Engineering construction file diagnosis and evaluation method, device and equipment

Abstract

This application discloses a method, apparatus, and equipment for diagnosing and evaluating engineering construction documents, relating to the technical fields of engineering construction and artificial intelligence. The method includes: acquiring the engineering construction documents to be diagnosed and engineering construction knowledge data; performing deep analysis and multimodal information extraction processing on the engineering construction documents to be diagnosed to obtain target data; performing semantic understanding processing on the target data and engineering construction knowledge data to perform multi-dimensional diagnostic processing on the target data, obtaining multi-dimensional diagnostic results; and performing risk quantification assessment on the multi-dimensional diagnostic results according to a preset risk assessment model, obtaining a multi-dimensional risk assessment report.

Description

Technical Field

[0001] This application relates to the technical fields of engineering construction and artificial intelligence, and in particular to a method, apparatus and equipment for diagnosing and evaluating engineering construction documents. Background Technology

[0002] In the real estate development process, engineering construction bidding is a crucial link in cost control and project quality management. A high-quality bidding document is fundamental to ensuring the smooth progress of the bidding process, attracting excellent partners, and avoiding legal disputes. Therefore, how to diagnose and evaluate engineering construction documents has become a key research focus.

[0003] The relevant technical standard operating procedures are usually drafted by the person in charge of bidding based on standard templates or historical project documents, and then submitted to relevant personnel from multiple departments such as legal, cost, and engineering for review. This process relies heavily on people's professional ability and sense of responsibility, and has low efficiency and accuracy. Another related technology uses the spell check and document comparison tools built into office software to identify some typos and basic grammatical errors in documents, as well as highlight differences in text content. However, this method cannot provide specific and actionable optimization suggestions. Summary of the Invention

[0004] The embodiments of this application aim to at least partially solve one of the technical problems in the related art. Therefore, the purpose of the embodiments of this application is to provide a method, apparatus, equipment, and medium for diagnosing and evaluating engineering construction documents, thereby improving the accuracy and efficiency of document diagnosis.

[0005] This application provides a method for diagnosing and evaluating engineering construction documents, including: acquiring engineering construction documents to be diagnosed and engineering construction knowledge data; performing in-depth analysis and multimodal information extraction processing on the engineering construction documents to be diagnosed to obtain target data; performing semantic understanding processing on the target data and engineering construction knowledge data to perform multidimensional diagnostic processing on the target data to obtain multidimensional diagnostic results; and performing risk quantification assessment on the multidimensional diagnostic results according to a preset risk assessment model to obtain a multidimensional risk assessment report.

[0006] For example, the engineering construction document diagnosis and assessment method further includes: optimizing the multi-dimensional risk assessment report based on engineering construction knowledge data to generate optimized data; and optimizing the engineering construction document to be diagnosed based on the optimized data in response to receiving a confirmation instruction.

[0007] For example, the engineering construction document to be diagnosed includes at least one of text data, tabular data, and image data; deep analysis and multimodal information extraction processing are performed on the engineering construction document to be diagnosed to obtain target data, including: performing text extraction processing on the text data, tabular data, and image data to obtain target text data; performing style extraction processing on the text data to obtain style data; performing relation extraction processing on the tabular data to obtain relation data; performing key element extraction processing on the image data to obtain key element data; and obtaining target data based on the target text data, style data, relation data, and key element data.

[0008] For example, multi-dimensional diagnosis includes compliance diagnosis, fairness diagnosis, reasonableness diagnosis, and consistency diagnosis. By performing semantic understanding processing on the target data and engineering construction knowledge data, multi-dimensional diagnostic processing is performed on the target data to obtain multi-dimensional diagnostic results, including: performing compliance diagnosis processing on the target text data and style data based on engineering construction knowledge data to obtain compliance diagnosis results; performing fairness diagnosis processing on the target text data based on engineering construction knowledge data to obtain fairness diagnosis results; performing reasonableness diagnosis processing on the engineering construction knowledge data and target text data to obtain reasonableness diagnosis results; performing consistency diagnosis processing on the target text data, relational data, and key element data to obtain consistency diagnosis results; and obtaining multi-dimensional diagnostic results based on the compliance diagnosis results, fairness diagnosis results, reasonableness diagnosis results, and consistency diagnosis results. For example, the multi-dimensional diagnostic results include risk points, which include at least one of compliance risk points, fairness risk points, reasonableness risk points, and consistency risk points. Based on a pre-set risk assessment model, the multi-dimensional diagnostic results are quantitatively assessed to obtain a multi-dimensional risk assessment report, including: quantitatively assessing the risk points based on the pre-set risk assessment model to obtain risk scores corresponding to the risk points; weighting the risk scores to obtain a comprehensive score; mapping the risk scores to pre-set mapping data to obtain the risk level corresponding to the risk scores; and obtaining the multi-dimensional risk assessment report based on the comprehensive score, risk level, risk points, and the location information corresponding to the risk points. For example, the engineering construction document diagnosis and evaluation method further includes: obtaining the associated documents corresponding to the engineering construction documents to be diagnosed; and verifying the associated documents and the engineering construction documents to be diagnosed. For example, a multi-dimensional risk assessment report includes risk points; the diagnostic assessment method for engineering construction documents also includes: highlighting risk points in the multi-dimensional risk assessment report.

[0009] For example, the engineering construction document diagnostic and evaluation method further includes: integrating the engineering construction document diagnostic and evaluation method into the document editor as a plug-in, so as to facilitate the diagnostic and evaluation of the engineering construction documents during the process of writing engineering construction documents through the document editor.

[0010] Another embodiment of this application provides an engineering construction document diagnosis and evaluation device, which includes: an acquisition module for acquiring engineering construction documents to be diagnosed and engineering construction knowledge data; an extraction module for performing deep analysis and multimodal information extraction processing on the engineering construction documents to be diagnosed to obtain target data; a diagnosis module for performing semantic understanding processing on the target data and engineering construction knowledge data to perform multidimensional diagnosis processing on the target data to obtain multidimensional diagnosis results; and an evaluation module for performing risk quantification assessment on the multidimensional diagnosis results according to a preset risk assessment model to obtain a multidimensional risk assessment report. Another embodiment of this application provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0011] Another embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0012] The above implementation method for diagnosing and evaluating engineering construction documents includes: acquiring the engineering construction documents to be diagnosed and engineering construction knowledge data; performing in-depth analysis and multimodal information extraction processing on the engineering construction documents to be diagnosed to obtain target data; performing semantic understanding processing on the target data and engineering construction knowledge data to perform multi-dimensional diagnostic processing on the target data to obtain multi-dimensional diagnostic results; and performing risk quantification assessment on the multi-dimensional diagnostic results according to a preset risk assessment model to obtain a multi-dimensional risk assessment report. Through multimodal information extraction and deep semantic understanding, automated and intelligent diagnosis and risk assessment of engineering construction documents are achieved. This enables accurate identification of compliance defects, fairness risks, unreasonable technical parameters, and inconsistencies in the documents, and generates quantitative and visualized risk assessment reports. This significantly improves the efficiency and accuracy of document review, reduces legal and engineering risks caused by document defects, and provides reliable data support for bidding management and engineering decision-making. Attached Figure Description

[0013] Figure 1 A flowchart of the engineering construction document diagnosis and evaluation method provided for the implementation of this application; Figure 2 Flowchart of another engineering construction document diagnosis and evaluation method provided for the implementation of this application; Figure 3 Block diagram of an engineering construction document diagnostic and evaluation device provided for another embodiment of this application; Figure 4 A block diagram of an electronic device provided for another embodiment of this application. Detailed Implementation

[0014] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0015] In the real estate development process, engineering construction bidding is a crucial link in cost control and project quality management. A high-quality bidding document is fundamental to ensuring the smooth progress of the bidding process, attracting excellent partners, and avoiding legal disputes. Therefore, how to diagnose and evaluate engineering construction documents has become a key research focus.

[0016] The technical problems in the preparation of bidding documents for real estate construction projects (such as land consolidation and pile foundation engineering) mainly include: (1) Low efficiency of manual review and difficulty in discovering potential risks: Engineering bidding documents are complex, highly professional and lengthy. Relying on manual review for multiple rounds is time-consuming and labor-intensive, and it is easy to miss superficial problems such as typos and format errors in the text. More importantly, for unreasonable technical parameters hidden in the clauses, exclusive supplier qualification requirements, "rule-based errors" and "directional errors" that contradict the company's internal compliance strategy or industry regulations, manual review is difficult to discover comprehensively and objectively due to the limitations of personal experience and knowledge, resulting in high potential risks of bid rejection, compliance and integrity. (2) Subjective risk assessment and lack of quantitative standards: For a bidding document, there is currently a lack of objective and quantitative assessment methods for its potential legal risks, business risks and technical risks. Reviewers usually make subjective judgments based on personal experience, resulting in unstable and unreliable assessment results, and managers have difficulty accurately controlling the overall risk level of the bidding documents. (3) Inconsistent quality of optimization suggestions and lack of intelligent assistance: After problems are discovered, how to revise and optimize the clauses depends heavily on the professional level of the person in charge. The existing process lacks intelligent assistance tools and cannot combine the company's historical best practices, successful bidding cases and the latest laws and regulations to provide the person in charge with high-quality and standardized modification suggestions, resulting in inconsistent quality of document optimization. (4) Lack of timely and effective optimization guidance: Even if problems are discovered by manual review, there is a lack of a systematic knowledge base to support reviewers in making high-quality and standardized modification suggestions. The quality of modification opinions is inconsistent, and the feedback loop of "discovering problems - making suggestions" is long.

[0017] The relevant technical standard operating procedures are usually drafted by the person in charge of bidding based on standard templates or historical project documents, and then submitted to relevant personnel from multiple departments such as legal, cost, and engineering for review. This process relies heavily on human professional ability and sense of responsibility, and has low efficiency and accuracy. Another related technology uses spell check and document comparison tools built into office software to identify some typos and basic grammatical errors in the document, and highlights the differences in text content by comparing it with another document. However, this method cannot provide specific and actionable optimization suggestions.

[0018] The fundamental technical limitations of the aforementioned related technologies cannot meet the review requirements of professional bidding documents, mainly reflected in the following aspects: (1) Lack of domain knowledge and deep semantic understanding: Traditional text checking tools lack professional knowledge in the field of engineering construction and cannot understand the specific technical requirements of the "foundation pit support scheme", nor can they judge whether the set "bidder performance requirements" constitute unreasonable exclusivity. They can only perform literal checks and cannot conduct in-depth analysis from the perspective of business logic and compliance. (2) Inability to perform rule verification based on knowledge base: The related technology is an isolated tool and cannot be connected and verified with the company's internal knowledge base (such as: historical bidding case library, supplier library, legal and regulatory library, corporate risk control compliance clause library). Therefore, it cannot find out whether the clauses in the bidding documents violate a new regulation of the company or whether they conflict with the latest industry standards. (3) Remaining at the level of "passive inspection" and unable to achieve "active diagnosis and optimization": The related tools can only passively find some superficial errors, but cannot provide a comprehensive and quantitative diagnostic report on the risk status of the entire document (e.g., "integrity risk: high; compliance risk: medium"). More importantly, it is completely incapable of intelligently generating specific, actionable optimization suggestions based on the discovered problems, combined with context and knowledge base.

[0019] In view of this, the embodiments of this application provide a diagnostic and evaluation method for engineering construction documents, which performs semantic understanding of multimodal data based on AI large model and simulates the intelligent diagnosis and optimization of domain experts through in-depth analysis, thereby systematically and efficiently improving the quality and compliance of engineering construction bidding documents.

[0020] Figure 1 A flowchart illustrating the engineering construction document diagnostic and evaluation method provided for the implementation of this application.

[0021] like Figure 1 As shown, the engineering construction document diagnosis and evaluation method 100 provided in this application includes, for example, steps S110-S140.

[0022] Step S110: Obtain the construction documents and construction knowledge data of the project to be diagnosed. For example, the engineering construction documents to be diagnosed include engineering construction bidding documents, which include text data, tabular data, and image data (such as drawings). The engineering construction documents to be diagnosed can be Word files (.docx) or PDF files (.pdf). Engineering construction knowledge data includes laws and regulations, company red line systems, industry general standards, mainstream technical parameter ranges, etc.

[0023] Step S120: Perform in-depth analysis and multimodal information extraction processing on the construction documents of the project to be diagnosed to obtain target data. For example, multimodal information extraction processing includes text extraction processing, style extraction processing, relationship extraction processing, and key element extraction processing. The target data includes target text data (obtained by text extraction processing of text data, table data, and image data), style data (obtained by style extraction processing of text data, such as titles, body text, lists, etc.), relationship data (obtained by relationship extraction processing of table data, such as the correspondence between row and column data), and key element data (obtained by key element extraction processing of image data, such as graphic elements in image data, etc.).

[0024] Step S130: Semantic understanding processing is performed on the target data and engineering construction knowledge data to perform multi-dimensional diagnostic processing on the target data and obtain multi-dimensional diagnostic results. For example, the AI ​​big model first performs semantic understanding processing on the target data and engineering construction knowledge data, and then performs multi-dimensional diagnostic processing on the target data based on the engineering construction knowledge data to obtain multi-dimensional diagnostic results. The multi-dimensional diagnostic processing includes compliance diagnosis, fairness diagnosis, rationality diagnosis and consistency diagnosis. The multi-dimensional diagnostic results include the risk points corresponding to each dimension (compliance risk points, fairness risk points, rationality risk points and consistency risk points).

[0025] Step S140: Based on the preset risk assessment model, perform risk quantification assessment on the multidimensional diagnostic results to obtain a multidimensional risk assessment report. For example, the risk assessment report includes the comprehensive score of the construction documents of the project to be diagnosed, the risk level corresponding to each risk point, the risk point and the location information corresponding to the risk point. The risk quantification assessment includes the risk calculation of the risk points corresponding to each dimension by the risk assessment model to obtain the corresponding risk score, mapping the risk score to preset mapping data (such as the correspondence between risk score range and risk level) to obtain the risk level, and weighting the risk scores of multiple risk points to obtain the comprehensive score.

[0026] In the above embodiments, through multimodal information extraction and deep semantic understanding, automated and intelligent diagnosis and risk assessment of engineering construction documents are realized. It can accurately identify compliance defects, fairness risks, unreasonable technical parameters and inconsistent content in the documents, and generate quantitative and visualized risk assessment reports. This significantly improves the efficiency and accuracy of document review, reduces legal and engineering risks caused by document defects, and provides reliable data support for bidding management and engineering decision-making. In one example, the engineering construction document to be diagnosed includes at least one of text data, tabular data, and image data; deep analysis and multimodal information extraction processing are performed on the engineering construction document to be diagnosed to obtain target data, including: text extraction processing of text data, tabular data, and image data to obtain target text data; style extraction processing of text data to obtain style data; relation extraction processing of tabular data to obtain relation data; key element extraction processing of image data to obtain key element data; and obtaining target data based on target text data, style data, relation data, and key element data.

[0027] Specifically, text data (e.g., text) and image data (key elements such as graphics) are unstructured data, while tabular data (e.g., correspondences) and text data (e.g., styles) are semi-structured data. Text data is parsed to extract all text (target text data) and identify its styles (style data), such as titles, body text, lists, etc. Tabular data is parsed to identify tables in the document (e.g., "pre-qualification scoring sheet," "materials list") and extract their row and column data (target text data) and correspondences (relationship data). Image data is parsed; when the document contains drawings (e.g., "site leveling diagram"), computer vision (CV) technology is used to identify key elements (key element data) and text (target text data) in the drawings for consistency verification with the body text (target text data). The target text data, style data, relationship data, and key element data constitute the resulting structured data.

[0028] In the above embodiments, by collaboratively extracting and deeply analyzing multimodal information such as text, tables, and images, comprehensive and accurate structured processing of engineering construction documents is achieved. This involves comprehensively extracting text content, style features, table relationships, and key image elements to generate a unified, machine-understandable target data object, effectively overcoming the limitations of traditional document parsing methods when processing semi-structured and unstructured information. This technology provides a complete, accurate, and semantically relevant data foundation for subsequent intelligent diagnosis and risk assessment, significantly improving the completeness and reliability of automated processing of engineering construction documents, and providing key technical support for efficient and accurate intelligent document analysis.

[0029] In one example, multi-dimensional diagnostics include compliance diagnostics, fairness diagnostics, reasonableness diagnostics, and consistency diagnostics. By performing semantic understanding processing on the target data and engineering construction knowledge data, multi-dimensional diagnostic processing is performed on the target data to obtain multi-dimensional diagnostic results. These include: performing compliance diagnostic processing on the target text data and style data based on engineering construction knowledge data to obtain compliance diagnostic results; performing fairness diagnostic processing on the target text data based on engineering construction knowledge data to obtain fairness diagnostic results; performing reasonableness diagnostic processing on the engineering construction knowledge data and target text data to obtain reasonableness diagnostic results; performing consistency diagnostic processing on the target text data, relational data, and key element data to obtain consistency diagnostic results; and obtaining multi-dimensional diagnostic results based on the compliance diagnostic results, fairness diagnostic results, reasonableness diagnostic results, and consistency diagnostic results. Specifically, the parsed structured information (target data) is fed into the AI ​​intelligent diagnostic engine. This engine leverages the semantic understanding capabilities of a large AI model and interacts in real-time with an enterprise-level bidding knowledge base (engineering construction knowledge data), performing parallel diagnostics across four dimensions: Text Compliance Diagnosis Unit (Compliance Diagnosis Processing): This unit checks for basic issues in the document, such as typos (target text data), punctuation errors, and inconsistent formatting (style data). More importantly, it compares the document clauses (target text data) with laws, regulations, and company red lines in Knowledge Base H (engineering construction knowledge data), identifying and highlighting descriptions that are non-compliant or pose legal risks. Bidding Rule Fairness Diagnosis Unit (Fairness Diagnosis Processing): This unit focuses on analyzing sections such as "Bidder Qualification Requirements" and "Scoring Criteria" (target text data), combining historical success and failure cases from Knowledge Base I (engineering construction knowledge data) (especially cases leading to bid rejection or complaints), and uses a large model to determine whether there are "biased clauses" that favor specific suppliers, assessing integrity risks. Technical Parameter Rationality Diagnosis Unit (Rationality Diagnosis Processing): For specific technical requirement chapters (target text data) such as "Land Consolidation" and "Pile Foundation Engineering," the technical parameters and process standards set in the document are compared with industry-standard and mainstream technical parameter ranges in Knowledge Base J (Engineering Construction Knowledge Data) to identify parameters that are too advanced, outdated, or unreasonable. Completeness and Consistency Diagnosis Unit (Consistency Diagnosis Processing): Checks whether the document (target text data) is missing necessary chapters (such as "Main Contract Terms," ​​"Confidentiality Agreement," etc.). Simultaneously, cross-validation is performed within the document (relationship data, key element data) to check whether the descriptions of key information (such as project name, construction period, amount, etc.) are consistent across different chapters.

[0030] In the above embodiments, by integrating a multi-dimensional diagnostic engine with semantic understanding technology, automated and refined intelligent diagnosis of engineering construction bidding documents is achieved. Based on semantic understanding, it performs deep correlation analysis between target data and a professional knowledge base, conducting specialized diagnoses from four dimensions: compliance, fairness, rationality, and consistency. This comprehensively identifies various risks and problems in text content, style structure, data relationships, and drawing elements. This technology effectively improves the coverage depth and diagnostic accuracy of document review, providing systematic and structured evaluation support for the completeness, compliance, and technical rationality of bidding documents. It significantly reduces the omissions and subjective biases of manual review, enhancing the professionalism and intelligence of engineering bidding management.

[0031] In one example, the multi-dimensional diagnostic results include risk points, which include at least one of compliance risk points, fairness risk points, reasonableness risk points, and consistency risk points. Based on a pre-defined risk assessment model, the multi-dimensional diagnostic results are quantitatively assessed to obtain a multi-dimensional risk assessment report, including: quantitatively assessing the risk points based on the pre-defined risk assessment model to obtain risk scores corresponding to the risk points; weighting the risk scores to obtain a comprehensive score; mapping the risk scores to pre-defined mapping data to obtain the risk level corresponding to the risk scores; and obtaining the multi-dimensional risk assessment report based on the comprehensive score, risk level, risk points, and the location information corresponding to the risk points. Specifically, multiple risk points obtained from multi-dimensional diagnosis (such as compliance risk points, fairness risk points, reasonableness risk points, and consistency risk points) are input into a preset risk assessment model. This model quantifies the risk for each risk point, obtains a risk score for each risk point, and weights multiple risk scores according to preset weights to obtain a comprehensive score for the construction documents to be diagnosed. The preset mapping data includes the risk level corresponding to the risk score range. For example, a risk score of 70-80 is considered a medium level. For each risk point, the corresponding risk level is obtained based on the risk score.

[0032] For example, the diagnostic engine feeds all diagnostic results (multidimensional diagnostic results) into a preset risk assessment model. The risk assessment model assigns a weighted score to each problem point (risk point) and finally generates a multidimensional risk assessment report containing the following: overall quality score (overall score) (e.g., 85 / 100); a risk radar chart (showing the risk level from four dimensions: compliance, fairness, reasonableness, and consistency); and a detailed list of problems, listing each identified problem point (risk point), its location, and its risk level.

[0033] In the above embodiments, by establishing a refined risk quantification assessment system, an objective, accurate, and systematic evaluation of the risks in engineering construction bidding documents is achieved. Based on a preset model, it automatically scores and weights multiple risk points such as compliance, fairness, reasonableness, and consistency, transforming unstructured diagnostic conclusions into quantified risk scores and levels, and accurately linking them to the original document text. This generates a comprehensive risk assessment report with clear risk indications, quantitative basis, and positioning guidance. This technology significantly improves the comprehensiveness of risk identification and the scientific nature of assessment results, providing an efficient and reliable decision support tool for compliance review and risk management of bidding documents, and effectively reducing legal, commercial, and engineering risks caused by document defects.

[0034] In one example, the multi-dimensional risk assessment report includes risk points; the engineering construction document diagnostic assessment method also includes: highlighting the risk points in the multi-dimensional risk assessment report.

[0035] Specifically, strong prompts include highlighting prompts, such as highlighting the location information corresponding to each risk point in a multi-dimensional risk assessment report. Users can directly click on the highlighted question area in the document preview to view the corresponding risk description (risk level, comprehensive score, etc.).

[0036] In one example, the engineering construction document diagnostic assessment method further includes: optimizing a multi-dimensional risk assessment report based on engineering construction knowledge data to generate optimized data; and optimizing the engineering construction document to be diagnosed based on the optimized data in response to receiving a confirmation instruction.

[0037] Specifically, for each problem point (risk point) in the problem list (risk assessment report), the AI ​​big model is driven and combined with context and knowledge base content (engineering construction knowledge data) to generate specific and actionable optimization suggestions (optimization data). The final diagnostic report (risk assessment report) and optimization suggestions are presented to the user through an interactive web interface. The user can clearly see the comprehensive score and risk distribution (risk points and risk point location information) of the document (engineering construction document to be diagnosed), and can directly click on the highlighted problem area in the document preview to view the corresponding risk description (risk level, comprehensive score, risk score) and modification suggestions (optimization data), and make one-click adoption or manual modification.

[0038] For example, regarding compliance risks: Issue: "Article 5.2, 'Bid security submitted after the deadline will not be refunded,' is inconsistent with the 'Regulations for the Implementation of the Bidding Law.'" Recommendation: "It is recommended to amend it to 'If the bidder fails to submit the bid security as required, the bid will be invalid. For bid security submitted after the deadline, the tendering party will refund it in accordance with regulations.'" Regarding fairness risks: Issue: "The qualification requirement 'must have more than 5 cooperation experiences with XX Group' has been determined to have a high degree of risk." Recommendation: "To broaden the range of suppliers and mitigate risks, it is recommended to amend it to 'In the past 5 years, the bidder should have completed at least 3 land consolidation projects of similar scale (e.g., earthwork exceeding XX million cubic meters) and provide contracts and acceptance certificates.'" In one example, the engineering construction document diagnostic assessment method further includes: obtaining the associated files corresponding to the engineering construction document to be diagnosed; and verifying the associated files and the engineering construction document to be diagnosed. Specifically, diagnostic capabilities are extended to a series of documents related to project bidding. Multiple related documents, such as the project's "Technical Standards," "Commercial Contract," and "Tender Announcement," can be read simultaneously for cross-document consistency checks, ensuring key information remains consistent across all documents and avoiding potential contract disputes.

[0039] In one example, the engineering construction document diagnostic evaluation method also includes: integrating the engineering construction document diagnostic evaluation method as a plugin into the document editor, so as to facilitate the diagnostic evaluation of the engineering construction documents during the process of writing engineering construction documents through the document editor.

[0040] Specifically, the diagnostic engine is integrated into commonly used enterprise document editors (such as Microsoft Word and WPS) as an embedded plugin. During the drafting of tender documents, the plugin can perform real-time analysis, dynamically displaying risk points and optimization suggestions in the sidebar, thus shifting from "post-event review" to "in-process assistance."

[0041] In another example, a risk simulation from the bidder's perspective is introduced, adding a "bidder's perspective" simulation diagnostic module. The AI ​​model will simulate a potential bidder, review the bidding documents, and predict clauses that may raise questions, be unclear, or potentially objectionable from the bidder's point of view. This helps the bidding party further refine the documents before publication, reducing communication costs and disputes during the bidding process.

[0042] Figure 2 Another flowchart of an engineering construction document diagnosis and evaluation method provided for the implementation of this application is shown below. Figure 2 As shown, the diagnostic and evaluation methods for engineering construction documents include S201-S208.

[0043] S201, Users upload engineering construction bidding documents (.docx / .pdf).

[0044] For example, users can upload engineering construction tender documents to be diagnosed (engineering construction documents to be diagnosed) through the front end. The engineering construction documents to be diagnosed include text data, tabular data and image data (such as drawings), etc. The engineering construction documents to be diagnosed can be Word files (.docx) or PDF files (.pdf). Engineering construction knowledge data includes laws and regulations, company red line system, industry general standards, mainstream technical parameter ranges, etc.

[0045] S202, Document Deep Analysis and Multimodal Information Extraction Module.

[0046] For example, the document parsing module first starts, extracting text and layout (text data), tables (table data), and image content (image data). It also uses multimodal capabilities to parse the semi-structured and unstructured information of the document: parsing the text data, extracting all text (target text data), and identifying its style (style data), such as titles, body text, lists, etc.; parsing the table data: identifying tables in the document (such as "prequalification scoring sheet" and "materials list"), and extracting their row and column data (target text data) and corresponding relationships (relationship data); parsing the image data, when the document contains drawings (such as "site leveling diagram"), using computer vision (CV) technology to identify key elements (key element data) and text (target text data) in the drawing.

[0047] S203, AI intelligent diagnostic engine.

[0048] For example, the parsed structured information (target data) is fed into the AI ​​intelligent diagnostic engine. This engine is based on the semantic understanding capabilities of the AI ​​large model and interacts in real time with the enterprise-level bidding knowledge base (engineering construction knowledge data). It performs diagnosis in parallel from four dimensions: text compliance diagnosis unit (compliance diagnosis processing), bidding rule fairness diagnosis unit (fairness diagnosis processing), technical parameter rationality diagnosis unit (rationality diagnosis processing), and rationality, completeness and consistency diagnosis unit (consistency diagnosis processing).

[0049] S204, Enterprise-level Bidding Knowledge Base (Engineering Construction Knowledge Data).

[0050] For example, engineering construction knowledge data includes legal and regulatory databases and company policy databases, historical case studies of successes and failures, industry technical standards and cost databases, etc.

[0051] S205, Risk Quantification Assessment and Report Generation Module.

[0052] S206, Intelligent Optimization Suggestion Generation Module.

[0053] S207, front-end visual report presentation.

[0054] For example, the document (construction documents to be diagnosed) is presented to the user through an interactive web interface. The user can clearly see the overall score and risk distribution (risk points and risk point location information) of the document, and can directly click on the highlighted problem areas in the document preview.

[0055] S208, users can view the report and adopt suggestions for optimization.

[0056] For example, users can view the corresponding risk description (risk level, comprehensive score, risk score) and modification suggestions (data optimization), and make one-click adoption or manual modification.

[0057] The diagnostic and evaluation method for engineering construction documents proposed in this application achieves the following: (1) An AI diagnostic model for the field of engineering bidding: A new multi-dimensional and in-depth AI diagnostic model specifically for engineering construction bidding documents is proposed. It surpasses traditional text correction and for the first time incorporates professional dimensions such as compliance, fairness (integrity risk), and technical rationality into the automated evaluation system, realizing a comprehensive quantitative diagnosis of the quality of bidding documents. (2) Deep integration of AI big model and enterprise domain knowledge base: The powerful generalized semantic understanding capability of AI big model is deeply integrated with a professional knowledge base containing enterprise systems, laws and regulations, historical cases, and industry standards. This makes the diagnosis no longer "theoretical" but a precise and reliable risk identification that closely fits the actual business scenario of the enterprise. (3) Realization of an intelligent closed loop of "diagnosis-evaluation-optimization": It can not only accurately "discover problems" but also objectively "evaluate risks" and intelligently "provide solutions". It provides bidding document drafters with a complete intelligent solution from risk warning to decision support, transforming expert experience into reusable AI capabilities, significantly improving work efficiency and compliance level.

[0058] Figure 3 A block diagram of an engineering construction document diagnostic and evaluation device provided for another embodiment of this application.

[0059] This specification provides an engineering construction document diagnostic and evaluation device 300. Please refer to [link / reference]. Figure 3 The engineering construction document diagnosis and evaluation device 300 includes: an acquisition module 310, an extraction module 320, a diagnosis module 330, and an evaluation module 340.

[0060] The acquisition module 310 is used to acquire the construction documents and construction knowledge data of the project to be diagnosed.

[0061] The extraction module 320 is used to perform in-depth analysis and multimodal information extraction processing on the construction documents of the project to be diagnosed, so as to obtain the target data.

[0062] The diagnostic module 330 is used to perform semantic understanding processing on the target data and engineering construction knowledge data to perform multi-dimensional diagnostic processing on the target data and obtain multi-dimensional diagnostic results.

[0063] The assessment module 340 is used to perform risk quantification assessment on the multidimensional diagnostic results based on the preset risk assessment model, and obtain a multidimensional risk assessment report. For example, the engineering construction document diagnosis and evaluation device 300 further includes: an optimization module, used to optimize the multi-dimensional risk assessment report based on engineering construction knowledge data to generate optimized data; and in response to receiving a confirmation instruction, to optimize the engineering construction document to be diagnosed based on the optimized data.

[0064] For example, the construction documents to be diagnosed include at least one of text data, tabular data, and image data; the extraction module 320 is further configured to perform text extraction processing on the text data, tabular data, and image data to obtain target text data; perform style extraction processing on the text data to obtain style data; perform relation extraction processing on the tabular data to obtain relation data; perform key element extraction processing on the image data to obtain key element data; and obtain target data based on the target text data, style data, relation data, and key element data.

[0065] For example, the multi-dimensional diagnosis includes compliance diagnosis, fairness diagnosis, reasonableness diagnosis, and consistency diagnosis; the diagnosis module 330 is also used to perform compliance diagnosis processing on the target text data and style data based on engineering construction knowledge data to obtain a compliance diagnosis result; perform fairness diagnosis processing on the target text data based on engineering construction knowledge data to obtain a fairness diagnosis result; perform reasonableness diagnosis processing on the engineering construction knowledge data and the target text data to obtain a reasonableness diagnosis result; perform consistency diagnosis processing on the target text data, relation data, and key element data to obtain a consistency diagnosis result; and obtain a multi-dimensional diagnosis result based on the compliance diagnosis result, fairness diagnosis result, reasonableness diagnosis result, and consistency diagnosis result. For example, the multi-dimensional diagnostic results include risk points, which include at least one of compliance risk points, fairness risk points, reasonableness risk points, and consistency risk points; the assessment module 340 is also used to perform risk quantification assessment on the risk points based on a preset risk assessment model to obtain the risk score corresponding to the risk point; perform weighted processing based on the risk score to obtain a comprehensive score; perform mapping processing based on the risk score and preset mapping data to obtain the risk level corresponding to the risk score; and obtain a multi-dimensional risk assessment report based on the comprehensive score, risk level, risk point, and the location information corresponding to the risk point. For example, the engineering construction document diagnosis and evaluation device 300 further includes: a verification module, used to obtain the associated file corresponding to the engineering construction document to be diagnosed; and to verify the associated file and the engineering construction document to be diagnosed. For example, the multi-dimensional risk assessment report includes risk points; the engineering construction document diagnostic assessment device 300 also includes: The alert module is used to highlight risk points in multi-dimensional risk assessment reports.

[0066] For example, the engineering construction document diagnostic and evaluation device 300 further includes an integration module for integrating the engineering construction document diagnostic and evaluation device 300 into a document editor as a plug-in, so as to perform diagnostic and evaluation of the engineering construction documents during the process of writing engineering construction documents through the document editor.

[0067] Figure 4 A block diagram of an electronic device provided for another embodiment of this application.

[0068] Another embodiment of this application provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0069] like Figure 4 As shown, for ease of understanding, embodiments of this application illustrate a specific electronic device 400.

[0070] Electronic device 400 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 400 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0071] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0072] Multiple components in electronic device 400 are connected to input / output (I / O) interface 405. These components include: input unit 406, such as a keyboard or mouse; output unit 407, such as various types of displays or speakers; storage unit 408, such as a hard disk or optical disk; and communication unit 409, such as a network interface card (NIC), modem, or wireless transceiver. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0073] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods described above. For example, in some embodiments, any one or more of the various methods described above can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of any one or more of the various methods described above can be performed. Alternatively, in other embodiments, the computing unit 401 can be configured to perform any one or more of the various methods described above by any other suitable means (e.g., by means of firmware).

[0074] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0075] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this application, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0076] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0077] In the description of this application, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this application, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0078] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0079] Furthermore, the terms "first," "second," etc., used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this application can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this application, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly and specifically defined in the embodiments.

[0080] In this application, unless otherwise explicitly specified or limited in the embodiments, the terms "installation," "connection," "joining," and "fixing" appearing in the embodiments should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral part; it can also be a mechanical connection, an electrical connection, etc. Of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication between two components, or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific implementation.

[0081] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0082] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method of diagnostic assessment of construction documents, characterized by, The method includes: Obtain the construction documents and construction knowledge data of the project to be diagnosed; The project construction documents to be diagnosed are subjected to in-depth analysis and multimodal information extraction processing to obtain target data; By performing semantic understanding processing on the target data and the engineering construction knowledge data, multi-dimensional diagnostic processing is performed on the target data to obtain multi-dimensional diagnostic results; Based on the preset risk assessment model, the multidimensional diagnostic results are quantitatively assessed to obtain a multidimensional risk assessment report.

2. The method of claim 1, wherein, The method further includes: Based on the engineering construction knowledge data, the multi-dimensional risk assessment report is optimized to generate optimized data; In response to receiving a confirmation instruction, the construction documents of the project to be diagnosed are optimized based on the optimization data.

3. The method of claim 1, wherein, The engineering construction documents to be diagnosed include at least one of text data, tabular data, and image data; the deep analysis and multimodal information extraction processing of the engineering construction documents to be diagnosed to obtain target data includes: The text data, the table data, and the image data are subjected to text extraction processing to obtain the target text data; The text data is processed by style extraction to obtain style data; The table data is processed to extract relationships, resulting in relational data. The image data is subjected to key element extraction processing to obtain key element data; The target data is obtained based on the target text data, the style data, the relationship data, and the key element data.

4. The method according to claim 3, characterized in that, The multi-dimensional diagnosis includes compliance diagnosis, fairness diagnosis, rationality diagnosis, and consistency diagnosis; the multi-dimensional diagnosis results are obtained by performing semantic understanding processing on the target data and the engineering construction knowledge data to obtain multi-dimensional diagnostic results, including: Based on the engineering construction knowledge data, the target text data and the style data are subjected to compliance diagnosis processing to obtain compliance diagnosis results; Based on the engineering construction knowledge data, the target text data is subjected to fairness diagnosis processing to obtain the fairness diagnosis result; The engineering construction knowledge data and the target text data are subjected to a rationality diagnosis process to obtain a rationality diagnosis result; A consistency diagnosis process is performed on the target text data, the relation data, and the key element data to obtain a consistency diagnosis result. Based on the compliance diagnosis results, the fairness diagnosis results, the reasonableness diagnosis results, and the consistency diagnosis results, a multidimensional diagnosis result is obtained.

5. The method according to claim 4, characterized in that, The multi-dimensional diagnostic results include risk points, which include at least one of compliance risk points, fairness risk points, reasonableness risk points, and consistency risk points; the multi-dimensional diagnostic results are then quantitatively assessed according to a preset risk assessment model to obtain a multi-dimensional risk assessment report, including: Based on the preset risk assessment model, the risk points are quantitatively assessed to obtain the risk scores corresponding to the risk points. The risk scores are weighted to obtain a comprehensive score. Based on the risk score and preset mapping data, a mapping process is performed to obtain the risk level corresponding to the risk score; Based on the comprehensive score, the risk level, the risk point, and the location information corresponding to the risk point, a multi-dimensional risk assessment report is obtained.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Retrieve the associated files corresponding to the construction documents of the project to be diagnosed; The associated files and the construction files of the project to be diagnosed are verified.

7. The method according to claim 5, characterized in that, The multi-dimensional risk assessment report includes the risk points; the method also includes: The aforementioned risk points are highlighted in the multi-dimensional risk assessment report.

8. The method according to any one of claims 1-5, characterized in that, The method further includes: The aforementioned engineering construction document diagnostic and evaluation method is integrated into the document editor as a plugin, so as to facilitate the diagnostic and evaluation of the engineering construction documents during the process of writing engineering construction documents through the document editor.

9. A diagnostic and evaluation device for engineering construction documents, characterized in that, The device includes: The acquisition module is used to acquire the construction documents and construction knowledge data of the project to be diagnosed. The extraction module is used to perform deep analysis and multimodal information extraction processing on the construction documents of the project to be diagnosed to obtain target data; The diagnostic module is used to perform semantic understanding processing on the target data and the engineering construction knowledge data to perform multi-dimensional diagnostic processing on the target data and obtain multi-dimensional diagnostic results. The assessment module is used to perform risk quantification assessment on the multidimensional diagnostic results according to a preset risk assessment model, and obtain a multidimensional risk assessment report.

10. An electronic device having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-8.

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