and the data division subunit 205 is used for dividing a data set into a training set and a testing set; the model training subunit 206 is configured to configure super parameters of the model, such as learning rate, batch size, and iteration number, and train the deep learning model using the training set; the classifying subunit 207 is configured to input the processed enterprise data into a classification model for classification.
The classification model construction subunit 204 employs a Recurrent Neural Network (RNN) to construct a classification model that is capable of understanding and classifying semantic information of natural language. The data dividing subunit 205 is responsible for dividing the data set into a training set and a testing set, so as to ensure reasonable distribution and utilization of the data. Model training subunit 206 is responsible for configuring the super-parameters of the model, such as learning rate, batch size, number of iterations, etc., to train the deep learning model using the training set. Finally, the classification subunit 207 inputs the processed enterprise data into the classification model to classify, so as to obtain a classification result, thereby classifying the enterprise data more conveniently.
The data analysis module 102 includes an index generating unit 208, a data matching unit 209, a weight generating unit 210, and a calculating unit 211, where the index generating unit 208 is configured to generate an innovation ability evaluation index; the data matching unit 209 is configured to match corresponding classification data based on the innovation capability index; the weight generating unit 210 is configured to generate a corresponding innovation evaluation weight based on the classification data; the calculating unit 211 is configured to calculate an innovation evaluation value based on the weight and the corresponding classification data.
The index generation unit 208 is capable of automatically generating innovation capability assessment indices based on specific needs and data sources. The data matching unit 209 is responsible for matching the innovation ability index with the massive classified data, and searching for data conforming to the index. The weight generation unit 210 can generate a corresponding innovation evaluation weight through precise calculation based on the classification data. Finally, the calculation unit 211 accurately calculates the innovation evaluation value according to the weight and the corresponding classification data. The process is automated, and the efficiency and accuracy of data analysis are greatly improved.
The data matching unit 209 includes a keyword generation subunit 212, an association subunit 213, and a matching subunit 213, where the keyword generation subunit 212 is configured to generate a keyword; the association subunit 213 is configured to associate the related information according to the keyword, so as to obtain associated information; the matching subunit 213 matches the classification data based on the associated term.
The keyword generation sub-analyzes the input data, extracts the key information therein, and converts the key information into keywords. The association subunit 213 is responsible for associating the related information according to the keywords to obtain associated information. The matching subunit 213 matches the classification data based on the associated word. This process requires extensive data and algorithms to ensure the accuracy and precision of the matching.
The weight generating unit 210 specifically performs a relative importance judgment using a scale of 1 to 9 based on the classification data for every level of comparison. The result of the comparison constitutes a decision matrix in which each element represents the degree of importance of one criterion or indicator relative to the other. And then, calculating the weight vector of each layer by carrying out eigenvalue decomposition on the judgment matrix. And solving step by step according to the hierarchical relation of the hierarchical structure to obtain the weight of each index.
The weight generating unit 210 specifically generates the weight of the pairwise comparison for each hierarchy through a complex process based on the classification data. In this process, classification data is used for detailed analysis and comparison of each level.
In this process, a scale of 1-9 is used to make a relatively important assessment, which is a very accurate and deep way of assessment. The result of the comparison constitutes a decision matrix, which is a powerful tool that clearly reveals the importance of each criterion or index relative to the other. Each element in the matrix is an important reference point, and can directly reflect the weight relation between different indexes. And calculating the weight vector of each layer by decomposing the characteristic value of the judgment matrix. This is a complex and accurate calculation process, ensuring the accuracy and fairness of the weights. By the method, the problem of each level can be solved one by one, and the accurate weight of each index is finally obtained by solving the problem step by step according to a specific hierarchical structure. The process is comprehensive and careful, and the fairness and accuracy of weight generation are ensured.
The data analysis module 102 further includes a data visualization unit 215, where the data visualization unit 215 is configured to visually display the analysis result in a chart form.
The policy matching module 103 comprises a policy importing unit 216, a data processing unit 217 and a policy matching unit 218, wherein the policy importing unit 216 is used for collecting enterprise policies and generating a policy database; the data processing unit 217 is configured to process policy data to obtain tag data; the matching unit is configured to match the policy based on the tag data and the innovation evaluation value, specifically, match the innovation evaluation value of the policy with the tag data of the policy. A matching model may be built from the relationship between the tag data and the innovative evaluation values using data mining or machine learning techniques. The matching model is trained using the partial policy data as a training set. The performance and accuracy of the model is assessed using other parts of the policy data as a validation set. And carrying out matching prediction on the new policy data by using the trained matching model. And predicting the label category to which the policy belongs according to the innovative evaluation value and label data of the policy. And finally, analyzing the matching result and evaluating the effect and accuracy of the matching model. Further applications such as policy analysis, decision support, etc. may be performed based on the matching results.
The policy importing unit 216 includes a document collecting subunit 219, a policy document classifying subunit 220, and an information extracting subunit 221, where the document collecting subunit 219 is configured to collect the enterprise-related policy documents through various channels; the policy file classification subunit 220 is configured to sort and classify the collected policy files. The information lifting sub-unit is used for extracting key policy key points and key information in the policy file. The document collection subunit 219 is capable of efficiently collecting policy documents related to an enterprise through various channels, such as official websites, news media, industry associations, and the like. The policy file classifying subunit 220 is responsible for sorting and classifying the collected policy files according to different classification modes such as policy field, implementation time, related industry and the like, so as to facilitate subsequent query and use. The information extraction subunit 221 can accurately extract the key policy gist and key information in the policy document.
The social capital recommendation module 104 includes a financing demand acquisition unit 222, a data collection unit 223, and a financing application submitting unit 224, where the financing demand acquisition unit 222 is configured to determine the financing demand of an enterprise according to the result of enterprise innovation assessment and development planning; the data aggregation unit 223 is configured to aggregate relevant enterprise data and policies to obtain application data according to requirements of financial capital; the financing application submitting unit 224 is configured to upload application data to the financing platform.
The financing demand acquisition unit 222 is configured to determine the financing demand of the enterprise according to the result of the enterprise innovation evaluation and the development planning; the data aggregation unit 223 is configured to aggregate relevant enterprise data and policies to obtain application data according to requirements of financial capital; the financing application submitting unit 224 is configured to upload application data to the financing platform. The financing demand acquisition unit 222 may also comprehensively analyze and determine the financing demand of the enterprise according to market dynamics, competitor conditions, and other factors. The profile aggregation unit 223 also needs to filter, sort, analyze enterprise data and policies to ensure the authenticity, accuracy and integrity of the application profiles. Finally, the financing application submitting unit 224 will upload the application data to the financing platform through an automated process, thereby greatly improving the working efficiency and accuracy.
The above disclosure is only a preferred embodiment of the present invention, and it should be understood that the scope of the invention is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present invention.
Claims (8)
1. An internet-based science and technology service data collaboration system, characterized in that,
the system comprises a data collection module, a data analysis module, a policy matching module and a capital recommendation module, wherein the data collection module, the data analysis module, the policy matching module and the capital recommendation module are connected in sequence;
the data collection module is used for collecting enterprise data from various channels;
the data analysis module is used for calculating an enterprise innovation evaluation value based on the enterprise innovation capability index;
the policy matching module is used for matching related policies based on the development condition of enterprises;
the social capital recommending module is used for accessing corresponding financial capital based on the enterprise innovation evaluation value.
2. The internet-based scientific and technological service data coordination system of claim 1,
the data collection module comprises a data acquisition unit, a data arrangement unit and a data classification unit, wherein the data acquisition unit is used for: the information collection module is used for collecting information corresponding to enterprises from the Internet;
the data arrangement unit is used for cleaning the collected enterprise information;
the data classification unit: the processed enterprise data is classified by different categories through the deep learning network.
3. The internet-based scientific and technological service data coordination system of claim 2,
the enterprise data includes industry reports, statistics, social media data, website traffic data.
4. The internet-based scientific and technological service data cooperation system according to claim 3,
the data classification unit comprises a classification model construction subunit, a data division subunit, a model training subunit and a classification subunit, wherein the classification model construction subunit adopts a cyclic neural network to construct a classification model;
the data dividing subunit is used for dividing the data set into a training set and a testing set;
the model training subunit is used for configuring super parameters of the model, such as learning rate, batch size and iteration times, and training the deep learning model by using a training set;
and the classifying subunit is used for inputting the processed enterprise data into the classifying model for classification.
5. The internet-based scientific and technological service data coordination system of claim 4,
the data analysis module comprises an index generation unit, a data matching unit, a weight generation unit and a calculation unit, wherein the index generation unit is used for generating innovation ability evaluation indexes;
the data matching unit is used for matching corresponding classified data based on innovation capability indexes;
the weight generation unit is used for generating corresponding innovation evaluation weights based on the classification data;
the computing unit is used for computing innovation evaluation values based on the weights and the corresponding classification data.
6. The internet-based scientific and technological service data coordination system of claim 5,
the data analysis module further comprises a data visualization unit, wherein the data visualization unit is used for visually displaying analysis results in a chart form.
7. The internet-based scientific and technological service data coordination system of claim 6,
the policy matching module comprises a policy importing unit, a data processing unit and a policy matching unit, wherein the policy importing unit is used for collecting enterprise policies and generating a policy database;
the data processing unit is used for processing the policy data to obtain tag data;
the matching unit is used for matching the policy based on the tag data and the innovation evaluation value.
8. The internet-based scientific and technological service data coordination system of claim 7,
the social capital recommendation module comprises a financing demand acquisition unit, a data collection unit and a financing application submitting unit, wherein the financing demand acquisition unit is used for determining the financing demand of an enterprise according to the result of enterprise innovation evaluation and development planning; the data collection unit is used for aggregating relevant enterprise data and policies to obtain application data according to the requirements of financial capital; the financing application submitting unit is used for uploading application data to the financing platform.
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| CN202311673756.8A CN117592943A (en) | 2023-12-07 | 2023-12-07 | Science and technology service data collaboration system based on Internet |
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2023
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