Semantic similarity analysis method for science and technology project management

Abstract

The invention provides a semantic similarity analysis method for science and technology project management, and belongs to the technical field of information, the semantic similarity analysis method for science and technology project management comprises the following steps: preprocessing collected project documents, including text cleaning, word segmentation and stop word removal; extracting vocabulary features in the document, and converting the vocabulary features into numerical vector representation; calculating the semantic similarity between the documents or terms; and applying a similarity analysis result to document management and information retrieval: performing automatic preprocessing on the document, including text cleaning, word segmentation, stop word removal and standardization processing, so that a system can quickly prepare document data, prepare for subsequent analysis, automatically extract vocabulary features in the document by using an advanced word embedding model (such as Word2Vec, GloVe or BERT), and perform data processing on the document. And the information is converted into a numerical vector for representation, so that the requirement of manual intervention is greatly reduced.

Description

Technical Field

[0001] The invention belongs to the field of information technology, and in particular relates to a semantic similarity analysis method for scientific and technological project management. Background Art

[0002] With the increasing number and complexity of scientific and technological projects, a large number of documents generated during project management need to be effectively managed and analyzed. Traditional document processing methods often rely on manual reading and classification, which is not only time-consuming and labor-intensive, but also prone to omissions or misjudgments. Especially when faced with a large number of technical documents, research reports, meeting minutes, etc., traditional manual management methods can no longer meet the needs of efficient management and fast retrieval.

[0003] Although the existing technical solutions have solved these problems to a certain extent, they still have the following shortcomings:

[0004] Low degree of automation: Many document processing tasks still require manual intervention and cannot be fully automated, which affects processing speed and efficiency.

[0005] Inaccurate information extraction: Existing document processing technologies may make misjudgments or omissions when extracting key information from documents, resulting in low accuracy of information extraction.

[0006] Information display is not intuitive: Even if some systems provide information retrieval functions, they often lack intuitive visualization methods when displaying document content and analysis results, making it difficult for users to quickly understand the core content of the document. Summary of the invention

[0007] The purpose of the present invention is to provide a semantic similarity analysis method for scientific and technological project management, aiming to solve the problems of low automation, inaccurate information extraction and unintuitive information display in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A semantic similarity analysis method for scientific and technological project management, comprising:

[0010] Preprocess the collected project documents, including text cleaning, word segmentation, and stop word removal;

[0011] Extract vocabulary features from documents and convert them into numerical vector representations;

[0012] Calculate semantic similarity between documents or terms;

[0013] Apply similarity analysis results to document management and information retrieval

[0014] As a preferred solution of the present invention, the preprocessing step further includes standardizing the document to eliminate the difference in uppercase and lowercase letters and the influence of punctuation marks in the text.

[0015] As a preferred solution of the present invention, the extraction of vocabulary features adopts a word embedding model, and the model is selected from one or more of Word2Vec, GloVe or BERT.

[0016] As a preferred solution of the present invention, the step of calculating semantic similarity includes using a cosine similarity or a Jaccard similarity algorithm.

[0017] As a preferred solution of the present invention, the method further includes classifying and marking the documents according to the similarity analysis results.

[0018] As a preferred solution of the present invention, the step of applying the similarity analysis result also includes generating a document summary and a keyword list.

[0019] As a preferred solution of the present invention, the method also includes using a machine learning algorithm to train a model to automatically identify key information points in a document.

[0020] As a preferred solution of the present invention, the method can also update the model through a continuous learning mechanism to adapt to changes in project documents.

[0021] As a preferred solution of the present invention, the method further includes visually displaying the analysis results so that project managers can intuitively understand the document content.

[0022] As a preferred solution of the present invention, the method is integrated into an existing project management system as a plug-in to enhance the system functionality.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. In this solution: By automatically preprocessing documents, including text cleaning, word segmentation, stop word removal, and standardization, the system can quickly prepare document data for subsequent analysis. Advanced word embedding models (such as Word2Vec, GloVe, or BERT) are used to automatically extract lexical features in documents and convert them into numerical vector representations, greatly reducing the need for manual intervention. The system can automatically calculate the semantic similarity between documents, and quickly identify documents with similar content through cosine similarity or Jaccard similarity algorithms, improving the efficiency of document classification and retrieval.

[0025] 2. In this solution: The machine learning model automatically identifies key information points in the document and generates a keyword list, allowing users to quickly grasp the core content of the document. The system automatically generates a document summary to help users quickly understand the main information of the document, saving time reading the full text. Documents are intelligently classified and marked based on the similarity analysis results, making document management more systematic and organized, and easier for users to find and manage.

[0026] 3. In this solution: The system provides a comprehensive dashboard page to display the main statistical data of the document, such as the total number of documents, keyword frequency distribution, etc., so that the information is clear at a glance. Document summaries, keywords, classification results and similarity analysis results are displayed in various charts such as bar charts, pie charts, heat maps, word clouds, etc., to help users intuitively understand the content of the document. Through the continuous learning mechanism, the system can dynamically adapt to changes in project documents, continuously optimize model performance, and ensure the accuracy and timeliness of information processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0028] Figure 1 is a flow chart of the method of the present invention;

[0029] Figure 2 For the present invention Figure 1 Flowchart of data collection in the middle;

[0030] Figure 3 For the present invention Figure 1 Flowchart of data preprocessing;

[0031] Figure 4 For the present invention Figure 1 Flowchart of feature extraction in ;

[0032] Figure 5 For the present invention Figure 1 Flowchart of similarity calculation in ;

[0033] Figure 6 For the present invention Figure 1 Flow chart of the application of the results;

[0034] Figure 7 For the present invention Figure 1 Flowchart of continuous learning in

[0035] Figure 8 For the present invention Figure 1 Flowchart for visual display in ;

[0036] Fig. 9 For the present invention Figure 1 Flowchart for integration into existing systems. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0039] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "mounted / connected", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] Example 1

[0041] See also Figure 1-9 , the present invention provides the following technical solutions:

[0042] A semantic similarity analysis method for scientific and technological project management, comprising:

[0043] Preprocess the collected project documents, including text cleaning, word segmentation, and stop word removal;

[0044] Extract vocabulary features from documents and convert them into numerical vector representations;

[0045] Calculate semantic similarity between documents or terms;

[0046] Apply similarity analysis results to document management and information retrieval.

[0047] In a specific embodiment of the present invention, in a specific embodiment of the present invention, document preprocessing:

[0048] Text cleaning: First, perform text cleaning on the collected project documents, removing non-text information such as irrelevant characters, HTML tags, and special symbols, and retaining only the pure text content.

[0049] Word segmentation: Segment the cleaned text, splitting long sentences into words or phrases for subsequent processing.

[0050] Stop word removal: Remove common stop words such as "de" (of), "shi" (is), "zai" (in) from the segmented results. These words contribute little to semantic understanding but increase data noise.

[0051] Feature extraction and vectorization:

[0052] Lexical feature extraction: Extract meaningful lexical features from the preprocessed documents. These features may be keywords, phrases, or topics.

[0053] Vector representation:

[0054] Convert the extracted lexical features into numerical vector representations. Common methods include deep learning models such as TF-IDF (Term Frequency-Inverse Document Frequency), Word2Vec, and BERT. These methods can capture the importance and semantic information of words in the document.

[0055] Calculate semantic similarity:

[0056] Similarity calculation:

[0057] Based on the obtained document vectors, use algorithms such as cosine similarity, Jaccard similarity, and edit distance to calculate the similarity between documents or terms.

[0058] Threshold setting:

[0059] Set the similarity threshold according to the application scenario. Only when the similarity between two documents exceeds this threshold is it considered that they have a high semantic similarity.

[0060] Apply the similarity analysis results:

[0061] Document management:

[0062] Use the similarity analysis results to classify and cluster documents, etc., to help managers better understand and organize a large number of scientific and technological project documents.

[0063] Information retrieval:

[0064] Apply the similarity analysis results in an information retrieval system to improve the retrieval precision and recall rate, enabling users to find relevant information resources more quickly.

[0065] To illustrate with a specific example, for example, in a specific science and technology project management scenario, we need to find literature related to a specific topic from a large number of documents.

[0066] First, we preprocess the documents, extract features and convert them into vectors. Next, we calculate the similarity between each document and the target topic and set a reasonable threshold. Finally, we group documents with high similarity together and apply them to document management and information retrieval systems, so that users can obtain relevant information more efficiently. This method can not only improve the efficiency of scientific and technological project management, but also help researchers and managers better understand and integrate knowledge in the project and promote the smooth progress of the project.

[0067] For details, please refer to 1-9. The preprocessing step further includes standardizing the document to eliminate the impact of capitalization differences and punctuation marks in the text.

[0068] In this embodiment: Standardization process in the pre-processing step:

[0069] Normalize upper and lower case letters and convert to lower case:

[0070] Converts all text in a document to lowercase to eliminate differences caused by uppercase and lowercase letters. This ensures that the same word is treated as the same word regardless of its capitalization, which improves consistency in text processing.

[0071] Punctuation processing:

[0072] Remove or replace punctuation:

[0073] Remove punctuation marks from documents, such as periods, commas, question marks, etc., or replace them with specific markers. Punctuation marks usually do not contain important semantic information, but in some cases, such as when expressing questions or exclamations, you may need to retain specific markers to distinguish semantics.

[0074] Number and special character handling:

[0075] Number standardization: For numbers in documents, they can be converted to a unified form, such as "2024" is converted to "2024" to reduce variations.

[0076] Special character processing: Some special characters or symbols, such as copyright symbols, registered trademark symbols, etc., can be removed or replaced to reduce interference.

[0077] Embodiment description:

[0078] Suppose we have a text: "Science and technology project management in 2024 is very important!" After the above standardization process, the text may become: "Science and technology project management in 2024 is very important" In this example, "" is removed, all letters are converted to lowercase, spaces are used to separate words, and the number "2024" remains unchanged.

[0079] This standardization process ensures that subsequent feature extraction and vectorization steps are more accurate and avoids mismatches due to differences in capitalization or punctuation. The ultimate goal of this is to improve the quality of semantic similarity analysis, so that relevant documents can be more accurately identified and matched during document management and information retrieval.

[0080] For details, please refer to 1-9. The word embedding model is used to extract vocabulary features. The model is selected from one or more of Word2Vec, GloVe or BERT.

[0081] In this embodiment: vocabulary feature extraction adopts word embedding model:

[0082] Word2Vec:

[0083] Introduction: Word2Vec is a technology that maps words to vector space and captures the semantic relationship between words by training the model.

[0084] Application scenarios: Suitable for processing large corpora and can capture the contextual relationship between words. Advantages: It can capture the semantic information of words well and has fast training speed.

[0085] Disadvantages: It is not ideal for dealing with polysemy.

[0086] Introduction: GloVe constructs word vectors through global matrix factorization, emphasizing the importance of statistical co-occurrence frequency. Application scenarios: Suitable for processing data sets containing a large amount of contextual information. Advantages: Can handle polysemy well.

[0087] Disadvantages: Compared with Word2Vec, the training time is longer. BERTBidirectional EncoderRepresentations from Transformers

[0088] Introduction: BERT is a deep learning model based on the Transformer architecture that can process bidirectional contextual information and provide more accurate word vector representation.

[0089] Application scenarios: Suitable for tasks that require highly accurate semantic understanding, such as question-answering systems, text summarization, etc.

[0090] Advantages: Ability to handle complex language structures and contextual dependencies. Disadvantages: High consumption of computing resources, relatively slow training and reasoning speeds.

[0091] Specific application in this embodiment: In this embodiment, one of the following methods or a combination thereof may be selected to extract vocabulary features:

[0092] Single model application: If the language expression of the project document is relatively simple and the corpus size is moderate, you can choose Word2Vec or GloVe to extract vocabulary features. If the document content is complex and requires in-depth understanding of the context, you can choose the BERT model.

[0093] Combining multiple models: For scenarios that require both speed and accuracy, you can first use Word2Vec or GloVe for preliminary word vectorization, and then use BERT for further semantic refinement. For example, use Word2Vec to quickly generate basic word vectors in the initial processing stage, and then use BERT to perform in-depth semantic analysis of key terms or sentences to improve the performance of the overall system.

[0094] Example description: Suppose we want to perform semantic similarity analysis on a batch of scientific and technological project management documents, and the document content involves complex terminology and technical details. In this case, you can first use Word2Vec or GloVe to perform preliminary vocabulary feature extraction on all documents to generate basic word vectors. Then, for those key parts that require a deep understanding of the context, you can use the BERT model for further processing to ensure semantic accuracy and consistency. In this way, you can ensure both processing speed and the accuracy of semantic analysis, thereby improving the application effect of the entire system in scientific and technological project management.

[0095] For details, please refer to 1-9. The step of calculating semantic similarity includes using cosine similarity or Jaccard similarity algorithm.

[0096] In this embodiment: Calculate semantic similarity:

[0097] Cosine Similarity.

[0098] Definition: Cosine similarity is a measure of the cosine of the angle between two non-zero vectors. It measures the direction of two vectors rather than their magnitude, ranging from =1 to 1, where 1 means they are exactly the same, 0 means they have no relationship, and =1 means they are completely opposite.

[0099] Application scenarios: Suitable for similarity calculation of word vectors, especially when the focus is on the direction of the vector rather than the length.

[0100] formula:

[0101]

[0102] The meanings of the symbols here are as follows:

[0103] · and are two vectors.

[0104] · Representation vector and vector The dot product (inner product) of .

[0105] · and Represents vectors and vector The modulus (length or norm) of the vector, that is, the magnitude of the vector.

[0106] Specifically:

[0107]

[0108] The dot product of vectors is calculated as follows:

[0109]

[0110] For example, if and Then their cosine similarity is calculated as follows:

[0111] 1. Calculate the dot product

[0112]

[0113] 2. Calculation model and

[0114]

[0115] 3. Finally, calculate the cosine similarity:

[0116]

[0117] Please note that the actual calculation results need to retain enough decimal places to ensure accuracy. The range of cosine similarity is from =1 to 1, where 1 represents exactly the same direction, 0 represents orthogonal (independent), and =1 represents the opposite direction.

[0118] Jaccard similarity definition: Jaccard similarity refers to the ratio of the number of elements in the intersection of two sets to the number of elements in the union. It is used to measure the similarity between two sets and is suitable for calculating the similarity of discrete features.

[0119] Application scenarios: Applicable to the calculation of word overlap in text, especially when it is necessary to measure the overlapping part of words in a document.

[0120] formula:

[0121] here:

[0122] A and B are two sets.

[0123] A∩B represents the intersection of sets A and B, that is, the set consisting of elements that belong to both sets A and B.

[0124] A∪B represents the union of sets A and B, that is, the set consisting of all elements that belong to at least one of sets A or B.

[0125] |A∩B| and |A∪B| represent the number of elements in the intersection and union, respectively.

[0126] The Jaccard similarity value ranges from 0 to 1, where 0 means that the two sets have no common elements and 1 means that the two sets are exactly the same.

[0127] For example, if there are two sets A = {1, 2, 3} and B = {2, 3, 4}, then:

[0128] Intersection A∩B = {2, 3}

[0129] Union A∪B = {1, 2, 3, 4}

[0130] So, the Jaccard similarity is:

[0131]

[0132] Specific application in this embodiment:

[0133] In this embodiment, according to different requirements and application scenarios, the cosine similarity algorithm or the Jaccard similarity algorithm may be selected to calculate the similarity between documents or terms.

[0134] 1. Use cosine similarity;

[0135] Applicable scenarios: When you need to evaluate the document similarity based on word vectors, you can use cosine similarity. For example, word vectors generated by Word2Vec, GloVe, or BERT can be used to calculate the content similarity between documents.

[0136] Advantages: It can better reflect the semantic similarity of words and is suitable for similarity calculation of long documents.

[0137] Use Jaccard similarity for the following scenarios:

[0138] Jaccard similarity can be used when you need to evaluate the degree of overlap of terms in documents.

[0139] For example, Jaccard similarity is very effective in calculating the similarity of short texts or keyword lists. Advantages: Simple calculation, suitable for similarity calculation of short texts or feature sets.

[0140] Practical application example: Suppose we need to evaluate the similarity between two scientific project management documents: Use cosine similarity:

[0141] If the document is long and the distribution of words in the document needs to be considered, cosine similarity can be used. For example, the document vector generated by the BERT model can be used to calculate the cosine similarity between two documents.

[0142] Using Jaccard similarity:

[0143] If the documents are short, or you only need to focus on whether the keywords appearing in the documents overlap, you can choose Jaccard similarity. For example, extract the keyword sets in two documents and then calculate their Jaccard similarity. By choosing an appropriate similarity calculation method, you can more accurately evaluate the semantic similarity between documents, thereby improving the efficiency of information retrieval and document management in scientific and technological project management.

[0144] For details, please refer to 1-9. The method also includes classifying and marking the documents according to the similarity analysis results.

[0145] In this embodiment: the documents are classified and marked according to the similarity analysis results:

[0146] Classification:

[0147] Automatic classification: Based on the calculated semantic similarity between documents, documents with high similarity can be automatically classified into the same category.

[0148] For example, if multiple documents discuss key technical issues in the same scientific and technological project, these documents can be classified into one category.

[0149] Hierarchical classification: For large-scale document collections, hierarchical classification can be used to first divide the documents into large categories, and then further divide each large category into subcategories. This can effectively manage a large number of documents and make it easier to find specific information.

[0150] mark:

[0151] Add tags: After categorizing documents, you can add tags based on the document's subject, keywords, project name, etc. Tags can help users quickly identify the content of the document and also facilitate subsequent information retrieval.

[0152] Smart tags: Using machine learning algorithms, you can automatically tag documents based on their content. For example, natural language processing technology can be used to identify key concepts in a document and generate tags accordingly.

[0153] Specific implementation steps:

[0154] Example Process

[0155] Document preprocessing: text cleaning, word segmentation, stop word removal, standardization, etc.

[0156] Feature extraction and vectorization: Use word embedding models such as Word2Vec, GloVe, or BERT to convert documents into numerical vector representations.

[0157] Calculate similarity: Use cosine similarity or Jaccard similarity algorithm to calculate the similarity between documents.

[0158] Classification: Automatically classify documents based on similarity analysis results. For example, you can set a threshold, and when the similarity between documents is higher than this threshold, they are classified into the same category.

[0159] mark:

[0160] Add tags to the classified documents. Tags can be entered manually or generated by automated tools. For example, you can add the tag "AI" to all documents about "artificial intelligence" technology.

[0161] Application results:

[0162] The results of classification and tagging are applied to document management and information retrieval systems, allowing users to find and manage relevant documents more conveniently.

[0163] Practical application cases

[0164] Suppose that in a technology project management platform, we need to process a large number of research reports and technical documents. Through the above method, we can achieve the following functions:

[0165] Automatic document classification: Research reports discussing the same technical issues are grouped together to make it easier for project team members to find relevant information.

[0166] Intelligent tag generation: Automatically generate tags for each document, such as "blockchain technology" and "big data analysis", to help users quickly identify document content.

[0167] Information retrieval optimization: Through classification and tagging, the information retrieval system can be optimized to make search results more accurate and improve work efficiency.

[0168] Through this method, not only the efficiency of document management can be improved, but also the accuracy and speed of information retrieval can be enhanced, providing strong support for the management and execution of scientific and technological projects.

[0169] For details, please refer to 1-9. The step of applying the similarity analysis result also includes generating a document summary and a keyword list.

[0170] In this embodiment: Generate document summary and keyword list:

[0171] 1. Document summary generation

[0172] Automatic summary: Based on the content of the document, the document summary is automatically generated using natural language processing technology. The summary should contain the most critical information in the document so that readers can quickly understand the topic and main points of the document.

[0173] Summary length control: The length of the summary can be adjusted according to actual needs. Generally, the summary should be brief and concise, but detailed enough to cover the main content of the document.

[0174] Summary quality: The summary should reflect the core idea of ​​the document as accurately as possible, avoiding missing important information or including irrelevant content.

[0175] 2. Keyword list generation

[0176] Keyword extraction: Extract keywords from documents through text analysis. These keywords are usually words that appear frequently in documents and are crucial to the topic of the document.

[0177] Keyword weight: You can use methods such as TF-IDFTerm Frequency-Inverse Document Frequency to determine the importance of keywords. Words with higher weights are usually more important.

[0178] Number of keywords: The length of the keyword list can be adjusted according to actual needs. Usually a certain number of important words are selected, which is neither too many to make it difficult to read nor too few to summarize the content of the document.

[0179] Specific implementation steps:

[0180] Automatically generate document summaries:

[0181] Extract main content: Use natural language processing techniques such as NLP to extract main content from documents.

[0182] Generate summary: Generate a concise and clear summary based on the extracted content, highlighting the key information of the document.

[0183] Optimize abstracts: Optimize the quality of abstracts through algorithms to ensure the accuracy and readability of abstracts.

[0184] Automatically generate keyword list:

[0185] Extract high-frequency words: Extract words that appear frequently from documents.

[0186] Calculate keyword weights: Use methods such as TF-IDF to calculate keyword weights and determine which words are most important.

[0187] Filter keywords: Filter out the most important keywords based on weight and generate a keyword list.

[0188] Deduplication and sorting: Make sure there are no duplicates in the keyword list and sort it according to importance.

[0189] Example Description

[0190] Suppose we need to process a research report on the application of new technology:

[0191] Generate a document summary:

[0192] The automatic summarization system extracts the most core part of the document and generates a brief text summary, such as: "This report explores the application prospects of X technology in Y field and proposes several key challenges..."

[0193] Generate a keyword list:

[0194] The keyword extraction system will generate a list of keywords, such as: "X technology, Y field, application prospects, key challenges, experimental results, data analysis".

[0195] By generating document summaries and keyword lists, users can quickly understand the main content of the document, which is convenient for subsequent information retrieval and management. This method not only improves the efficiency of information processing, but also makes the document content clearer and easier to understand, which is conducive to improving the effectiveness of scientific and technological project management.

[0196] For details, please refer to 1-9. The method also includes using a machine learning algorithm to train a model to automatically identify key information points in a document.

[0197] In this embodiment: using machine learning algorithm to train the model

[0198] 1. Data preparation

[0199] Annotated data: First, you need to prepare a set of annotated data sets that contain key information points that have been manually annotated. Annotations can include but are not limited to keywords, phrases, sentences, etc. in the document.

[0200] Feature engineering: preprocessing of raw data, including text cleaning, word segmentation, stop word removal, and feature extraction, such as word frequency statistics and TF-IDF calculation.

[0201] 2. Model selection

[0202] Supervised learning model: You can use traditional machine learning models such as support vector machine (SVM), logistic regression (Logistic Regression), random forest (Random Forest), or you can choose deep learning models such as convolutional neural network (CNN), recurrent neural network (RNN), or long short-term memory (LSTM).

[0203] Unsupervised learning models: In some cases, if there is a lack of sufficient labeled data, you can try to use unsupervised learning methods such as clustering algorithm K-means, topic model LDA, etc. to discover patterns in documents.

[0204] 3. Model training

[0205] Training process: Use the labeled dataset to train the selected model and minimize the prediction error by adjusting the model parameters.

[0206] Cross-validation: In order to evaluate the generalization ability of the model, the cross-validation method can be used to test the performance of the model on unseen data.

[0207] 4. Model evaluation and tuning

[0208] Evaluation indicators: Use evaluation indicators such as accuracy, precision, recall, and F1 score to measure the performance of the model.

[0209] Hyperparameter tuning: Adjust the model’s hyperparameters through methods such as Grid Search and Random Search to achieve better performance.

[0210] 5. Model Application

[0211] Automatic identification: The trained model can be applied to new documents to automatically identify key information points.

[0212] Continuous learning: Over time, the model can be updated to adapt to new document types and changing contexts.

[0213] Specific implementation steps

[0214] Assume that we need to automatically identify key information points from scientific and technological project management documents. The specific steps are as follows:

[0215] Data preparation:

[0216] Collect and annotate a series of documents, including project name, key technologies, research objectives, expected results, etc.

[0217] Preprocess the document, including text cleaning, word segmentation and other steps.

[0218] Model selection:

[0219] Choose a model suitable for text classification tasks, such as BERT or LSTM, which can handle text sequence data well.

[0220] Model training:

[0221] Use the labeled dataset to train the model and optimize the model performance by adjusting parameters such as learning rate and batch size.

[0222] Cross-validation was used to evaluate the performance of the model on different subsets of data.

[0223] Model evaluation and tuning:

[0224] Evaluate the performance of the model on the test set to ensure that the model has good generalization ability.

[0225] Perform hyperparameter tuning to find the optimal model configuration.

[0226] Model Application:

[0227] Deploy the trained model to the actual application environment to automatically identify key information points in new documents.

[0228] Update the model regularly to ensure it adapts to the latest document types and changing requirements.

[0229] Through this method, the system can automatically identify key information points in documents, greatly improving the efficiency and accuracy of information processing and providing strong support for scientific and technological project management.

[0230] For details, please refer to 1-9. The method can also update the model through a continuous learning mechanism to adapt to changes in project documents.

[0231] In this embodiment: Continuous learning mechanism

[0232] 1. Data collection and update

[0233] Real-time data collection: The system needs to continuously collect new document data from the project management process, including research reports, meeting minutes, emails, etc.

[0234] Data cleaning and labeling: Preprocess the newly collected data, including text cleaning and analysis, and label it as needed for model training.

[0235] 2. Model incremental training

[0236] Incremental learning: When new labeled data is added, there is no need to retrain the entire model. Instead, an incremental learning method is used to only fine-tune the original model to adapt to the new data.

[0237] Online learning: In some cases, online learning can be used, where the model learns and adjusts instantly as it receives new data, adapting to changes in real time.

[0238] 3. Model evaluation and update

[0239] Regular evaluation: Regularly evaluate the model's performance on new data to ensure that the model's performance meets expectations.

[0240] Model update strategy: Based on the evaluation results, decide whether the model needs to be updated. If the model performance deteriorates, update it; if the performance is stable, keep the existing model unchanged.

[0241] 4. Model deployment and monitoring

[0242] Model deployment: Deploy the updated model to a production environment to process new project documents.

[0243] Real-time monitoring: After deployment, the system needs to monitor the performance of the model in real time and collect feedback data to prepare for the next model update.

[0244] Specific implementation steps:

[0245] Suppose we need to maintain an automatic recognition system that can adapt to changes in project documents. The specific steps are as follows:

[0246] Data collection and update:

[0247] Real-time data collection: Automatically collect new document data by integrating with project management system APIs or other data sources.

[0248] Data cleaning and annotation: Preprocess the newly collected data and annotate it as needed, such as annotating new technical terms, project phase progress and other information.

[0249] Model incremental training:

[0250] Incremental learning: When new labeled data is available, the model is updated using incremental learning methods. For example, transfer learning techniques can be used to fine-tune the original model based on the new data.

[0251] Online learning: For scenarios with high real-time requirements, online learning can be used, that is, real-time learning while receiving new data.

[0252] Model evaluation and update:

[0253] Periodic evaluation: Set up a periodic evaluation mechanism to regularly check the performance of the model on new data.

[0254] Model update strategy: Based on the evaluation results, formulate an update strategy. If the model performance deteriorates, use a new dataset to retrain or fine-tune the model; if the performance is stable, keep the existing model unchanged.

[0255] Model deployment and monitoring:

[0256] Model deployment: Deploy the updated model to the production environment to ensure that the system can recognize new document information in a timely manner.

[0257] Real-time monitoring: After deployment, monitor the system’s performance in real time and collect feedback data for subsequent updates.

[0258] Implementation Effect

[0259] Through the continuous learning mechanism, the system can dynamically adapt to changes in project documents and continuously optimize its recognition capabilities. This mechanism not only improves the flexibility and adaptability of the system, but also reduces the recognition errors caused by document changes, ensuring the accuracy and timeliness of information processing.

[0260] By introducing a continuous learning mechanism, this method can automatically adapt to changes in project documents and keep the model up to date, thereby improving the accuracy and efficiency of automatically identifying key information points. For scientific and technological project management, this means that changes can be responded to more quickly, and decision-making quality and project management efficiency can be improved.

[0261] For details, please refer to 1-9. The method also includes visually displaying the analysis results so that project managers can intuitively understand the document content.

[0262] In this embodiment: Implementation of visual display

[0263] 1. Design a visual interface

[0264] Dashboard Design: Create a comprehensive dashboard that displays key statistics of your documents, such as total number of documents, keyword frequency distribution, etc.

[0265] Chart type selection: Select the appropriate chart type according to the type of data to be displayed, such as bar chart, pie chart, heat map, word cloud chart, etc.

[0266] 2. Display document summary and keywords

[0267] Document summary: Display the document summary in concise and clear text form, so that users can understand the core content of the document at a glance.

[0268] Keyword list: Display the keywords in the document in a word cloud or other form. Important keywords can be highlighted in larger fonts or colors.

[0269] 3. Display document classification results

[0270] Classification tree diagram: Use a tree diagram to display the classification results of documents to help users understand the hierarchical relationship between documents.

[0271] Heat map: A heat map is used to show the quantity distribution of documents in different categories. The depth of color indicates the number of documents.

[0272] 4. Display similarity analysis results

[0273] Similarity Matrix: Use a matrix or heat map to show the similarity between documents, with light and dark colors indicating higher or lower similarity.

[0274] Network graph: Draw a similarity network graph between documents, where nodes represent documents and edges represent similarity relationships between documents.

[0275] Specific implementation steps:

[0276] Assume that we need to visualize the analysis results of scientific and technological project management documents. The specific steps are as follows:

[0277] Design visual interface:

[0278] Create a comprehensive dashboard page that displays basic statistics of your documents, such as total number of documents, keyword frequency distribution, etc.

[0279] Select an appropriate chart type, such as a bar chart to show the distribution of document quantities, a pie chart to show the proportion of document categories, etc.

[0280] Display document summary and keywords:

[0281] Set up an area in the dashboard specifically for displaying document summaries, using concise and clear text format.

[0282] Use word cloud charts to display keywords in the document. Important keywords can be enlarged so that users can see the key points at a glance.

[0283] Display document classification results:

[0284] Use a tree diagram to display the classification results of documents to help users understand the hierarchical relationship between documents.

[0285] The heat map shows the distribution of the number of documents in different categories, and the depth of color indicates the number of documents.

[0286] Display similarity analysis results:

[0287] Use a heat map to display the similarity matrix between documents, with light and dark colors indicating higher or lower similarities.

[0288] Draw a similarity network diagram between documents. Nodes represent documents, edges represent similarity relationships between documents, and color and thickness can indicate the degree of similarity.

[0289] Implementation Effect

[0290] Through visual display, project managers can understand the document content more intuitively, as shown in the following:

[0291] Improve decision-making efficiency: Managers can quickly understand the main information of the document through intuitive charts and speed up the decision-making process.

[0292] Facilitates information sharing: Visualized results are easier to understand and disseminate, making it easier to share information among team members.

[0293] Enhanced communication: Charts and graphical presentations help enhance communication, making discussions more focused and efficient.

[0294] By visualizing the results of document analysis, project managers can grasp the key information of documents more quickly and intuitively, thereby improving the efficiency and quality of information processing. This method not only simplifies the presentation of information, but also improves the efficiency of teamwork and decision-making, providing strong support for scientific and technological project management.

[0295] For details, please refer to 1-9. The method is integrated into the existing project management system as a plug-in to enhance the system functions.

[0296] In this embodiment: the purpose of plug-in integration

[0297] Enhancement: Add new capabilities to existing systems, such as data mining, natural language processing, machine learning models, etc.

[0298] Improve user experience: Enable users to manage and analyze project-related documents and data more effectively.

[0299] Facilitate collaboration: Improve communication and collaboration among teams by adding interactivity and visual components.

[0300] Plugin development and integration steps

[0301] Demand Analysis

[0302] Identify user needs and deficiencies in the existing system.

[0303] Gather user feedback, determine which features are necessary, and assess priorities.

[0304] Designing a plugin architecture

[0305] Design the overall architecture of the plugin, including how it interacts with existing systems.

[0306] Determine the functional modules that the plug-in needs to support and their interrelationships.

[0307] Developing plugins

[0308] Choose the appropriate technology stack programming language, framework, etc. for plug-in development.

[0309] Write code, and make sure to follow software engineering best practices.

[0310] Testing the plugin

[0311] Perform unit testing, integration testing, and system testing to ensure that the plug-in can run normally and does not affect the stability of the existing system.

[0312] Collect test results and fix any issues found.

[0313] Deploy plugin

[0314] Deploy the developed plug-in to the production environment and make it part of the existing system.

[0315] Provide detailed installation guides and user manuals to ensure users can enable new features smoothly.

[0316] Training and support

[0317] Provide necessary training to users to let them know how to use the newly added functions.

[0318] Provide technical support and answer questions users encounter during use.

[0319] Continuous Improvement

[0320] Collect user feedback regularly and optimize plugin functionality accordingly.

[0321] Track plugin performance and perform maintenance and updates when necessary.

[0322] Examples of specific plugin functionality

[0323] Suppose we are integrating a document analysis plug-in into an existing project management system. The plug-in may include the following functions:

[0324] Automatic document classification: Automatically classify documents into corresponding folders or tags based on their content.

[0325] Keyword extraction: Identifying key terms in documents and allowing users to search based on those keywords.

[0326] Content Summary: Generate a summary of the document to help users quickly browse the main content.

[0327] Visual presentation: Convert the results of document analysis into charts or graphical interfaces to make the information easier to understand.

[0328] By integrating document analysis methods into existing project management systems, we can significantly improve the usability and functionality of the system, while also improving the user experience. This integration should focus on user needs, as well as compatibility and stability with existing systems. In addition, continuous feedback loops and improvements are also key factors to ensure the long-term success of the plugin.

[0329] The working principle and use process of the present invention are as follows: first, the document is preprocessed to remove irrelevant characters, HTML tags, special symbols and other non-text information, and the pure text content is retained. The cleaned text is segmented, and long sentences are divided into words or phrases. Common stop words are removed from the results after word segmentation. These words do not contribute much to semantic understanding, but will increase data noise. They are uniformly converted to lowercase letters, punctuation marks are removed or replaced, numbers and special characters are standardized, and meaningful lexical features such as keywords, phrases or topics are extracted from the preprocessed documents. The extracted lexical features are converted into numerical vector representations. Commonly used methods include TF-IDF, Word 2Vec, GloVe or BERT, etc., based on the obtained document vector, use algorithms such as cosine similarity and Jaccard similarity to calculate the similarity between documents or terms, set the similarity threshold according to the application scenario, and only when the similarity of two documents exceeds the threshold, they are considered to have a high semantic similarity, prepare annotated data sets, including key information points that have been manually annotated, select a suitable machine learning model, such as SVM, logistic regression, random forest, CNN, RNN or BERT, etc., use the annotated data set to train the model, minimize the prediction error by adjusting the model parameters, and use accuracy, precision, The recall rate, F1 score and other evaluation indicators are used to measure the model performance, and the model performance is improved through hyperparameter tuning. Natural language processing technology is used to automatically generate document summaries, extract keywords from documents, and generate keyword lists based on weight sorting. A comprehensive dashboard page is created to display the main statistical data of the document. Charts such as bar charts, pie charts, heat maps, word clouds, etc. are used to display document summaries, keywords, classification results and similarity analysis results. Users upload documents to be analyzed through the project management system, and the system automatically pre-processes the uploaded documents, including text cleaning, word segmentation, stop word removal and standardization. The system extracts lexical features in the document and It is converted into a numerical vector representation. The system calculates the semantic similarity between documents and judges the similarity of documents according to the set threshold. The system uses the trained machine learning model to automatically identify the key information points in the document. The system generates a document summary and extracts a keyword list. The system displays the analysis results through a visual interface. Users can quickly understand the main information of the document through charts. Users can use the classification, tagging, summary and keyword functions provided by the system to manage and retrieve documents. Through the above working principle and usage process, the present invention can effectively help project managers understand and manage a large amount of document information and improve the efficiency and accuracy of information processing. Users can obtain the key information of the document through simple operations and intuitively display the analysis results through visualization tools, thereby better supporting project management decisions.

[0330] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A semantic similarity analysis method for scientific and technological project management, characterized in that: The following steps are involved: S1: The data collection module collects text data related to scientific and technological projects; S2: The data processing module performs preprocessing such as cleaning, word segmentation, and stop word removal on the collected text data, and converts the processed data into vector representation; S3: The semantic analysis module calculates the semantic similarity between texts based on the preprocessed vector data; S4: The result display module displays the semantic similarity analysis results through a graphical interface or other forms, and provides user interaction functions.

2. The semantic similarity analysis method according to claim 1, characterized in that: The data collection module can also automatically capture relevant information from an external database.

3. The semantic similarity analysis method according to claim 2, characterized in that: The data processing module further includes a data standardization processing unit for converting text data from different sources into a unified format.

4. The semantic similarity analysis method according to claim 3, characterized in that: The semantic analysis module also includes a weight assignment unit for assigning corresponding weight values ​​according to the importance and context of different texts. The weight calculation formula is: w i =α·importance i +β·context i Among them, w i is the weight of the i-th text, α and β are weight coefficients, is the importance of the i-th text, context i is the contextual relevance of the ith text.

5. A semantic similarity analysis method for scientific and technological project management according to any of the preceding claims, wherein the result display module further comprises an interactive analysis tool, allowing a user to dynamically adjust parameters and instantly view result changes.

6. A semantic similarity analysis method for scientific and technological project management according to any of the preceding claims, wherein the system further comprises a feedback mechanism, allowing the user to make modification suggestions based on the displayed results and feed them back to the data processing module for updating, the feedback mechanism comprising: S61: The user submits feedback through the result display module; S62: The feedback processing module receives and analyzes user feedback; S63: Based on the feedback, update the preprocessing rules of the data processing module and the weight allocation strategy of the semantic analysis module.

7. A semantic similarity analysis method for scientific and technological project management according to any of the preceding claims, wherein the feedback mechanism also supports users to add new evaluation dimensions and integrate them into the existing model.

8. A semantic similarity analysis method for scientific and technological project management according to any of the preceding claims, wherein the semantic analysis module further comprises a prediction analysis unit for predicting future trends and development directions based on historical data.

9. A semantic similarity analysis method for scientific and technological project management according to any of the preceding claims, wherein the system further comprises a security module for protecting data in the system from unauthorized access or tampering, the security module comprising: S91: data encryption unit, used for encrypting sensitive data; S92: Access control unit, used to manage user rights and prevent unauthorized access; S93: Logging unit, used to record system operation logs for auditing and tracking.

10. A semantic similarity analysis method for scientific and technological project management according to any of the preceding claims, wherein the system further comprises a user interface module, wherein the user interface module is connected to the result display module and is used to provide a user with a visual display of the semantic similarity analysis to help the user understand and manage the text data in the project.

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