A technology development consulting system based on correlation analysis

Abstract

This invention relates to the field of technology development consulting, specifically to a technology development consulting system based on correlation analysis. The system includes a data acquisition module, a data preprocessing module, a correlation analysis module, and a consulting module. Users input technical questions and requirements through a user interface module. The data acquisition module collects relevant technical data based on the input questions and requirements. The data preprocessing module cleans, formats, and classifies the collected technical data. The correlation analysis module performs correlation analysis on the preprocessed data to identify relationships and trends between data points. Based on the correlation analysis results, the consulting module provides users with targeted technology development consulting suggestions. By collecting and analyzing a large amount of technical data, comprehensive and objective technology development consulting suggestions can be provided. Utilizing correlation analysis algorithms, relationships and trends between data can be quickly identified, improving consulting efficiency and making the system more convenient to use.

Description

Technical Field

[0001] This invention relates to the field of technology development consulting technology, and in particular to a technology development consulting system based on correlation analysis. Background Technology

[0002] Once a technology or idea is developed, its feasibility and problems need to be assessed and consulted beforehand. Currently, there is a lack of targeted online service platforms that cannot effectively respond to inquiries. Inquiries do not receive reliable and useful information. Furthermore, assessments and consultations with professional organizations are usually costly and not suitable for developers' initial consultations and improvements.

[0003] The existing publication number CN113609371A discloses an online service platform for computer technology development consulting, including a server, a storage module, a user terminal, and an authenticated user terminal. The user terminal can search for questions through a search and communication module. The user terminal and authenticated user terminals can communicate and exchange information for initial consultation. A professional matching module allows users to select authenticated user terminals with the required level and domain, and can quickly match target authenticated user terminals based on payment information, resulting in good matching results. The professional matching module starts timing after the user terminal pays. The authenticated user terminal guides the user terminal in online debugging and problem solving through a sharing module. The user terminal and authenticated user terminal share the consultation process through the sharing module, recording the communication process between the user terminal and authenticated user terminal, forming consultation cases, and storing them in a consultation record database through the storage module. This forms a targeted online service platform that effectively answers the inquirer's information, providing reliable and useful responses. It enables quick and effective consultation and communication with professionals, reducing the initial consultation and improvement costs for computer technology developers.

[0004] However, when using the aforementioned online service platform for computer technology development consulting, users receive a variety of responses, which are often subjective and reflect the respondent's own opinion. Users are required to analyze whether the responses are relevant to their own technology, making the platform inconvenient to use. Summary of the Invention

[0005] The purpose of this invention is to provide a technology development consulting system based on correlation analysis, which solves the problem that consultants still need to analyze whether the obtained answers are related to their own technology, which is inconvenient to use.

[0006] To achieve the above objectives, the present invention provides a technology development consulting system based on correlation analysis, comprising a data acquisition module, a data preprocessing module, a correlation analysis module, and a consulting module. The data preprocessing module is connected to the data acquisition module, the correlation analysis module is connected to the data preprocessing module, and the consulting module is connected to the correlation analysis module.

[0007] The data acquisition module is used to collect technical data, including publicly available technical documents, patents, research reports and industry trend analyses, and upload the collected data to the data preprocessing module.

[0008] The data preprocessing module is used to clean, format, and classify the collected data;

[0009] The correlation analysis module is used to perform correlation analysis on the preprocessed data to find the relationships and trends between the data.

[0010] The consultation module is used to provide users with targeted technical development consultation suggestions based on the results of correlation analysis.

[0011] The technology development consulting system based on correlation analysis also includes a user interface module, which is connected to the data acquisition module.

[0012] The user interface module is used to receive user input, including technical questions and requirements, and upload them to the data acquisition module.

[0013] The technology development consulting system based on correlation analysis also includes a knowledge base module, which is connected to the consulting module.

[0014] The knowledge base module is used to store verified technical knowledge and experience for the consultation module to refer to.

[0015] The consultation module includes an expert unit and a prediction unit. The expert unit is connected to the correlation analysis module, and the prediction unit is connected to the correlation analysis module.

[0016] The expert unit is used to integrate the knowledge and experience of domain experts to provide users with more in-depth technical development consulting advice.

[0017] The prediction unit uses machine learning algorithms to predict future technology trends and provides users with forward-looking technology development consulting advice.

[0018] The data preprocessing module includes a data cleaning unit and a data formatting unit. The data cleaning unit is connected to the data acquisition module, and the data formatting unit is connected to the data cleaning unit.

[0019] The data cleaning unit is used to check for missing values ​​in the collected data, fill in the missing values, identify and process outliers in the data, and standardize the data.

[0020] The data formatting unit is used to convert the cleaned data into an appropriate type, integrate data from different sources, and ensure data consistency and integrity.

[0021] The data preprocessing module further includes a data classification unit, which is connected to the data formatting unit and the correlation analysis module.

[0022] The data classification unit is used to select the features most relevant to the target variable, remove irrelevant features, select a suitable classification algorithm based on the nature of the data and the type of the target variable, train the integrated data using the selected algorithm, evaluate the performance of the model, and adjust the model parameters or select other algorithms as needed.

[0023] The correlation analysis module includes a feature selection unit and a model training unit. The feature selection unit is connected to the data classification unit, and the model training unit is connected to the feature selection unit.

[0024] The feature selection unit is used to select the feature most relevant to the target variable from the classification features using a feature selection algorithm.

[0025] The model training unit is used to train selected features using machine learning algorithms, build a model, and predict the value of the target variable.

[0026] This invention discloses a technology development consulting system based on correlation analysis. Users input technical problems and needs through a user interface module. A data acquisition module collects relevant technical data based on the input problems and needs, including publicly available technical literature, patents, research reports, and industry trend analyses. A data preprocessing module cleans, formats, and classifies the collected technical data. A correlation analysis module performs correlation analysis on the preprocessed data to identify relationships and trends between data points. Based on the correlation analysis results, a consulting module provides users with targeted technology development consulting suggestions. By collecting and analyzing a large amount of technical data, comprehensive and objective technology development consulting suggestions can be provided. The correlation analysis algorithm can quickly identify relationships and trends between data points, improving consulting efficiency and making the system more convenient to use. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0028] Figure 1 This is a structural block diagram of the technology development consulting system based on correlation analysis according to the first embodiment of the present invention.

[0029] Figure 2 This is a structural block diagram of the technology development consulting system based on correlation analysis according to the second embodiment of the present invention.

[0030] In the diagram: 101-Data Acquisition Module, 102-Data Preprocessing Module, 103-Correlation Analysis Module, 104-Consultation Module, 105-User Interface Module, 106-Knowledge Base Module, 201-Expert Unit, 202-Prediction Unit, 203-Data Cleaning Unit, 204-Data Formatting Unit, 205-Data Classification Unit, 206-Feature Selection Unit, 207-Model Training Unit, 208-Model Evaluation Unit. Detailed Implementation

[0031] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0032] The first embodiment of this application is as follows:

[0033] Please see Figure 1 ,in, Figure 1 This is a structural block diagram of a technology development consulting system based on correlation analysis according to the first embodiment of the present invention. The present invention provides a technology development consulting system based on correlation analysis, comprising a data acquisition module 101, a data preprocessing module 102, a correlation analysis module 103, a consulting module 104, a user interface module 105, and a knowledge base module 106.

[0034] In this specific embodiment, the data preprocessing module 102 is connected to the data acquisition module 101, the correlation analysis module 103 is connected to the data preprocessing module 102, and the consultation module 104 is connected to the correlation analysis module 103.

[0035] The data acquisition module 101 is used to collect technical data, including publicly available technical documents, patents, research reports and industry trend analyses, and upload the collected data to the data preprocessing module 102.

[0036] The data preprocessing module 102 is used to clean, format, and classify the collected data.

[0037] The correlation analysis module 103 is used to perform correlation analysis on the preprocessed data to find the relationships and trends between the data.

[0038] The consultation module 104 is used to provide users with targeted technical development consultation suggestions based on the correlation analysis results.

[0039] The data collection module is used to collect technical data from various sources, including publicly available technical documents, patents, research reports, industry trend analyses, etc.; the data preprocessing module 102 performs preprocessing operations such as cleaning, formatting, and classifying the collected data for subsequent analysis; the correlation analysis module 103 uses statistical and machine learning algorithms to perform correlation analysis on the preprocessed data to find the relationships and trends between the data; the consulting module 104 provides users with targeted technical development consulting suggestions based on the correlation analysis results.

[0040] The user interface module 105 is connected to the data acquisition module 101.

[0041] The user interface module 105 is used to receive user input, including technical questions and requirements, and upload them to the data acquisition module 101.

[0042] The user interface module 105 is used to receive user input, including technical questions and requirements, and upload the user input to the data acquisition module 101.

[0043] Secondly, the knowledge base module 106 is connected to the consultation module 104;

[0044] The knowledge base module 106 is used to store verified technical knowledge and experience for reference by the consultation module 104.

[0045] The knowledge base module 106 stores verified technical knowledge and experience for the system to refer to during the analysis process.

[0046] Using the technology development consulting system based on correlation analysis in this embodiment, the user inputs technical questions and needs through the user interface module 105. The data acquisition module 101 collects technical data based on the input technical questions and needs, including publicly available technical documents, patents, research reports, industry trend analyses, etc. The data preprocessing module 102 performs preprocessing operations such as cleaning, formatting, and classification on the collected data for subsequent analysis. The correlation analysis module 103 uses statistical and machine learning algorithms to perform correlation analysis on the preprocessed data to identify the relationships and trends between data. The consulting module 104 provides targeted technology development consulting suggestions to the user based on the correlation analysis results, referring to the verified technical knowledge and experience stored in the knowledge base module 106 during the suggestion process. By collecting and analyzing a large amount of technical data, comprehensive and objective technology development consulting suggestions can be provided. The correlation analysis algorithm can quickly identify the relationships and trends between data, improving consulting efficiency and making the system more convenient to use.

[0047] The second embodiment of this application is as follows:

[0048] Based on the first embodiment, please refer to Figure 2 ,in, Figure 2 This is a structural block diagram of a technology development consulting system based on correlation analysis according to a second embodiment of the present invention. The consulting module 104 in this embodiment includes an expert unit 201 and a prediction unit 202. The data preprocessing module 102 includes a data cleaning unit 203, a data formatting unit 204, and a data classification unit 205. The correlation analysis module 103 includes a feature selection unit 206, a model training unit 207, and a model evaluation unit 208.

[0049] In this specific embodiment, the data cleaning unit 203 is connected to the data acquisition module 101, and the data formatting unit 204 is connected to the data cleaning unit 203;

[0050] The data cleaning unit 203 is used to check for missing values ​​in the collected data, fill in the missing values, identify and process outliers in the data, and standardize the data.

[0051] The data formatting unit 204 is used to convert the cleaned data into an appropriate type, integrate data from different sources, and ensure data consistency and integrity.

[0052] The data classification unit 205 is connected to the data formatting unit 204 and the correlation analysis module 103, respectively.

[0053] The data classification unit 205 is used to select the features most relevant to the target variable, remove irrelevant features, select a suitable classification algorithm according to the nature of the data and the type of the target variable, train the integrated data using the selected algorithm, evaluate the performance of the model, and adjust the model parameters or select other algorithms as needed.

[0054] The data cleaning unit 203 checks for missing values ​​in the collected data and selects a method to fill in the missing values ​​based on the actual situation, such as using the mean, median, or mode. It also identifies and processes outliers in the data, using statistical methods (such as IQR, Z-score, etc.) or visualization methods (such as box plots). For continuous data, it can map the data to the range [0, 1] or [-1, 1] through min-max normalization or z-score normalization. The data formatting unit 204 converts the data into an appropriate type, such as converting categorical variables into dummy variables or one-hot encoding, and integrates data from different sources to ensure data consistency and integrity. The data classification unit 205 selects the features most relevant to the target variable, removes irrelevant features, selects a suitable classification algorithm based on the nature of the data and the type of the target variable, such as decision trees, random forests, or support vector machines, trains the data using the selected algorithm, evaluates the model's performance, and adjusts the model parameters or selects other algorithms as needed. This ensures that the data input into the correlation analysis module 103 is accurate, consistent, and effective, thereby improving the accuracy and efficiency of data analysis.

[0055] The feature selection unit 206 is connected to the data classification unit 205, and the model training unit 207 is connected to the feature selection unit 206.

[0056] The feature selection unit 206 is used to select the feature most relevant to the target variable from the classification features using a feature selection algorithm.

[0057] The model training unit 207 is used to train selected features using machine learning algorithms, build a model, and predict the value of the target variable.

[0058] The model evaluation unit 208 is connected to the model training unit 207;

[0059] The model evaluation unit 208 evaluates the performance of the model and adjusts the model parameters or selects other algorithms based on the evaluation results.

[0060] The feature selection unit 206 uses feature selection algorithms, such as model-based feature selection and filtered feature selection, to select the features most relevant to the target variable from the original features, thereby reducing feature dimensionality and improving analysis efficiency. The model training unit 207 uses machine learning algorithms such as support vector machines and neural networks to train the selected features and build models. These models can learn complex relationships between variables and predict the value of the target variable. The model evaluation unit 208 evaluates the performance of the model through methods such as cross-validation and hold-out validation. Based on the evaluation results, the model parameters can be adjusted or other algorithms can be selected.

[0061] In addition, the expert unit 201 is connected to the correlation analysis module 103, and the prediction unit 202 is connected to the correlation analysis module 103;

[0062] The expert unit 201 is used to integrate the knowledge and experience of domain experts to provide users with more in-depth technical development consulting advice.

[0063] The prediction unit 202 uses machine learning algorithms to predict future technology trends and provide users with forward-looking technology development consulting advice.

[0064] The expert unit 201 integrates the knowledge and experience of domain experts based on the correlation analysis results, and can provide users with more in-depth and professional technical development consulting advice. The prediction unit 202 uses a trained model to predict future technology trends, which may include predicting technology development trends and market size changes in the future. Based on the prediction results, it can provide targeted suggestions and guidance for technology development, and can provide users with forward-looking technical development consulting advice to help users seize market opportunities.

[0065] Using the correlation analysis-based technology development consulting system of this embodiment, the data cleaning unit 203 handles missing values, outliers, and standardizes data; the data formatting unit 204 performs data type conversion and data integration; and the data classification unit 205 performs feature selection, classification algorithm selection, and model training and evaluation to ensure that the data input to the correlation analysis module 103 is accurate, consistent, and effective, thereby improving the accuracy and efficiency of data analysis. The feature selection unit 206 selects the most relevant features to the target variable from the original features, reducing feature dimensions and improving analysis efficiency. The model training unit 207 builds models that can learn complex relationships between variables and predict the value of the target variable. The model evaluation unit 208 can adjust the model parameters or select other algorithms to help the system better understand and interpret data, improving the reliability and credibility of consulting advice. The expert unit 201 provides users with more in-depth technology development consulting advice, and the prediction unit 202 provides users with forward-looking technology development consulting advice. By collecting and analyzing a large amount of technical data, we can provide comprehensive and objective technical development consulting advice. By using correlation analysis algorithms, we can quickly identify the relationships and trends between data, improving consulting efficiency. Combined with the knowledge and experience of domain experts, we can provide users with more in-depth and professional technical development consulting advice. By predicting future technology trends, we can provide users with forward-looking technical development consulting advice, helping them seize market opportunities.

[0066] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims

1. A technology development consulting system based on correlation analysis, characterized in that, It includes a data acquisition module, a data preprocessing module, a correlation analysis module, and a consultation module. The data preprocessing module is connected to the data acquisition module, the correlation analysis module is connected to the data preprocessing module, and the consultation module is connected to the correlation analysis module. The data acquisition module is used to collect technical data, including publicly available technical documents, patents, research reports and industry trend analyses, and upload the collected data to the data preprocessing module. The data preprocessing module is used to clean, format, and classify the collected data; The correlation analysis module is used to perform correlation analysis on the preprocessed data to find the relationships and trends between the data. The consultation module is used to provide users with targeted technical development consultation suggestions based on the results of correlation analysis.

2. The technology development consulting system based on correlation analysis as described in claim 1, characterized in that, The technology development consulting system based on correlation analysis also includes a user interface module, which is connected to the data acquisition module. The user interface module is used to receive user input, including technical questions and requirements, and upload them to the data acquisition module.

3. The technology development consulting system based on correlation analysis as described in claim 1, characterized in that, The technology development consulting system based on correlation analysis also includes a knowledge base module, which is connected to the consulting module. The knowledge base module is used to store verified technical knowledge and experience for the consultation module to refer to.

4. The technology development consulting system based on correlation analysis as described in claim 1, characterized in that, The consultation module includes an expert unit and a prediction unit. The expert unit is connected to the correlation analysis module, and the prediction unit is connected to the correlation analysis module. The expert unit is used to integrate the knowledge and experience of domain experts to provide users with more in-depth technical development consulting advice. The prediction unit uses machine learning algorithms to predict future technology trends and provides users with forward-looking technology development consulting advice.

5. The technology development consulting system based on correlation analysis as described in claim 1, characterized in that, The data preprocessing module includes a data cleaning unit and a data formatting unit. The data cleaning unit is connected to the data acquisition module, and the data formatting unit is connected to the data cleaning unit. The data cleaning unit is used to check for missing values ​​in the collected data, fill in the missing values, identify and process outliers in the data, and standardize the data. The data formatting unit is used to convert the cleaned data into an appropriate type, integrate data from different sources, and ensure data consistency and integrity.

6. The technology development consulting system based on correlation analysis as described in claim 5, characterized in that, The data preprocessing module further includes a data classification unit, which is connected to the data formatting unit and the correlation analysis module respectively. The data classification unit is used to select the features most relevant to the target variable, remove irrelevant features, select a suitable classification algorithm based on the nature of the data and the type of the target variable, train the integrated data using the selected algorithm, evaluate the performance of the model, and adjust the model parameters or select other algorithms as needed.

7. The technology development consulting system based on correlation analysis as described in claim 6, characterized in that, The correlation analysis module includes a feature selection unit and a model training unit. The feature selection unit is connected to the data classification unit, and the model training unit is connected to the feature selection unit. The feature selection unit is used to select the feature most relevant to the target variable from the classification features using a feature selection algorithm. The model training unit is used to train selected features using machine learning algorithms, build a model, and predict the value of the target variable.

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