Archive retrieval method and system, product and readable storage medium

Abstract

An archive retrieval method, system and product and a readable storage medium relate to the field of archive retrieval, and the method comprises the following steps: performing initial matching based on a water conservancy project professional retrieval word, obtaining a professional term dictionary and a business rule template from a water conservancy project corpus, performing word frequency statistics and keyword extraction by using the professional term dictionary, and obtaining a business rule template; and matching the extracted business keywords with the standard terms to obtain professional feature items, substituting the professional feature items into the business rule template for rule matching, determining the engineering stage, the technical index and the construction process of the engineering technology document, and generating an archive business label. And according to the professional role type of the user, selecting a corresponding service label combination for recombination and display. By implementing the method, the correlation between the retrieval result and the user can be improved.

Description

Technical Field

[0001] The present application relates to the field of archive retrieval, and in particular to an archive retrieval method, system, product and readable storage medium. Background Art

[0002] With the vigorous development of water conservancy project construction, various water conservancy projects have generated a large amount of engineering and technical documents in different stages such as planning, design, construction and operation and maintenance. These documents not only record the entire process of engineering construction, but also provide important technical references and experience accumulation for subsequent engineering construction. Therefore, establishing an efficient archive retrieval system is of great significance to the field of water conservancy projects.

[0003] The current archive retrieval system mainly uses a keyword-based retrieval method. By establishing a document index library, the search terms entered by the user are matched with the keywords in the document, and a list of documents containing relevant keywords is returned. At the same time, the system will classify the documents according to basic attributes such as time and type, making it easier for users to browse and filter by category.

[0004] However, due to the high degree of professionalism in water conservancy projects, technical documents contain a large number of professional terms and industry terms. Different design units and construction units have different ways of expression, resulting in the same technical content being described in different ways by different user identities (such as designers, construction workers, etc.). This makes it difficult to accurately understand the professional content of the document by relying solely on keyword matching retrieval methods, resulting in low relevance of retrieval results to users, and users of different identities need to invest a lot of time in manually screening and judging the retrieval results. Summary of the invention

[0005] The present application provides an archive retrieval method, system, product and readable storage medium for improving the relevance of retrieval results to users.

[0006] In a first aspect, the present application provides an archive retrieval method, which is applied to an archive retrieval system, the method comprising: responding to an archive retrieval request from a user, the archive retrieval request containing a professional search term for water conservancy engineering; matching the professional search term for water conservancy engineering in an archive database to obtain an initial search result, the initial search result containing multiple engineering technical documents; obtaining a professional terminology dictionary and a business rule template from a water conservancy engineering corpus, the professional terminology dictionary containing standard terms and business attributes for water conservancy engineering, the business rule template containing rules for determining the engineering stage, rules for extracting technical indicators, and rules for identifying construction processes; using the professional terminology dictionary to perform word frequency statistics and keyword extraction on the text content in the initial search result, matching the obtained business keywords with the standard terms in the professional terminology dictionary, and obtaining professional feature items of the engineering technical document; substituting the professional feature items into the business rule template for rule matching, determining the engineering stage, technical indicators, and construction process of the engineering technical document based on the matching result of the rule matching, and generating an archive business tag; selecting a corresponding business tag combination in the archive business tag according to the professional role type of the user, and reorganizing the initial search result according to a preset display template to obtain an adaptive search result.

[0007] By adopting the above technical solution, after initial matching based on the professional search terms of water conservancy engineering, the professional terminology dictionary and business rule template are obtained from the water conservancy engineering corpus, and the professional terminology dictionary is used to perform word frequency statistics and keyword extraction. The extracted business keywords are matched with standard terms to obtain professional feature items, and then the professional feature items are substituted into the business rule template for rule matching to determine the engineering stage, technical indicators and construction process of the engineering technical documents, and generate archive business tags. According to the user's professional role type, the corresponding business tag combination is selected for reorganization and display, which realizes the professional analysis and personalized presentation of the search results, improves the accuracy and applicability of the retrieval of water conservancy engineering technical documents, and reduces the workload of users in screening and judging the search results.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the professional terminology dictionary is used to perform word frequency statistics and keyword extraction on the text content in the initial search results, and the obtained business keywords are matched with the standard terms in the professional terminology dictionary to obtain the professional feature items of the engineering and technical document. The steps specifically include: performing word segmentation processing on the text content of each engineering and technical document in the initial search results, counting the frequency of occurrence of each word in the engineering and technical document, and selecting words whose frequency exceeds a preset frequency threshold as candidate keywords for the engineering and technical document; screening out words that appear in the professional terminology dictionary from the candidate keywords as business keywords for the engineering and technical document; performing string matching on the business keywords of the engineering and technical document with the standard terms in the professional terminology dictionary one by one, and when the matching degree between the business keywords and the standard terms exceeds the preset matching threshold, the standard terms and corresponding business attributes are used as the professional feature items of the engineering and technical document.

[0009] By adopting the above technical solution, the document content is segmented and counted to obtain candidate keywords, and then the correspondence between the document content and the standardized terms is established through professional terminology dictionary screening and standard terminology matching. Through multi-level screening and threshold control, the accuracy of professional feature item extraction is ensured, laying a solid foundation for subsequent rule matching.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the professional feature items are substituted into the business rule template for rule matching, and the steps of determining the engineering stage, technical indicators and construction process of the engineering technical document based on the matching results of the rule matching to generate an archive business label, specifically including: classifying the professional feature items of the engineering technical document according to business attributes; inputting the professional feature items with engineering stage attributes in the engineering technical document into the engineering stage determination rule to determine the engineering stage to which the engineering technical document belongs; inputting the professional feature items with technical indicator attributes in the engineering technical document into the technical indicator extraction rule to extract the numerical and descriptive technical indicators of the engineering technical document; inputting the professional feature items with construction process attributes in the engineering technical document into the construction process identification rule to identify the main construction process and process parameters of the engineering technical document; and combining the determined engineering stage, extracted technical indicators and identified construction process of the engineering technical document to generate a business label for the engineering technical document.

[0011] By adopting the above technical solution, professional feature items are classified according to business attributes and then input into corresponding judgment rules respectively. Engineering phases, technical indicators and construction process information are extracted and combined to generate business labels. Through regularized and modular processing methods, multi-dimensional analysis of the professional content of engineering technical documents is achieved, thereby enhancing the professional value of the search results.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the step of selecting a corresponding business tag combination in the archive business tag according to the professional role type of the user, and reorganizing the initial search results according to a preset display template to obtain an adaptive search result specifically includes: obtaining the professional role type of the user, and determining the business tag type corresponding to the professional role type according to the role-tag mapping relationship; filtering out a business tag subset of the engineering technical document corresponding to the business tag type from the archive business tags; reorganizing and typeset the engineering phases, technical indicators and construction process information in the business tag subset according to the preset display template to obtain a reorganized technical document; and integrating the reorganized technical document with the basic information of the engineering technical document to obtain an adaptive search result.

[0013] By adopting the above technical solution, the corresponding business tag type is obtained according to the user's professional role type, the matching business tag subset is screened, the professional information content is reorganized according to the preset display template, and the reorganized professional information is integrated with the basic information of the document to form differentiated search results for different professional roles, so that the search display content is more in line with the user's professional needs and the reading efficiency of the search results is improved.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of selecting a corresponding business tag combination in the archive business tag according to the professional role type of the user, and reorganizing the search results according to a preset display template to obtain adaptive search results, the method also includes: counting the water conservancy engineering professional search terms with a preset value ranked before the user's frequency of use within a preset usage time; classifying the water conservancy engineering professional search terms according to the dimensions of project stage, technical indicators and construction technology to form a recommended search term list for the user; when the user adds a new archive search request, displaying the water conservancy engineering professional search terms in the recommended search term list with the highest match degree with the current input characters.

[0015] By adopting the above technical solution, the frequency of use of users' historical search terms is counted and classified according to project stages, technical indicators and construction process dimensions. When users enter new search requests, the recommended search terms with the highest matching degree are displayed. A search term recommendation mechanism based on user usage habits is established, which improves the input accuracy of professional search terms and the efficiency of search operations.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of selecting a corresponding business tag combination in the archive business tag according to the user's professional role type, and reorganizing the search results according to a preset display template to obtain an adaptive search result, the method also includes: extracting the chart page number information contained in the engineering and technical document; and marking the chart page number information in the title of the engineering and technical document in the adaptive search result.

[0017] By adopting the above technical solution, the page number information of figures and tables in engineering and technical documents is extracted, and the page number information is marked in the document title, so that the search results can directly display the location distribution of the figures and tables, and a quick positioning association between the search results and the document content is established, thereby optimizing the efficiency of figure search in engineering and technical documents.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of marking the chart page number information in the document title in the adaptability retrieval results, the method also includes: when the user clicks on the chart page number information, jumping to the page number position corresponding to the engineering technical document.

[0019] By adopting the above technical solution, a page jump function is added to the diagram page number information displayed in the title. When the user clicks the page number information, the user is directly positioned at the corresponding page position of the document. The information display of the search results and the document content access are organically combined, and fast navigation from the search interface to the specific diagram content is achieved, thereby improving the search and browsing experience of engineering and technical documents.

[0020] In a second aspect, an embodiment of the present application provides an archive retrieval system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the archive retrieval system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when the computer program product is run on an archive retrieval system, enables the archive retrieval system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on an archive retrieval system, the archive retrieval system executes the method described in the first aspect and any possible implementation of the first aspect.

[0023] It can be understood that the archive retrieval system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. After the initial matching based on the professional search terms of water conservancy engineering, this application obtains the professional terminology dictionary and business rule template from the water conservancy engineering corpus, uses the professional terminology dictionary to perform word frequency statistics and keyword extraction, matches the extracted business keywords with the standard terms to obtain professional feature items, and then substitutes the professional feature items into the business rule template for rule matching to determine the engineering stage, technical indicators and construction process of the engineering technical documents, and generate archival business tags. According to the user's professional role type, the corresponding business tag combination is selected for reorganization and display, which realizes the professional analysis and personalized presentation of the search results, improves the accuracy and applicability of the retrieval of water conservancy engineering technical documents, and reduces the workload of users in screening and judging the search results.

[0025] 2. This application counts the frequency of use of users' historical search terms, classifies and organizes them by project stage, technical indicators and construction process dimensions, and displays the recommended search terms with the highest matching degree when users enter new search requests. It establishes a search term recommendation mechanism based on user usage habits, thereby improving the input accuracy of professional search terms and the efficiency of search operations.

[0026] 3. This application adds a page jump function to the diagram page number information displayed in the title. When the user clicks the page number information, the user is directly positioned at the corresponding page of the document. This organically combines the information display of the search results with the document content access, and realizes quick navigation from the search interface to the specific diagram content, thereby improving the search and browsing experience of engineering and technical documents. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the archive retrieval method in the embodiment of the present application; Figure 2 is another flow chart of the archive retrieval method in the embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the archive retrieval system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the following is a description of the process of the method provided by this implementation. Figure 1 , is a flow chart of the archive retrieval method in the embodiment of the present application.

[0031] S101. Respond to a file search request from a user, where the file search request includes a search term for water conservancy engineering.

[0032] Among them, archive retrieval request refers to the query operation request initiated by the user in the retrieval system; response refers to the process of the system processing and feeding back the user request; water conservancy engineering professional search terms are used to represent professional terms or keywords related to the field of water conservancy engineering input by the user, such as "flood gate design", "concrete strength", "diversion construction", etc.; inclusion indicates that the search term must exist in the retrieval request.

[0033] The retrieval system executes this step when the user enters the search term through the search interface and triggers the search action. Specifically, after receiving the user's search operation, the system first verifies the validity of the search request and confirms that the request contains the search term. Then the search term is preprocessed, including removing extra spaces, unifying the case format, and identifying the professional attributes of the search term. The system encapsulates the preprocessed search term into a standard search request format to prepare for subsequent matching searches.

[0034] In some embodiments, the response processing of the search request can be implemented in a variety of ways: optionally, a search input box is set to receive user input, the search term is obtained through form submission, the legality of the input content is checked, and the search term is standardized to generate a search request; optionally, a list of search term recommendations is provided for the user to choose, and the user clicks on the recommended term and automatically fills it into the input box, triggering a search event, and the system captures the event and constructs a search request. It is understandable that other methods can also be used to implement the response processing of the search request, such as voice input, scanning input, etc., which are not limited here.

[0035] S102: Matching is performed in the archive database based on the water conservancy engineering professional search term to obtain an initial search result, which includes a plurality of engineering technical documents.

[0036] Among them, the archival database refers to a data storage system that stores engineering technical documents and their metadata; matching refers to the process of comparing and searching the search terms with the document content in the database; the initial search results are used to represent the original document set returned by the matching process; engineering technical documents refer to professional documents such as design documents, construction plans, technical specifications, etc. related to water conservancy project construction.

[0037] This step is performed after the system completes the search request processing and is used to find relevant documents from the archive. Specifically, the system uses the pre-processed search terms as matching conditions to perform a full-text search in the archive database. The search scope includes fields such as document title, text content, and metadata. The system extracts all document information that matches the search terms, sorts them by relevance, and forms an initial search result set.

[0038] In some embodiments, database matching retrieval can be implemented in a variety of ways: optionally, using inverted index technology to establish a document retrieval index, searching the index after segmenting the search terms, counting the word frequency and position information, calculating the document relevance score, sorting by score and returning the matching documents; optionally, using a vector space model, converting the search terms and document content into vector representations, calculating the vector cosine similarity, setting a similarity threshold to filter matching documents, and generating a retrieval result list. It is understandable that other methods can also be used to implement database matching retrieval, such as semantic retrieval, fuzzy matching and other technologies, which are not limited here.

[0039] S103, obtaining a professional terminology dictionary and a business rule template from a water conservancy project corpus, wherein the professional terminology dictionary includes standard terms and business attributes of water conservancy projects, and the business rule template includes determination rules for project stages, extraction rules for technical indicators, and identification rules for construction processes.

[0040] Among them, the water conservancy engineering corpus refers to a professional knowledge base containing a large amount of text data in the field of water conservancy engineering; the professional terminology dictionary is used to represent the vocabulary that includes standardized terms and their attribute information in the field of water conservancy engineering; the business rule template refers to a set of rules used to identify and process the content of professional documents; standard terms represent professional terms that are unified by industry specifications; business attributes refer to characteristic information such as professional classification and usage scenarios of terms; judgment rules are used to represent conditional logic for identifying engineering stages; extraction rules represent pattern matching rules for obtaining technical indicators; and identification rules refer to classification rules for determining construction process types.

[0041] This step is executed when the system is initialized or the knowledge base is updated, and is used to build the basic supporting data for document processing. Specifically, the system connects to the water conservancy engineering corpus, reads the term entries and attribute definitions of the professional term dictionary, and loads various business rule templates. The term dictionary is structured and the term index and attribute mapping relationship are established. At the same time, the business rule template is parsed into an executable rule expression to prepare for the subsequent document feature recognition.

[0042] In some embodiments, professional knowledge acquisition can be achieved in a variety of ways: optionally, access the corpus through a direct database connection, execute query statements to obtain dictionary data, parse the data structure to build a memory dictionary, load the rule configuration file to parse the rule template, and establish a rule execution engine; optionally, use knowledge graph technology to build a terminology system, extract term nodes and relationship edges in the graph to build a dictionary, generate business rule templates based on the inference rules of the graph, and build a rule inference mechanism. It is understandable that other methods can also be used to achieve the acquisition and loading of professional knowledge, such as distributed caching, real-time synchronization and other technical solutions, which are not limited here.

[0043] S104, using the professional terminology dictionary to perform word frequency statistics and keyword extraction on the text content in the initial search result, matching the obtained business keywords with the standard terms in the professional terminology dictionary to obtain the professional feature items of the engineering technical document.

[0044] Among them, word frequency statistics refers to the process of calculating the number of times each word appears in a document; keyword extraction is used to represent the operation of identifying important words from a text; business keywords refer to important terms related to water conservancy engineering business; standard term matching refers to the process of matching extracted keywords with dictionary terms; professional feature items are used to represent the professional attribute identifiers of documents.

[0045] This step is performed after obtaining the initial search results, and is used to deeply analyze the document content and extract professional features. Specifically, the system first performs word segmentation on the document text, counts the frequency of occurrence of each word, and extracts keywords based on word frequency and position information. Then the extracted keywords are matched with the professional terminology dictionary to determine the standardized term form and business attributes of each keyword. Finally, the matching results are integrated to form a set of feature items that reflect the professional characteristics of the document.

[0046] In some embodiments, document feature extraction can be achieved in a variety of ways: optionally, using the TF-IDF algorithm to calculate word importance, selecting words with importance exceeding a threshold as keywords, querying the term dictionary to obtain standard terms, extracting the business attributes of the terms as feature items, and constructing a feature vector for the document; optionally, using a deep learning model for text feature learning, using a pre-trained model to extract text semantic features, mapping the features to the term space for standardization, and generating feature item descriptions based on the attributes of the terms. It is understandable that other methods can also be used to achieve document feature extraction and standardization, such as topic models, semantic similarity and other analysis methods, which are not limited here.

[0047] S105, substituting the professional feature item into the business rule template for rule matching, determining the engineering phase, technical indicators and construction process of the engineering technical document based on the matching result of the rule matching, and generating an archive business tag.

[0048] Among them, rule matching refers to the process of comparing and verifying feature items with business rules; matching results are used to represent the judgment data obtained after rule verification; engineering stages represent different periods of project construction, such as feasibility study, preliminary design, construction drawing design, etc.; technical indicators refer to key parameters and performance requirements in engineering construction; construction technology is used to represent specific construction methods and technical measures; archive business tags represent the business attribute classification identification of documents.

[0049] This step is performed after the professional feature extraction is completed, and is used to deeply analyze the business features of the document. Specifically, the system uses the extracted professional feature items as input and substitutes them into the business rule template for rule calculation. First, the engineering phase determination rule is applied to determine the project phase based on the timing features and professional terms in the document. Then, the technical indicator extraction rule is used to identify the parameter values ​​and performance descriptions in the document. Then, the construction process identification rule is used to analyze the process flow and technical solutions in the document. Finally, these analysis results are integrated to form a business tag set for the document.

[0050] In some embodiments, rule matching and label generation can be achieved in a variety of ways: optionally, using rule engine technology to process business rules, converting feature items into factual data, executing rule reasoning to obtain matching results, extracting rule output variables to generate label attributes, assembling label data structures, and outputting business label sets; optionally, using a decision tree model to make rule decisions, mapping feature items to decision tree nodes, traversing decision paths to obtain classification results, extracting label information from leaf nodes, merging label attributes, and generating complete business labels. It is understandable that other methods can also be used to achieve rule matching and label generation, such as fuzzy reasoning, neural networks, and other technical solutions, which are not limited here.

[0051] S106 , selecting a corresponding business tag combination from the archive business tags according to the professional role type of the user, and reorganizing the initial search results according to a preset display template to obtain an adaptive search result.

[0052] Among them, professional role type refers to the user's business position or professional function classification; business tag combination represents the tag subset filtered according to the user role; preset display template is used to represent the layout and display rules of search results; reorganization refers to the process of reorganizing and formatting search results according to the template; adaptive search results represent the final search results that have been personalized.

[0053] This step is performed after the archive business tags are generated, and is used to realize the personalized display of the search results. Specifically, the system first obtains the professional role information of the current user, and filters the relevant tags from the archive business tags according to the business focus of the role. Then, the display template corresponding to the user role is loaded to determine the display structure and format requirements of the search results. Finally, the initial search results are reorganized according to the template rules, and the content layout and display order are adjusted to highlight the business information that the user is concerned about.

[0054] In some embodiments, adaptive display of search results can be achieved in a variety of ways: optionally, configure tag filtering rules based on user roles, execute rule filtering to obtain tag subsets, read role-related display templates, parse template structures, reorganize search data, apply style rendering, and output personalized results; optionally, use template engine technology to process display logic, map user roles to tag selectors, filter matching business tags, load template files, replace template variables, render page content, and generate adaptive results. It is understandable that other methods can also be used to achieve adaptive display of search results, such as dynamic layout, adaptive rendering and other technical solutions, which are not limited here.

[0055] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the archive retrieval method in the embodiment of the present application.

[0056] S201. Respond to a file search request from a user, where the file search request includes a search term for water conservancy engineering.

[0057] Archive retrieval request is a document query operation initiated by the user through the archive retrieval system interface, which includes parameters such as search terms and search scope. Hydraulic engineering professional search terms refer to professional terms related to the field of hydraulic engineering, such as "dam design", "flood gate", "seepage analysis", etc.

[0058] The specific implementation process of this step is: when the user enters the search term in the system search box and clicks the search button, the system captures the search behavior, parses the search term entered by the user, and confirms whether it belongs to the category of water conservancy engineering professional vocabulary. The system encapsulates the search request into a standard format, including search terms, search time, user ID and other information, and passes it to the background search module for processing. For example, if the user enters "flood discharge gate design specifications", the system will identify the search term as a water conservancy engineering professional search term.

[0059] S202: Matching is performed in the archive database based on the water conservancy engineering professional search term to obtain an initial search result, which includes a plurality of engineering technical documents.

[0060] The archive database is a structured data warehouse that stores engineering technical documents, including document content, metadata and other information. The initial search result refers to the original document set matched in the database according to the search term. Engineering technical documents include various types of technical documents such as design drawings, construction plans, and acceptance reports.

[0061] The specific implementation of this step is: the system uses the search terms to perform fuzzy matching in the full-text content and metadata fields of the archive database. The matching algorithm uses an inverted index method to segment the document content and establish a term-document mapping relationship. The system returns all relevant documents containing the search terms as the initial result set. For example, searching for "flood gate design specifications" will return design documents and specification files containing these keywords.

[0062] S203, obtaining a professional terminology dictionary and a business rule template from a water conservancy project corpus, wherein the professional terminology dictionary includes standard terms and business attributes of water conservancy projects, and the business rule template includes determination rules for project stages, extraction rules for technical indicators, and identification rules for construction processes.

[0063] The water conservancy engineering corpus is a knowledge base that contains professional vocabulary, business rules and other knowledge. The professional terminology dictionary stores standardized professional terms and their attribute information. The business rule template defines the rule set for document classification and information extraction.

[0064] The specific implementation of this step is: the system calls the professional terminology dictionary and rule template from the corpus. The terminology dictionary uses a tree structure to store terms and their hierarchical relationships, business attributes and other information. The rule template uses a formal language to define the judgment rules, including rule elements such as keyword features and value ranges. For example, the terminology dictionary stores the type, parameters and other attributes of "flood discharge gate", and the rule template defines how to identify the engineering stage and extract technical indicators from the document.

[0065] S204, performing word segmentation processing on the text content of each engineering technical document in the initial search result, counting the frequency of occurrence of each word in the engineering technical document, and selecting words whose frequency exceeds a preset frequency threshold as candidate keywords for the engineering technical document; Word segmentation refers to the process of dividing continuous text content into individual words, using a word segmentation algorithm based on dictionaries and statistics. The frequency of occurrence refers to the number of times a word appears repeatedly in a document. The preset frequency threshold is a pre-set word frequency judgment standard, such as 0.1% of the total number of words in the document. Candidate keywords refer to a collection of words with a higher frequency.

[0066] The specific implementation process is as follows: First, preprocess the engineering technical documents, including removing punctuation marks, special characters, etc. Then use the word segmentation algorithm to segment the text. For example, the sentence "The flood discharge gate construction adopts reinforced concrete structure" is segmented to obtain "flood discharge gate / construction / adopt / reinforced concrete / structure". Then count the number of occurrences of each word, establish a word frequency statistics table, and record the words and their frequencies. Finally, compare the word frequency with the preset threshold to filter out words with a frequency higher than the threshold. For example, if the total number of words in the document is 1000 and the threshold is set to 0.1%, then words that appear more than once are selected as candidate keywords.

[0067] S205, selecting words that appear in the professional term dictionary from the candidate keywords as business keywords of the engineering technical document; The professional term dictionary is a collection of standard terms in the field of water conservancy engineering, including standardized professional vocabulary. Business keywords are words with professional meanings selected from candidate keywords.

[0068] The specific implementation process is: compare the candidate keywords obtained in step S204 with the professional term dictionary. Use the exact matching method to check whether each candidate keyword exists in the term dictionary. If it exists, the word is marked as a business keyword. For example, the candidate keywords "flood discharge gate" and "reinforced concrete" have corresponding entries in the term dictionary, so they are determined as business keywords, while ordinary words such as "adopt" are filtered out.

[0069] S206, performing string matching on the business keywords of the engineering technical document and the standard terms in the professional term dictionary one by one, and when the matching degree between the business keywords and the standard terms exceeds a preset matching threshold, using the standard terms and the corresponding business attributes as professional feature items of the engineering technical document.

[0070] String matching is the process of calculating the similarity between two words. The matching degree is a quantitative indicator of similarity, ranging from 0 to 1. The preset matching threshold is a standard value for determining the degree of word matching, such as 0.8. Professional feature items include standard terms and their business attribute information.

[0071] The specific implementation process is as follows: for each business keyword, calculate the string similarity with the standard term in the term dictionary. Use the edit distance algorithm to divide the minimum number of operations required to convert two terms by the length of the longer term to obtain a normalized similarity value. When the similarity is greater than the preset threshold, extract the business attributes of the corresponding standard term to form a professional feature item. For example, the similarity between the business keyword "reinforced concrete" and the standard term "reinforced concrete" is 0.85, which exceeds the threshold of 0.8, so the material type, strength and other attributes of "reinforced concrete" are extracted as feature items.

[0072] S207, classifying the professional feature items of the engineering technical document according to business attributes; Business attributes are the classification identifiers of professional feature items, including three categories: engineering stage, technical indicators, and construction technology. Professional feature item classification is the process of classifying and arranging feature items with the same business attributes.

[0073] The specific implementation process is as follows: the system reads the business attribute field of each professional feature item and establishes a mapping table between attributes and feature items. The feature items are grouped according to the business attributes to form the engineering stage feature set, the technical indicator feature set, and the construction process feature set. For example, the business attribute of the feature item "preliminary design" is the engineering stage, so it is classified into the engineering stage feature set; the business attribute of the feature item "concrete strength C30" is the technical indicator, so it is classified into the technical indicator feature set.

[0074] S208, inputting the professional feature items with engineering stage attributes in the engineering technical document into the engineering stage determination rule to determine the engineering stage to which the engineering technical document belongs; The engineering stage is the division of the water conservancy project construction process, including feasibility study, preliminary design, construction drawing design, construction, acceptance, etc. The engineering stage determination rules define the characteristic words and determination methods of different stages.

[0075] The specific implementation process is: match the feature items in the engineering stage feature set with the judgment rules. The judgment rules adopt a decision tree structure, and the nodes contain the feature item combination conditions and the stage judgment results. The system checks whether the feature items meet the rule conditions in turn to determine the stage to which the document belongs. For example, if the document contains the feature items of "preliminary design" and "design manual" and meets the judgment conditions of the preliminary design stage, it is determined that the document belongs to the preliminary design stage.

[0076] S209, inputting the professional feature items with technical indicator attributes in the engineering technical document into the technical indicator extraction rule, and extracting the numerical and descriptive technical indicators of the engineering technical document; Technical indicators include numerical indicators (such as size, strength) and descriptive indicators (such as material type, construction requirements). Technical indicator extraction rules define the identification mode and extraction method of indicators.

[0077] The specific implementation process is: apply extraction rules to the feature items in the technical indicator feature set. Numerical indicators use regular expressions to match numbers and units, such as extracting the value "30" and the unit "MPa" from "concrete strength C30". Descriptive indicators are extracted through keyword positioning and context analysis, such as extracting the material type "reinforced concrete" from "using reinforced concrete structure". The extracted indicators are stored separately in numerical and descriptive types to form a set of technical indicators for the document. For complex professional terms, predefined templates are used for parsing, such as "C30P8" is parsed into strength grade "C30" and impermeability grade "P8".

[0078] S210, inputting the professional feature items with construction process attributes in the engineering technical document into the construction process identification rule to identify the main construction processes and process parameters of the engineering technical document.

[0079] Construction technology refers to the specific construction methods and technical measures used in engineering construction. The main construction technology refers to the core construction method used in the project. The process parameters are the specific technical requirements and control indicators of the construction technology. The construction technology identification rules define the identification method of process characteristics and the parameter extraction method.

[0080] The specific implementation process is: apply the recognition rules to parse the feature items in the construction process feature set. First, identify the main construction processes through keyword matching, such as process action words such as "vibration", "spraying", and "pouring". Then extract process-related parameter information, including process requirements and control values. For example, from "the concrete vibration time is not less than 30 seconds", identify the "vibration" process and time parameter "30 seconds"; from "three-layer segmented pouring, the thickness of each layer is controlled within 50cm", identify the "segmented pouring" process and thickness parameter "50cm". For complex process descriptions, use predefined semantic templates for parsing to extract process flows and parameter combinations.

[0081] S211, combining the engineering phase determined in the engineering technical document, the extracted technical indicators, and the identified construction process to generate a business label for the engineering technical document.

[0082] Business tags are professional attribute identifiers of engineering technical documents, including information in three dimensions: engineering phase, technical indicators, and construction technology. Tag generation is the process of integrating professional information of different dimensions into structured tags.

[0083] The specific implementation process is: organize the label data in JSON format, which contains three main fields: engineering stage, technical indicators and construction technology. The engineering stage field stores the construction stage to which the document belongs; the technical indicator field stores indicator information in numerical and descriptive types; the construction technology field contains the main technology and its parameters. For example, the generated label format is: {"engineering stage": "construction stage", "technical indicators": {"numeric type": ["concrete strength: 30MPa"], "descriptive type": ["material: reinforced concrete"]}, "construction technology": {"technology name": "stage casting", "technology parameters": ["layer thickness: 50cm"]}}.

[0084] S212: Obtain the professional role type of the user, and determine the business tag type corresponding to the professional role type according to the role-tag mapping relationship.

[0085] Professional role type refers to the user's business position in water conservancy projects, such as designer, construction worker, supervisor, etc. The role-label mapping relationship defines the business label types that different roles are concerned about. Business label type is the professional information classification in the business label.

[0086] The specific implementation process is as follows: first, obtain the user's role information from the user management system. Then query the role-label mapping table to obtain the label type that the role is concerned about. The mapping table adopts a matrix structure, with rows representing role types and columns representing label types. The matrix element value 1 indicates concern, and 0 indicates no concern. For example, if the designer focuses on technical indicators, the value of the corresponding technical indicator column is 1; if the construction personnel focus on construction technology, the value of the corresponding construction technology column is 1. Determine the label type combination that needs to be displayed based on the mapping relationship.

[0087] S213: Filter out a business tag subset of the engineering technical document corresponding to the business tag type from the archive business tags.

[0088] A business tag subset is a set of specific tags that are filtered out from the complete business tags. Tag filtering is the process of extracting relevant information from business tags based on user role requirements.

[0089] The specific implementation process is as follows: read the business tag type determined in step S212, and traverse the JSON data structure of the complete business tag. For each tag type, extract the tag information of the corresponding field. For example, if the designer is concerned about technical indicators, the content of the "technical indicators" field is extracted from the business tag; if the designer is also concerned about the engineering stage, the "engineering stage" field is additionally extracted. The extracted tag information maintains the original data structure and hierarchical relationship to form a tag subset for a specific role. The extraction process uses JSON parsing and field filtering operations to ensure the integrity and structure of the data.

[0090] S214: reorganize and typeset the engineering phases, technical indicators, and construction process information in the business tag subset according to a preset display template to obtain a reorganized technical document.

[0091] The preset display template is a template file that defines the layout and display format of label information. Reorganization is the process of reorganizing label information according to the format specified by the template. Reorganized technical documents are a collection of professional information that has been formatted.

[0092] The specific implementation process is: substitute the content of the tag subset into the preset HTML template. The template defines the display position, font style, layout format, etc. of different types of information. The engineering stage information is displayed in the document header, using a striking title style. Technical indicators are displayed in numerical and descriptive categories, and the indicator names and values ​​are presented in a table format. The construction process is in a list format, showing the process name and parameter information. For example, the display template for technical indicators is: Indicator name Index value , substitute the concrete strength of 30MPa and generate the corresponding table row.

[0093] S215: Integrate the basic information of the reorganization technology document and the engineering technology document to obtain an adaptive search result.

[0094] Basic information includes metadata such as document number, name, date, etc. The adaptive search result is a complete document view that integrates the reorganized professional information with the basic information.

[0095] The specific implementation process is as follows: First, obtain the basic information fields of engineering and technical documents from the archive database. Use the predefined document structure template to place the basic information in a fixed position in the document view. Then insert the reorganized professional information content into the corresponding display area. The integrated document structure includes: document title area (displaying document name, number), basic information area (displaying creation date, author, etc.), professional information area (displaying reorganized tag content). The adaptive retrieval results finally generated are in HTML format for easy display on the Web interface. The integration process ensures the integrity of the information and the rationality of the layout, making the retrieval results easy for users to read and use.

[0096] In some embodiments, the following steps may also be included: Statistics are collected for the water conservancy engineering professional search terms with a preset ranking of usage frequencies by the user within the preset usage time.

[0097] In this step, the preset usage time is the time interval of the statistical range, such as the past 30 days. The usage frequency is the number of times the search term is used. The preset value is the selected frequency ranking threshold, such as the top 10. The water conservancy engineering professional search term is the professional term search term that the user has used in the past.

[0098] The specific implementation process is as follows: query the search records of the specified user within the preset time period from the search log database. Extract the search term field for each search record and establish a search term-frequency mapping table. Use a counter to count the number of times each search term appears and sort in descending order of frequency. Select the top N search terms, where N is the preset ranking threshold. For example, count the search terms used by users in the past 30 days: "flood discharge gate design" is used 15 times, "concrete strength" is used 12 times, and select the top 10 search terms in frequency to form a high-frequency search term set.

[0099] The water conservancy engineering professional search terms are classified according to the dimensions of engineering stage, technical indicators and construction technology to form a search term recommendation list for the user.

[0100] In this step, search term classification is the process of classifying high-frequency search terms according to professional attributes. The search term recommendation list is a structured search term collection after classification.

[0101] The specific implementation process is as follows: attribute identification is performed on each search term in the high-frequency search term set. Predefined classification rules are used, including a feature word list for each dimension. Search terms are matched with feature words to determine the dimension to which they belong. For example, search terms containing "design" and "planning" are classified into the engineering stage dimension; search terms containing "strength" and "size" are classified into the technical indicator dimension; search terms containing "construction" and "process" are classified into the construction process dimension. The classification results are stored in a tree structure, with each dimension as a first-level node and the lower-level nodes being the corresponding search term list.

[0102] When the user adds a new archive search request, the water conservancy engineering professional search terms in the recommended list of search terms with the highest matching degree with the current input characters are displayed.

[0103] In this step, the current input characters are the text entered by the user in the search box. The matching degree is the similarity between the input characters and the recommended search terms.

[0104] The specific implementation process is as follows: listen to the user's input event in the search box and obtain the input character sequence. Calculate the string similarity between the input characters and the search terms in the recommendation list. Use the character-level edit distance algorithm to calculate the minimum number of operations required to convert the input characters into search terms. Select the top N search terms with the highest similarity as candidate recommendation terms. Display the candidate recommendation term list below the search box, sorted in descending order by similarity. When the user continues to input, the recommendation list is updated in real time. For example, if the user enters "flood discharge", the system matches "flood discharge gate design", "flood discharge scale" and other related search terms from the recommendation list and displays them to the user.

[0105] Extract the diagram page number information contained in the engineering technical document.

[0106] In this step, the diagram page number information is the page number position of the drawings and tables in the document. The title of the engineering document is the name and basic identification information of the document. Page numbering is the process of displaying the diagram location information in the document title.

[0107] The specific implementation process is as follows: first parse the directory structure and content index of the document, and extract the directory items containing the keywords "Figure" and "Table". Extract the page number value for each directory item to form a figure-page number mapping table. Use regular expression matching pattern to identify the page number information of the figure in the document. Sort the extracted page number information according to the drawings and tables, and generate a formatted page number list, such as "Figure: 3, 5, 7 pages; Table: 10, 12 pages". Add page number annotation after the document title, and use parentheses to include the page number information, such as "Flood Gate Design Manual (Figure: 3, 5, 7 pages; Table: 10, 12 pages)", so that the page number information is integrated with the title, which is convenient for users to quickly understand the location distribution of the figure.

[0108] In the adaptability search results, the diagram page number information is marked in the title of the engineering technical document.

[0109] In this step, the adaptive retrieval result is a reorganized document display view. Annotation is the process of adding information to a specified location in a specific format. In-title annotation refers to the display method of integrating additional information in the document title.

[0110] The specific implementation process is: set the page number information display position in the document title area of ​​the adaptive search results. Use the combination format of "title text + brackets + page number information" for annotation to ensure that the page number information is clearly distinguished from the original title. The specific annotation method of page number information is: add a small bracket after the title text, and list the page numbers in the brackets according to the "Figure:" and "Table:" categories, with commas separating the page numbers and semicolons separating the categories. For example, the title of "Flood Gate Design Manual" is expanded to "Flood Gate Design Manual (Figure: 3, 5, 7 pages; Table: 10, 12 pages)". When annotating, maintain the primary and secondary relationship of the title, so that the page number information is used as a supplementary explanation of the title, and at the same time, highlight the interactive characteristics of the page number through style control such as font size, color, and spacing. In order to improve the recognition of information, the page number is highlighted, and a mouse hover effect is added to prompt that it can be clicked to jump. The entire annotation process must ensure that the clarity of the original title and the integrity of the document structure are not affected.

[0111] When the user clicks on the page number information of the chart, it jumps to the corresponding page number position of the engineering technical document.

[0112] In this step, page number jump is an interactive operation of locating to a specified page of a document after clicking on the page number information. The page number position is a specific page number in the document.

[0113] The specific implementation process is as follows: add a hyperlink tag to the page number information in the title, and the link address contains the page number parameter. Set an anchor tag at the corresponding page position in the document content area to establish the correspondence between the page number and the document position. When the user clicks the page number link, the page positioning event is triggered, and the document content area is scrolled to the corresponding page position. The clickable feature of the page number information is highlighted through style settings to enhance the user interaction experience. Add a smooth scrolling effect during the page jump process to make the positioning process more natural. The entire interactive process does not require users to manually turn pages, which improves the efficiency of document browsing.

[0114] The following is a specific example of the method in this embodiment: Construction worker Xiao Zhang needs to check the concrete construction process parameters of a reservoir floodgate at the construction site. He opens the archive search system and enters the water conservancy engineering professional search term "floodgate concrete construction" in the search box.

[0115] In response to the search request, the system performs a full-text match with the keyword "flood discharge gate concrete construction" in the archive database, and returns 15 engineering technical documents containing relevant content as the initial search results, including design specifications, construction organization designs, construction plans and other types of documents.

[0116] The system calls professional resources from the water conservancy engineering corpus. The professional terminology dictionary includes standard terms such as "flood discharge gate", "concrete strength", "vibration", and "maintenance", as well as their business attributes such as engineering parts, material properties, and construction processes. The business rule template includes: rules for determining the engineering stage through keywords such as "design", "construction", and "acceptance" in the document; rules for extracting technical indicators through formats such as "strength grade C30" and "impermeability grade P8"; rules for identifying construction processes through parameters such as "vibration time" and "maintenance cycle".

[0117] Each document in the initial search results is segmented and the word frequency is counted. Taking one of the documents, "Concrete Construction Plan for Flood Discharge Gate", as an example, the system identifies high-frequency words including "concrete" which appears 95 times, "vibration" which appears 42 times, and "maintenance" which appears 38 times. These high-frequency words are matched with the professional terminology dictionary to determine that "concrete" belongs to the material terminology, "vibration" and "maintenance" belong to the construction process terminology, forming the professional feature items of the document. For a total of 5,000 words in this document, the system sets the word frequency threshold to 0.1% of the total number of words, that is, words that appear more than 5 times are selected as candidate keywords. When these candidate keywords are matched with the professional terminology dictionary, those with a matching degree of more than 0.8 are determined as standard terms, such as the matching degree of "vibration" and "vibration vibration" is 0.85, so the standard term "vibration" and its construction process attributes are selected.

[0118] The professional feature items are classified according to the three dimensions of engineering stage, technical indicators, and construction technology, and then substituted into the business rule template for matching. The engineering stage rule identifies feature words such as "construction plan" and "construction steps" to determine that the document belongs to the construction stage; the technical indicator rule extracts numerical indicators such as "concrete strength grade C30" and "impermeability grade P8"; the construction technology rule identifies specific process parameters such as "vibration using a high-frequency vibrator, vibration time 30-40 seconds", "film covering and curing, curing time not less than 14 days", and generates the archive business label of the document accordingly.

[0119] The system identifies Xiao Zhang's role as a construction worker and determines the construction process-related content that needs to be displayed based on the role-label mapping relationship. Construction process parameters such as vibration method, time requirements, maintenance method, and maintenance cycle are selected from the archive business tags. The system loads a display template dedicated to construction personnel and displays these process parameters in a prominent position on the page in a table format, including columns such as process name, parameter value, and control requirements. After integrating with the basic information of the document, it generates an adaptive search result and presents it to Xiao Zhang.

[0120] At the same time, the system analysis found that Xiao Zhang's frequently used search terms in the past 30 days include "concrete vibration" and "concrete maintenance", and sorted these terms into a recommendation list according to the construction process dimension. When he enters "mix", the system will prompt these high-frequency search terms for quick selection.

[0121] The system also marks the page number of the document containing the construction process diagram in the search results, such as "(Figure: Page 15, 16)". The page number supports click operation, and Xiao Zhang can jump directly to view the specific process diagrams such as vibration and maintenance.

[0122] Through this intelligent retrieval and display method, Xiao Zhang can quickly and accurately obtain the concrete construction process parameters required for the construction site, which fully demonstrates the practical application value of this application in improving the efficiency of water conservancy project archive management.

[0123] The following describes the archive retrieval system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of the archive retrieval system in an embodiment of the present application.

[0124] It should be noted that Figure 3 The structure of the archive retrieval system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0125] like Figure 3As shown, the archive retrieval system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0126] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0127] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0128] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0130] Specifically, the archive retrieval system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the archive retrieval method provided in the above embodiment is implemented.

[0131] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the archive retrieval system described in the above embodiment; or may exist independently without being assembled into the archive retrieval system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the archive retrieval system, the archive retrieval system implements the archive retrieval method provided in the above embodiment.

[0132] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0133] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0134] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A file retrieval method, characterized in that: Applied to an archive retrieval system, the method comprises: Responding to a user's file search request, wherein the file search request includes a search term for water conservancy engineering; Based on the water conservancy engineering professional search terms, matching is performed in the archive database to obtain initial search results, wherein the initial search results include multiple engineering technology documents; Obtaining a professional terminology dictionary and a business rule template from a water conservancy project corpus, wherein the professional terminology dictionary includes standard terms and business attributes of water conservancy projects, and the business rule template includes a determination rule for a project stage, an extraction rule for technical indicators, and an identification rule for a construction process; Using the professional term dictionary to perform word frequency statistics and keyword extraction on the text content in the initial search results, matching the obtained business keywords with the standard terms in the professional term dictionary to obtain the professional feature items of the engineering technical document; Substituting the professional feature item into the business rule template for rule matching, determining the engineering phase, technical indicators and construction process of the engineering technical document based on the matching result of the rule matching, and generating an archive business tag; A corresponding business tag combination is selected from the archive business tags according to the professional role type of the user, and the initial search results are reorganized according to a preset display template to obtain an adaptive search result.

2. The method according to claim 1, characterized in that The step of using the professional term dictionary to perform word frequency statistics and keyword extraction on the text content in the initial search results, matching the obtained business keywords with the standard terms in the professional term dictionary, and obtaining the professional feature items of the engineering technical document specifically includes: Performing word segmentation processing on the text content of each engineering technical document in the initial search results, counting the frequency of occurrence of each word in the engineering technical document, and selecting words whose frequency exceeds a preset frequency threshold as candidate keywords for the engineering technical document; Screening out words that appear in the professional term dictionary from the candidate keywords as business keywords of the engineering technical document; The business keywords of the engineering technical document are string matched with the standard terms in the professional terminology dictionary one by one. When the matching degree between the business keywords and the standard terms exceeds a preset matching threshold, the standard terms and corresponding business attributes are used as professional feature items of the engineering technical document.

3. The method according to claim 1, characterized in that Substituting the professional feature item into the business rule template for rule matching, determining the engineering phase, technical indicators and construction process of the engineering technical document based on the matching result of the rule matching, and generating an archive business tag specifically includes: Classifying the professional feature items of the engineering technical documents according to business attributes; Inputting the professional feature items with engineering stage attributes in the engineering technical document into the engineering stage determination rule to determine the engineering stage to which the engineering technical document belongs; Inputting the professional feature items with technical indicator attributes in the engineering technical document into the technical indicator extraction rule to extract the numerical and descriptive technical indicators of the engineering technical document; Inputting the professional feature items with construction process attributes in the engineering technical document into the construction process identification rule to identify the main construction processes and process parameters of the engineering technical document; The engineering phase determined in the engineering technical document, the extracted technical indicators and the identified construction process are combined to generate a business label for the engineering technical document.

4. The method according to claim 1, characterized in that The step of selecting a corresponding business tag combination from the archive business tags according to the professional role type of the user, and reorganizing the initial search results according to a preset display template to obtain an adaptive search result specifically includes: Acquire the professional role type of the user, and determine the business tag type corresponding to the professional role type according to the role-tag mapping relationship; Filtering out a subset of business tags of the engineering technical documents corresponding to the business tag type from the archive business tags; Reorganize and typeset the engineering phases, technical indicators, and construction process information in the business tag subset according to a preset display template to obtain a reorganized technical document; The basic information of the recombinant technology document and the engineering technology document are integrated to obtain an adaptive search result.

5. The method according to claim 1, characterized in that After the step of selecting a corresponding business tag combination from the archive business tags according to the professional role type of the user, and reorganizing the search results according to a preset display template to obtain an adaptive search result, the method further includes: Count the water conservancy engineering professional search terms with the highest frequency of use by the user within the preset usage time and the highest preset value; Classifying the water conservancy engineering professional search terms according to the dimensions of engineering stage, technical indicators and construction technology to form a search term recommendation list for the user; When the user adds a new archive search request, the water conservancy engineering professional search terms in the recommended list of search terms with the highest matching degree with the current input characters are displayed.

6. The method according to claim 4, characterized in that After the step of selecting a corresponding business tag combination from the archive business tags according to the professional role type of the user, and reorganizing the search results according to a preset display template to obtain an adaptive search result, the method further includes: Extracting the diagram page number information contained in the engineering technical document; In the adaptability search result, the diagram page number information is marked in the title of the engineering technical document.

7. The method according to claim 6, characterized in that After the step of marking the diagram page number information in the document title in the adaptability search result, the method further includes: When the user clicks on the page number information of the chart, it jumps to the page number position corresponding to the engineering technical document.

8. An archive retrieval system, characterized in that: The archive retrieval system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the archive retrieval system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an archive retrieval system, the archive retrieval system is caused to execute the method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on an archive retrieval system, the archive retrieval system is caused to execute the method according to any one of claims 1 to 7.

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