AI (authentication report) auxiliary query method
Through AI-assisted query methods, natural language processing and intelligent search technology are used to solve the problem of inefficiency of traditional query methods, and the rapid and accurate acquisition of key information in the identification report is achieved, and query efficiency and accuracy are improved.
Patent Information
- Application Number
- CN202411976712.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2025-06-06
AI Technical Summary
The traditional identification report query method based on manual or simple database retrieval is inefficient, it is difficult to quickly locate specific information in complex reports, and it is impossible to understand professional terms and data relationships, resulting in incomplete or inaccurate query results.
Using AI-assisted query methods, through natural language processing, data analysis and intelligent search technology, key information in the report is identified and extracted, text classification, entity recognition and relationship extraction are carried out, automatic summary is generated, and semantic search and knowledge graphs are used for in-depth query and analysis.
It achieves rapid and accurate acquisition of key information in the identification report, improves query efficiency and accuracy, can process complex and multi-dimensional data, and provides deeper analysis and understanding.
Smart Images
Figure CN120104778A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of appraisal report query, and specifically to an appraisal report AI-assisted query method. Background Art
[0002] Civil engineering quality appraisal reports contain a large amount of complex professional information, such as mechanical performance data of building structures, material quality inspection data (including concrete strength, steel bar performance, etc.), construction process records, foundation conditions, etc. These data usually exist in various forms such as text, tables, drawings, etc. For example, a building structure appraisal report may contain calculations for the bearing capacity of beams and columns, as well as detailed tabular data for the compressive strength test of concrete test blocks.
[0003] In the entire life cycle of civil engineering, including quality monitoring during the construction process, completion acceptance, safety assessment during the use phase, and accident investigation after problems occur, it is necessary to frequently query quality appraisal reports. Different stakeholders, such as construction units, construction units, supervision units, quality supervision departments, etc., have their own query focuses. For example, construction units may pay more attention to the impact of construction process-related content on quality, while quality supervision departments focus on checking whether relevant standards and specifications are met.
[0004] Traditional manual or simple database search-based query methods are difficult to meet the needs. Manual query is inefficient, especially when faced with a large number of reports and complex content, it is very time-consuming to find specific information. Moreover, simple database search cannot understand the complex relationship between professional terms and data, and can only match keywords, resulting in incomplete or inaccurate query results.
[0005] Therefore, those skilled in the art provide an AI-assisted query method for identification reports to solve the problems raised in the above-mentioned background technology. Summary of the invention
[0006] In view of the shortcomings of the prior art, the present invention provides an AI-assisted query method for identification reports, which solves the problem that traditional query methods based on manual or simple database retrieval are difficult to meet the needs, and manual query is inefficient. Especially when faced with a large number of reports and complex content, it is very time-consuming to find specific information. Moreover, simple database retrieval cannot understand the complex relationship between professional terms and data, and can often only match keywords, resulting in the problem that the query results may be incomplete or inaccurate. Technical Solution
[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The AI-assisted query method for identification report includes the following steps: Step S1. First, determine whether the appraisal report is electronic. If the report is electronic, it is usually stored in PDF or Word format, and its document is recognized by AI. If the report is a paper version, it is converted into editable text through optical character recognition technology (OCR); Step S2. Extract key information from the report through AI, remove irrelevant content or format errors, and ensure the accuracy of the text content; Step S3. Based on the content of the appraisal report, use the AI model to classify it to help identify the type and content of the report; Step S4. Use entity recognition and relationship extraction technology to extract key information from the appraisal report, such as the appraisal conclusion, the legal provisions involved, and the data results; Step S5. Using automatic summary generation technology to help extract the core content of the appraisal report to save review time; Step S6. The AI model searches through keywords or query statements in the appraisal report to quickly locate relevant paragraphs or content in the appraisal report, helping users quickly find the required information; Step S7. Analyze the content of the appraisal report according to the context of the appraisal report through the AI model to help users understand complex appraisal conclusions and technical details and provide more accurate explanations; Step S8. For some highly repetitive appraisal reports, the AI model automatically generates the report content based on the known template. The user only needs to provide some necessary information, and the AI can automatically fill in the corresponding appraisal report; Step S9. For an identification report containing a large amount of data, the corresponding data chart is automatically generated through the AI model to help users understand the identification results more intuitively; Step S10. By constructing a knowledge graph related to identification, the AI model associates key information and data in different reports, making it easier for users to conduct deeper queries and analysis.
[0008] Furthermore, the specific process of converting it into editable text by optical character recognition technology (OCR) in step S1 is as follows: 1) Use a scanner to scan and identify the paper appraisal report; 2) Use OCR algorithm to remove noise from the scanned image and enhance its clarity; 3) Recognize the text in the image through the OCR algorithm and convert it into text; 4) Text processed by OCR algorithms usually needs to be further organized and formatted to restore the original format, such as tables, paragraphs, and titles; 5) Convert the scanned report into a format suitable for storage, management and query, so as to facilitate later editing, archiving and sharing.
[0009] Furthermore, in step S3, the process of text classification of the report is as follows: 1) Remove irrelevant characters in the report, such as punctuation marks, HTML tags, and special characters; 2) Split the text into words or subwords and remove stop words in the text; 3) Split the text into words or subwords and convert the text into a digital form that can be processed by machine learning algorithms; 4) Select appropriate machine learning models to train text classification content, such as naive Bayes classifier, support vector machine, decision tree, and convolutional neural network; 5) Further process the vectorized text data to extract meaningful features, so that the classification model can have a deeper understanding of the text content; 6) Use some standard evaluation indicators to verify the performance of the model, and use cross-validation and hyperparameter tuning methods to optimize the performance of the model and improve classification accuracy.
[0010] Furthermore, in step S5, the automatic summary generation technology can select important sentences or phrases in the original text, and then select the most representative sentences therein to directly splice and form a summary. Common methods include: TF-IDF: Sentences in a text are scored based on term frequency-inverse document frequency, thereby selecting sentences with high TF-IDF scores; TextRank: A graph-based algorithm that calculates the similarity between sentences and constructs a graph to select important sentences. LexRank: Select the most representative sentences by calculating the similarity matrix between sentences.
[0011] Furthermore, in step S6, semantic search can be used to retrieve the content of the appraisal report. Compared with traditional keyword-based search, semantic search can understand the intention behind the query and provide more accurate results.
[0012] Furthermore, in step S8, the AI model can also provide suggestions for improving the report content based on historical data and similar cases to ensure the accuracy and professionalism of the report.
[0013] Furthermore, in step S9, the AI model can also perform trend analysis or comparative analysis on the data in the report to help users identify important changes or potential problems.
[0014] Furthermore, in step S10, by using knowledge graph and machine learning, the AI model can provide natural language question-answering services. Users can ask questions through simple queries, and the AI model can provide relevant answers or reasoning processes.
[0015] Furthermore, the AI model can also be integrated with existing appraisal systems, electronic archive management systems, and legal databases to ensure seamless integration of queries and information extraction. For cross-border or cross-regional appraisal reports, the AI model can provide multilingual translation and localization support to ensure that users of different languages can obtain accurate query information.
[0016] Furthermore, the AI model can also automatically evaluate the quality of the report, such as checking for spelling errors, format inconsistencies, and logical loopholes in the report, and can compare it with other similar reports or data sources to verify whether the report results are consistent or there are anomalies, helping to improve the reliability of report queries. Beneficial Effects
[0017] The present invention provides an AI-assisted query method for identification reports. It has the following beneficial effects: The present invention provides an AI-assisted query method for appraisal reports. The method mainly provides users with efficient and accurate information extraction and analysis services through natural language processing, data analysis, intelligent search and other technologies. Through these methods, users can quickly and conveniently obtain key information in the appraisal report, effectively improving work efficiency.
[0018] The present invention provides an AI-assisted query method for appraisal reports. The method can quickly extract key data from different types of appraisal reports through big data technology, conduct cross-analysis, and help users fully understand the background and related information of the report. It can also process data of multiple dimensions at the same time, conduct deeper and more detailed queries and analyses, and help users understand problems from different angles.
[0019] The present invention provides an AI-assisted query method for identification reports, which can format all reports into a unified standard to avoid confusion caused by inconsistent formats. This consistency helps to improve the readability and effectiveness of the reports. By training AI models to identify common patterns in reports, it can ensure that the reports are structurally in line with established standards and reduce the impact of human differences. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 This is a flow chart of the AI-assisted query method for the appraisal report of the present invention; Figure 2 A schematic diagram of a process of converting a report into an editable text according to the present invention; Figure 3 It is a schematic diagram of the process of text classification of reports according to the present invention. DETAILED DESCRIPTION
[0021] The following will be combined with the drawings in the specific embodiments of the present invention to clearly and completely describe the technical solutions in the specific embodiments of the present invention. Obviously, the specific embodiments described are only part of the specific embodiments of the present invention, not all of the specific embodiments. Based on the specific embodiments of the present invention, all other specific embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0022] like Figure 1-3 As shown, the specific embodiment of the present invention provides an AI-assisted query method for identification reports, comprising the following steps: Step S1. First, determine whether the appraisal report is electronic. If the report is electronic, it is usually stored in PDF or Word format, and its document is recognized by AI. If the report is a paper version, it is converted into editable text through optical character recognition technology (OCR). The specific process is as follows: 1) Use a scanner to scan and identify the paper appraisal report; 2) Use OCR algorithm to remove noise from the scanned image and enhance its clarity; 3) Recognize the text in the image through the OCR algorithm and convert it into text; 4) Text processed by OCR algorithms usually needs to be further organized and formatted to restore the original format, such as tables, paragraphs, and titles; 5) Convert the scanned report into a format suitable for storage, management and query, so as to facilitate later editing, archiving and sharing; Step S2. Extract key information from the report through AI, remove irrelevant content or format errors, and ensure the accuracy of the text content; Step S3. Based on the content of the appraisal report, use the AI model to classify it to help identify the type and content of the report. The process of text classification of the report is as follows: 1) Remove irrelevant characters in the report, such as punctuation marks, HTML tags, and special characters; 2) Split the text into words or subwords and remove stop words in the text; 3) Split the text into words or subwords and convert the text into a digital form that can be processed by machine learning algorithms; 4) Select appropriate machine learning models to train text classification content, such as naive Bayes classifier, support vector machine, decision tree, and convolutional neural network; 5) Further process the vectorized text data to extract meaningful features, so that the classification model can have a deeper understanding of the text content; 6) Use some standard evaluation indicators to verify the performance of the model, and use cross-validation and hyperparameter tuning methods to optimize the performance of the model and improve classification accuracy; Step S4. Use entity recognition and relationship extraction technology to extract key information from the appraisal report, such as the appraisal conclusion, the legal provisions involved, and the data results; Step S5. Using automatic summary generation technology to help extract the core content of the appraisal report to save review time; Automatic summary generation technology can select important sentences or phrases in the original text, and then select the most representative sentences from them to directly splice them into a summary. Common methods include: TF-IDF: Sentences in a text are scored based on term frequency-inverse document frequency, thereby selecting sentences with high TF-IDF scores; TextRank: A graph-based algorithm that calculates the similarity between sentences and constructs a graph to select important sentences. LexRank: Select the most representative sentences by calculating the similarity matrix between sentences; Step S6. The AI model searches through keywords or query statements in the appraisal report to quickly locate relevant paragraphs or content in the appraisal report, helping users quickly find the required information. It can also use semantic search to search the content of the appraisal report. Compared with traditional keyword-based search, semantic search can understand the intention behind the query and provide more accurate results. Step S7. Analyze the content of the appraisal report according to the context of the appraisal report through the AI model to help users understand complex appraisal conclusions and technical details and provide more accurate explanations; Step S8. For some appraisal reports with high repetitiveness, the AI model automatically generates the report content based on the known template. The user only needs to provide some necessary information, and the AI can automatically fill in the corresponding appraisal report. The AI model can also give improvement suggestions for the report content based on historical data and similar cases to ensure the accuracy and professionalism of the report; Step S9. For an appraisal report containing a large amount of data, the AI model automatically generates corresponding data charts to help users understand the appraisal results more intuitively. The AI model can also perform trend analysis or comparative analysis on the data in the report to help users identify important changes or potential problems; Step S10. By constructing a knowledge graph related to identification, the AI model associates key information and data in different reports, making it easier for users to conduct deeper queries and analysis. By using knowledge graphs and machine learning, the AI model can provide natural language question-and-answer services. Users can ask simple queries and the AI model can provide relevant answers or reasoning processes.
[0023] AI models can also be integrated with existing appraisal systems, electronic archive management systems, and legal databases to ensure seamless integration of queries and information extraction. For cross-border or cross-regional appraisal reports, AI models can provide multilingual translation and localization support to ensure that users of different languages can obtain accurate query information.
[0024] The AI model can also automatically evaluate the quality of reports, such as checking for spelling errors, format inconsistencies, and logical loopholes in the reports. It can also compare reports with other similar reports or data sources to verify whether the report results are consistent or contain anomalies, helping to improve the reliability of report queries.
[0025] In the present invention, the method mainly provides users with efficient and accurate information extraction and analysis services through natural language processing, data analysis, intelligent search and other technologies. Through these methods, users can quickly and conveniently obtain key information in the appraisal report, effectively improving work efficiency.
[0026] In the present invention, the method can use big data technology to quickly extract key data from different types of appraisal reports and conduct cross-analysis to help users fully understand the background and related information of the report. It can also process data of multiple dimensions at the same time to conduct deeper and more detailed queries and analyses, helping users understand problems from different angles.
[0027] In the present invention, the method can format all reports into a unified standard to avoid confusion caused by inconsistent formats. This consistency helps to improve the readability and effectiveness of the reports. By training the AI model to identify common patterns in the reports, it can ensure that the reports are structurally in line with established standards and reduce the impact of human differences.
[0028] Although specific embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the specific embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. An AI-assisted query method for identification reports, characterized in that: The following steps are involved: Step S1. First, determine whether the appraisal report is electronic. If the report is electronic, it is usually stored in PDF or Word format, and its document is recognized by AI. If the report is a paper version, it is converted into editable text through optical character recognition technology; Step S2. Extract key information from the report through AI, remove irrelevant content or format errors, and ensure the accuracy of the text content; Step S3. Based on the content of the appraisal report, use the AI model to classify it to help identify the type and content of the report; Step S4. Use entity recognition and relationship extraction technology to extract key information from the appraisal report, such as the appraisal conclusion, the legal provisions involved, and the data results; Step S5. Using automatic summary generation technology to help extract the core content of the appraisal report to save review time; Step S6. The AI model searches through keywords or query statements in the appraisal report to quickly locate relevant paragraphs or content in the appraisal report, helping users quickly find the required information; Step S7. Analyze the content of the appraisal report according to the context of the appraisal report through the AI model to help users understand complex appraisal conclusions and technical details and provide more accurate explanations; Step S8. For some highly repetitive appraisal reports, the AI model automatically generates the report content based on the known template. The user only needs to provide some necessary information, and the AI can automatically fill in the corresponding appraisal report; Step S9. For an identification report containing a large amount of data, the corresponding data chart is automatically generated through the AI model to help users understand the identification results more intuitively; Step S10. By constructing a knowledge graph related to identification, the AI model associates key information and data in different reports, making it easier for users to conduct deeper queries and analysis.
2. The AI-assisted query method for identification reports according to claim 1, characterized in that: The specific process of converting it into editable text by optical character recognition technology in step S1 is as follows: 1) Use a scanner to scan and identify the paper appraisal report; 2) Use OCR algorithm to remove noise from the scanned image and enhance its clarity; 3) Recognize the text in the image through the OCR algorithm and convert it into text; 4) Text processed by OCR algorithms usually needs to be further organized and formatted to restore the original format, such as tables, paragraphs, and titles; 5) Convert the scanned report into a format suitable for storage, management and query, so as to facilitate later editing, archiving and sharing.
3. The AI-assisted query method for identification reports according to claim 1, characterized in that: In step S3, the process of text classification of the report is as follows: 1) Remove irrelevant characters in the report, such as punctuation marks, HTML tags, and special characters; 2) Split the text into words or subwords and remove stop words in the text; 3) Split the text into words or subwords and convert the text into a digital form that can be processed by machine learning algorithms; 4) Select appropriate machine learning models to train text classification content, such as naive Bayes classifier, support vector machine, decision tree, and convolutional neural network; 5) Further process the vectorized text data to extract meaningful features, so that the classification model can have a deeper understanding of the text content; 6) Use some standard evaluation indicators to verify the performance of the model, and use cross-validation and hyperparameter tuning methods to optimize the performance of the model and improve classification accuracy.
4. The AI-assisted query method for identification reports according to claim 1, characterized in that: In step S5, the automatic summary generation technology can select important sentences or phrases in the original text, and then select the most representative sentences therein, and directly splice them to form a summary. Common methods include: TF-IDF: Sentences in a text are scored based on term frequency-inverse document frequency, thereby selecting sentences with high TF-IDF scores; TextRank: A graph-based algorithm that calculates the similarity between sentences and constructs a graph to select important sentences. LexRank: Select the most representative sentences by calculating the similarity matrix between sentences.
5. The AI-assisted query method for identification reports according to claim 1, characterized in that: In step S6, semantic search can also be used to retrieve the content of the appraisal report. Compared with traditional keyword-based search, semantic search can understand the intention behind the query and provide more accurate results.
6. The AI-assisted query method for identification reports according to claim 1, characterized in that: In step S8, the AI model can also provide suggestions for improving the report content based on historical data and similar cases to ensure the accuracy and professionalism of the report.
7. The AI-assisted query method for identification reports according to claim 1, characterized in that: In step S9, the AI model can also perform trend analysis or comparative analysis on the data in the report to help users identify important changes or potential problems.
8. The AI-assisted query method for identification reports according to claim 1, characterized in that: In step S10, by using knowledge graph and machine learning, the AI model can provide natural language question-answering services. Users can ask simple queries and the AI model can provide relevant answers or reasoning processes.
9. The AI-assisted query method for identification reports according to claim 1, characterized in that: The AI model can also be integrated with existing appraisal systems, electronic archive management systems, and legal databases to ensure seamless integration of queries and information extraction. For cross-border or cross-regional appraisal reports, the AI model can provide multilingual translation and localization support to ensure that users of different languages can obtain accurate query information.
10. The AI-assisted query method for identification reports according to claim 1, characterized in that: The AI model can also automatically evaluate the quality of the report, such as checking for spelling errors, format inconsistencies, and logical loopholes in the report, and can compare it with other similar reports or data sources to verify whether the report results are consistent or there are anomalies, helping to improve the reliability of report queries.