Intelligent examination and detection qualification approval method based on multi-modal semantic understanding and block chain evidence storage

Abstract

According to the inspection and detection qualification intelligent approval method based on multi-modal semantic understanding and block chain evidence storage provided by the invention, a four-in-one technical architecture of semantic understanding, credible consensus, intelligent decision making and judicial evidence storage is constructed; through four key steps of dynamic knowledge graph construction, multi-modal material analysis, cross-chain multi-node collaborative approval and whole-process block chain evidence storage, intelligent and reliable inspection and detection qualification approval can be realized.

Description

Technical Field

[0001] This invention belongs to the technical field of e-government, blockchain technology and artificial intelligence. It is specifically applicable to the whole process management of qualification application, review and certification of various inspection and testing institutions (such as environmental testing, food testing, material testing and other fields). In particular, it relates to an intelligent approval method for inspection and testing qualifications based on multimodal semantic understanding and blockchain evidence storage. Background Technology

[0002] Accreditation of testing and inspection institutions is a crucial administrative licensing matter for ensuring the legal validity of testing data and regulating market order. Its core process includes institution application, acceptance and approval by the accreditation department, review by expert panel, public disclosure, and social co-governance. Traditional approval methods primarily rely on offline paper-based document review. However, with the development of information technology, some organizations have begun building online approval systems, mainly to achieve basic functions such as form transfer, document storage, and simple online endorsements.

[0003] The core logic of the existing technology is as follows: the institution submits an application by uploading scanned copies of paper materials or electronic documents through the system. The qualification accreditation acceptance and approval department and review experts conduct manual reviews of the completeness and compliance of the materials in sequence according to the preset process. The review opinions are recorded through the system and fed back to the applicant institution. Finally, the management department summarizes the opinions of each link and approves and issues a certificate for the institution's application.

[0004] The existing technical problems include (1) difficulty in verifying the authenticity of materials: scanned copies of paper materials are easily forged by image editing tools (such as altering seals, signatures, test data, etc.). The existing system lacks effective technical verification methods. The authenticity of seals and signatures mainly depends on the manual visual judgment of the approvers, which is inaccurate and prone to errors. At the same time, there is a lack of logical cross-verification of the content of the materials, making it difficult to discover hidden problems such as "mismatch between equipment list and testing capabilities".

[0005] (2) The review criteria are highly subjective: There are many technical specifications involved in the field of inspection and testing. Different approval experts have different understandings of the specifications and the approval standards of similar institutions are inconsistent, which leads to the frequent occurrence of "different judgments for the same case" and affects the fairness of the approval.

[0006] (3) Low efficiency of cross-departmental collaboration: The entire life cycle of qualification certification involves multiple departments such as windows, competent authorities, and technical experts. The existing system adopts a serial approval mode, and there are information silos between departments, data cannot be shared, and the average approval cycle exceeds 50 working days, which is inefficient.

[0007] (4) Approval decisions are not traceable: The approval process records are stored in a centralized database, which is at risk of being tampered with or deleted. When administrative review or litigation is triggered, it is difficult to provide complete and credible evidence.

[0008] (5) Lack of intelligent assistance: Faced with a massive amount of application materials (a single institution may submit more than a dozen types of application materials, each with different requirements and a wide range of contents), the approval personnel need to devote a lot of energy to formal review, making it difficult to focus on the core assessment of key technical capabilities (such as personnel qualifications, equipment compliance, and the effectiveness of testing methods). The repetitive labor intensity is high, and key risk points are easily overlooked.

[0009] In other words, the shortcomings of existing approval models and systems are mainly concentrated at the technical level: existing systems do not deeply integrate artificial intelligence technology, lack the ability to semantically understand and extract features from multimodal materials (text, images, numerical data), and cannot achieve intelligent identification of material authenticity and logical consistency verification; at the mechanism level: different applications involve different business processes, and a rule-driven process engine architecture is not adopted, which does not support the visual configuration of approval nodes, permissions, and process logic, resulting in low efficiency in collaboration and process flow; at the standard level: an approval knowledge system that is dynamically synchronized with technical specifications has not been established, and approval rules rely on manual interpretation, making it difficult to achieve standardized and regulated review; at the evidence preservation level: tamper-proof evidence preservation technology is not adopted, and the integrity and authenticity of approval process data cannot be effectively guaranteed. There is an urgent need for an intelligent approval solution that integrates multimodal semantic understanding and blockchain evidence preservation technology to solve core issues such as material forgery identification, standardization, cross-departmental collaboration, and full-process traceability. Summary of the Invention

[0010] Technical Solution: To address the shortcomings of existing technologies, this invention provides an intelligent approval method for inspection and testing qualifications based on multimodal semantic understanding and blockchain evidence storage. Specific steps include: constructing a dynamic knowledge graph in the field of inspection and testing: building an initial graph based on domain technical specifications and historical approval cases, and automatically incrementally updating it according to specification updates or frequently rejected cases; performing multimodal intelligent parsing of application materials: generating structured feature vectors by extracting features, performing semantic analysis, and conducting logical verification on various types of application materials, including text, images, and numerical data. Cross-node collaborative approval based on consortium blockchain: Deploy a consortium blockchain network that includes multiple nodes for qualification certification acceptance, review group management, on-site review, qualification certification approval and judicial evidence storage, and use fault-tolerant algorithms to achieve consensus approval among the nodes; End-to-end evidence storage: Calculate the hash digest of the entire approval process data and write it into a consortium blockchain ledger jointly maintained by multiple parties to achieve integrity protection and traceability of the approval data.

[0011] As an improvement, the specific steps for constructing a dynamic knowledge graph include: Step S101: Entity relation extraction. A bidirectional encoder representation transformation-conditional random field (BERT-CRF) model is used to extract entity relation triples containing subject qualifications, personnel requirements, and detection equipment from unstructured policy texts. Step S102: Establish a mapping model, identify the correspondence between technical specification clauses and approval key points through an attention mechanism, and construct a specification-key point mapping network. Step S103: Graph Evolution Trigger. Set an evolution trigger. When a national standard update or a high-frequency rejection item appears in a historical approval case, the graph incremental learning process is automatically activated. The case association pattern is analyzed through graph convolutional network, and new requirements or risk points are added to the knowledge graph.

[0012] As an improvement, the specific steps for intelligent parsing of multimodal application materials include: Step S201: Classify the material formats by automatically identifying the type of application materials, including text materials such as quality manuals and procedural documents; image materials such as legal entity status certificates and fixed site usage right certificates; and numerical materials such as instrument and equipment standard material configuration tables and testing capability application forms, and assign corresponding parsing channels. Step S202: Text semantic layer parsing. A pre-trained model combining document layout features, text semantic features and spatial location information is used to perform OCR recognition and semantic understanding of text materials, and to achieve cell-level semantic annotation for key tables to generate structured text features. Step S203: Visual authenticity layer analysis. An adversarial discrimination model trained based on the differences between the texture distribution of real seals and forged samples is used. By learning the microscopic texture features of existing real seals, including ink penetration marks and deformation caused by uneven stamping pressure, the model distinguishes between genuine and counterfeit seals and signatures in image-based materials. Step S204: Logical consistency layer verification. Based on the cross-validation rules in the dynamic knowledge graph, logical consistency verification is performed on numerical data and text information. When there are logical contradictions or inconsistencies with compliance, an anomaly is marked. Step S205: Generate a structured feature vector by integrating text semantic features, visual authentic features, and logical verification results to generate a unified structured feature vector, providing data support for subsequent approval.

[0013] As an improvement, the specific steps for determining authenticity include: The first layer of verification: topological structure analysis of the seal, including comparing the core features of the official seal such as splines, character spacing, and border style; The second verification step involves analyzing the color distribution characteristics of the stamp area to determine whether the RGB color gamut meets the preset standard. The third verification step: mechanical trace detection of the stamped area. By analyzing the ink spillage, shadow gradient and edge sharpness in the two-dimensional image, it can identify whether there are smooth edge traces left by image editing software.

[0014] As an improvement, the specific steps for cross-chain multi-node collaborative approval include: Step S301: Decompose the approval process and combine the responsibilities of multiple nodes to standardize the qualification approval process into four consensus steps, and clarify the correspondence between each step and node; Step S302: Pre-verification stage. The qualification acceptance node takes the lead in conducting pre-verification to quickly verify the "material integrity" index in the structured feature vector. If the verification fails, the process is terminated and the pre-verification results are synchronized to the judicial evidence storage node. Step S303: Dynamic subnet division. After the pre-verification of the qualification acceptance node is passed, the review group management, on-site review, and qualification approval nodes are dynamically divided into multiple consensus subnets according to the field classification and risk level of the approval items. The judicial evidence storage node independently connects to each subnet for synchronous evidence storage. Step S304: Parallel approval. Based on the prior review, each consensus subnet independently and in parallel executes the approval task of the corresponding stage without waiting for the previous stage to be completely completed. The on-site review node checks the data of the laboratory temperature and humidity recorder and equipment operation log through manual verification to verify whether the standard requirements are met. The approval process of each subnet is synchronized to the judicial evidence storage node in real time. Step S305: Opinion aggregation. The (t,n) threshold signature technology is used to aggregate the approval opinions of each consensus subnet, where n is the number of subnet nodes and t is a preset threshold. When more than t valid signatures are collected, a subnet consensus opinion can be formed without the need for all nodes to agree. Finally, the qualification approval node summarizes the opinions of each subnet to form the final review opinion. Step S306: Smart contract recording. The smart contract automatically records the approval opinions, review basis and operation time of each node, and generates a unique timestamp for on-chain storage.

[0015] As an improvement, the corresponding relationships in step S301 include: Initial material review: handled by the accreditation acceptance node, responsible for reviewing the completeness and format compliance of the materials; Technical review: handled collaboratively by the review team formation and dispatch nodes, responsible for reviewing testing capabilities, personnel qualifications, and equipment compliance; On-site verification: handled by the on-site review node, responsible for reviewing the compliance of the laboratory environment and operating procedures; Comprehensive evaluation: handled by the accreditation approval node, responsible for summarizing opinions from each stage and making the final compliance judgment; Judicial evidence preservation node: evidence preservation is tracked synchronously throughout the entire process.

[0016] As an improvement, the specific steps for end-to-end blockchain-based evidence storage include: Step S401: Data packaging. The hash values ​​of the application materials, the approval logs of each node (including timestamps, approval opinions, and signature information), the decision basis, and the final results are packaged according to the Merkle tree structure to generate the Merkle tree root hash. Step S402: Hash anchoring, anchoring the Merkel root hash to a third-party authoritative blockchain evidence storage platform with judicial evidence storage capabilities to obtain a reliable timestamp with judicial validity; Step S403: Smart contract monitoring; The list of required materials for approval, approval time limits, and process rules are pre-set in the smart contract; When any step is detected as not being completed within the specified time limit, the smart contract automatically triggers an alert and records the violation status; Step S404: Evidence package generation. After the approval process is completed, the system automatically generates a standardized electronic evidence package containing application material hashes, approval logs, Merkle tree path proofs, and trusted timestamp proofs, for use in subsequent administrative reconsideration or judicial review.

[0017] Beneficial effects: The intelligent approval method for inspection and testing qualifications based on multimodal semantic understanding and blockchain evidence storage proposed in this invention constructs a four-in-one technical architecture of "semantic understanding - trusted consensus - intelligent decision-making - judicial evidence storage". Through four key steps of "dynamic knowledge graph construction → multimodal material analysis → cross-chain multi-node collaborative approval → full-process blockchain evidence storage", it can realize intelligent and reliable inspection and testing qualification approval.

[0018] Compared with the prior art, the present invention has the following significant advantages: (1) The ability to identify material forgery has been greatly improved: By constructing a multimodal intelligent analysis mechanism, using the adversarial discrimination model in the visual authenticity layer analysis and the spectral feature analysis of ink pad, it is possible to effectively identify seal forgery and PS tampering traces that are difficult to detect with the naked eye; combined with the logical consistency verification based on knowledge graph, the cross-verification of text and numerical data has been realized, which effectively solves the logical contradictions and hidden forgery problems that are difficult to detect in traditional manual review, and ensures the authenticity of the approval materials from the source; (2) Unified Approval Standards and Dynamic Updates: Based on dynamic knowledge graph technology, unstructured laws, regulations, and technical specifications are transformed into standardized knowledge graph rules, and evolution triggers are set to achieve automatic incremental updates of the rules. This ensures that the approval basis for different approvers and at different times remains highly consistent, effectively reducing the phenomenon of "different judgments for the same case" caused by human misunderstanding, and significantly improving the fairness and standardization of qualification certification.

[0019] (3) Significantly improved cross-departmental collaboration efficiency: By utilizing the cross-chain collaboration and dynamic subnet technology of blockchain, the traditional sequential approval process is reconstructed into a parallel and consensus mechanism. Through the rapid filtering of invalid applications in the pre-verification stage and the parallel operation of multiple nodes (technical review, on-site verification, etc.), the process stagnation caused by single-node blocking is avoided; at the same time, smart contracts automatically monitor the time limits of each link, effectively solving the problems of shirking responsibility and delays in multi-departmental collaboration, significantly improving approval efficiency, shortening the average approval cycle from 58 days to 20 days, and increasing collaboration efficiency by 65%; (4) The approval process is traceable and tamper-proof: the entire approval process (materials, opinions, decisions, and basis) is stored using blockchain, achieving judicial-level evidence storage, reducing the administrative review rate by 81%, and providing complete and credible evidence quickly when disputes occur. (5) Intelligent assistance reduces the workload of human resources: Through the intelligent decision-making center, the system can automatically pre-examine the integrity and compliance of materials, and handle a large amount of formal review work, so that approval experts can focus on core technical aspects such as testing capability assessment. This not only reduces the workload of approval personnel, but also reduces the risk of human error and improves the overall approval quality through the auxiliary prompt function of knowledge graph.

[0020] This invention can be widely applied to the qualification certification of various testing and inspection institutions, such as environmental testing, food testing, material testing, and medical testing. It can also be extended to other administrative licensing fields that require multi-departmental collaboration, material authenticity verification, and process traceability (such as special equipment production licenses and medical device business licenses), with broad application prospects. Attached Figure Description

[0021] Figure 1 This is a timing diagram of the fault-tolerant consensus algorithm used in the entire method of this invention.

[0022] Figure 2 This is a flowchart illustrating the multimodal material analysis process of this invention.

[0023] Figure 3 This is a Merkel tree structure diagram for judicial evidence preservation in this invention. Detailed Implementation

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below, so that those skilled in the art can better understand the advantages and features of the present invention, thereby making a clearer definition of the scope of protection of the present invention. The embodiments described in this invention are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] The "special features" of the intelligent approval method for inspection and testing qualifications based on multimodal semantic understanding and blockchain evidence storage proposed in this invention compared with existing technologies are specifically reflected in the following aspects: (1) "On-chain-off-chain" dual-anchored storage mechanism: A hybrid architecture of "IPFS decentralized storage of original documents + blockchain notarization hash" is adopted. The original application materials are stored in IPFS. Once the file content is modified, its hash value will change and will not match the hash stored on the chain, thus achieving "file immutability" and "low storage cost and high efficiency". This solves the contradiction between large file notarization and blockchain performance.

[0026] (2) Microservice architecture with decoupled computation and consensus: "AI computation (heavy computation)" and "blockchain consensus (light computation)" are decoupled through a microservice layer. Multimodal parsing and knowledge graph reasoning run in independent microservice containers (off-chain computation), and only the generated "structured feature vectors" and "decision results" are sent to the consortium blockchain network for consensus (on-chain confirmation). This architecture retains the powerful processing capabilities of AI while ensuring the efficient flow of blockchain.

[0027] (3) "Dynamically perceptive gateway routing strategy: The gateway layer is linked with the intelligent decision-making center to realize "dynamic routing based on business attributes". When the gateway receives applications from different fields (such as "environmental detection" vs. "food detection"), it can route them to microservice instances loaded with specific field knowledge graph models or to specific consortium blockchain subnet nodes according to the request header tags. It supports the rapid response of "dynamic grouping" and "dynamic knowledge graph" mentioned in the claims.

[0028] Specifically, this invention proposes an intelligent approval method for inspection and testing qualifications based on multimodal semantic understanding and blockchain evidence storage. The specific steps of the method include constructing a dynamic knowledge graph in the field of inspection and testing: constructing an initial graph based on domain technical specifications and historical approval cases, and automatically incrementally updating it according to specification updates or high-frequency rejection cases. Multimodal intelligent parsing of application materials: By performing feature extraction, semantic analysis, and logical verification on various types of application materials, including text, images, and numerical data, structured feature vectors are generated; Cross-node collaborative approval based on consortium blockchain: Deploy a consortium blockchain network that includes multiple nodes for qualification certification acceptance, review group management, on-site review, qualification certification approval and judicial evidence storage, and use fault-tolerant algorithms to achieve consensus approval among the nodes; End-to-end evidence storage: Calculate the hash digest of the entire approval process data and write it into a consortium blockchain ledger jointly maintained by multiple parties to achieve integrity protection and traceability of the approval data.

[0029] The above method includes four core parts, the first step being the construction of a dynamic knowledge graph.

[0030] This step is implemented by the knowledge graph engine module. Its core is to construct an evolvable knowledge graph for the testing and inspection field, providing standardized criteria for approval. Specifically, it includes: Step S101: Entity Relationship Extraction. A bidirectional encoder representation transformation-conditional random field (BERT-CRF) model is used to extract entity relation triples containing subject qualifications, personnel requirements, and detection equipment from unstructured policy text; Step S102: Establish a mapping model. Identify the correspondence between technical specification clauses and approval key points using an attention mechanism, and construct a specification-key point mapping network; Step S103: Graph Evolution Trigger. Set an evolution trigger. When a national standard update or a high-frequency rejection item appears in a historical approval case, the graph incremental learning process is automatically activated. The case association pattern is analyzed through graph convolutional network, and new requirements or risk points are added to the knowledge graph.

[0031] Step 2: Intelligent analysis of multimodal application materials.

[0032] This step is implemented by the multimodal parsing service module, which integrates OCR, natural language processing (NLP), and computer vision capabilities to perform three-dimensional verification of application materials: visual, semantic, and logical. (See below) Figure 2 As shown in the diagram, this figure illustrates the detailed process of multimodal material analysis, clarifying the processing logic and technical means of each step. The entire process includes input material → format classification → parallel processing (text branch, image branch, data branch) → feature fusion → verification report. The text branch process includes: OCR recognition → semantic understanding (LayoutLMv3 model) → entity extraction; the image branch includes: stamp / signature detection → image feature enhancement based on feature learning → triple verification (topological structure + spectral features + mechanical traces); the data branch includes: numerical extraction → logical rule matching (knowledge graph support) → consistency check.

[0033] As a specific embodiment of the present invention, the entire process includes: Step S201: Material format classification. The system automatically identifies the type of application materials (text type: quality manual, procedure document; image type: legal person status certificate, fixed location use right certificate; numerical type: instrument and equipment (standard substance) configuration table, testing capability application form), and assigns the corresponding parsing channel.

[0034] Step S202: Text Semantic Layer Parsing. A pre-trained model combining document layout features, text semantic features, and spatial location information (a pre-trained model supporting document layout understanding) is used to perform OCR recognition and semantic understanding of textual materials. Under the condition of a historical inspection and testing application document sample of no less than a preset scale, the accuracy is improved by 35% compared with the traditional OCR method that only performs character-level recognition. Furthermore, cell-level semantic annotation (such as "Detection Item - Corresponding Equipment Model - Detection Standard") is implemented for key tables such as the detection capability table, generating structured text features.

[0035] Step S203: Visual Authenticity Layer Analysis. An adversarial discrimination model trained on the differences between the texture distribution of real seals and forged samples learns the microscopic texture features (ink penetration marks, deformation caused by uneven stamping pressure) of 30,000+ real seals to distinguish the authenticity of seals and signatures in image-based materials.

[0036] In the steps of this invention, the specific method for determining authenticity includes: First verification: topological structure analysis of the seal, such as comparing the core features of the seal, such as the spline, character spacing, and border style; The second verification step involves analyzing the color distribution characteristics of the stamp area to determine whether the RGB color gamut meets the preset standard. The third verification: mechanical trace detection of the stamped area, which identifies whether there are smooth edge traces left by image editing software by analyzing the ink spillage, shadow gradient and edge sharpness in the two-dimensional image; With no fewer than 5,000 genuine seal samples and 500 counterfeit samples, and in an image input environment with a resolution of no less than 300 dpi, this step achieved a recall rate of 98.7% for counterfeit seals and an overall accuracy rate of over 99.5%.

[0037] Step S204: Logical Consistency Layer Verification. Based on the cross-validation rules in the dynamic knowledge graph, logical consistency verification is performed on numerical data and text information, for example: Equipment list and testing capability verification: If an application for "food additive testing qualification" is submitted but "liquid chromatograph" is not found in the equipment register, it will be marked as a logical contradiction; Personnel Qualification and Testing Item Verification: If the testing item requires "senior engineer" qualification, but the submitted personnel certificate is "junior professional title", it will be marked as non-compliance. Step S205: Generate a structured feature vector. Integrate textual semantic features, visual authentic features, and logical verification results to generate a unified structured feature vector, providing data support for subsequent approval.

[0038] Step 3: Cross-chain multi-node collaborative approval.

[0039] This step is implemented by the consortium blockchain consensus network module. This module consists of multiple nodes responsible for qualification acceptance, forming a review panel, dispatching the review panel for review, on-site review, qualification approval, and judicial evidence preservation. It employs a fault-tolerant algorithm to achieve multi-department collaborative approval. See [link / details]. Figure 1 As shown in the diagram, this illustrates the timeline logic of cross-departmental collaborative approval, clarifying the improvement steps of the fault-tolerant algorithm and the interaction flow of each node. Furthermore, the timeline flow can be derived as follows: Client (Applicant / System) → Pre-prepare (initiated by the master node) → Pre-verification (verification of material integrity by each node) → Dynamic grouping (subnet partitioning) → Preparation (exchange of opinions among nodes within the subnet) → Commit (threshold signature aggregation) → Reply (return of approval opinion) → On-chain (proof storage). The specific process of this step is as follows: Step S301: Approval Process Decomposition. Based on the responsibilities of the five parties involved, the qualification approval process is standardized into four consensus-based stages, clarifying the correspondence between each stage and node: Initial Material Review (responsible by the qualification acceptance node, responsible for reviewing the completeness and format compliance of materials); Technical Review (co-responsible by the review team formation and dispatch nodes, responsible for reviewing testing capabilities, personnel qualifications, and equipment compliance); On-site Verification (responsible by the on-site review node, responsible for reviewing the compliance of the laboratory environment and operating procedures); and Comprehensive Evaluation (responsible by the qualification approval node, responsible for summarizing opinions from each stage and making the final compliance judgment). The judicial evidence preservation node will simultaneously follow up on evidence preservation throughout the entire process.

[0040] Step S302: Pre-verification Phase. The qualification acceptance node leads the pre-verification process, which involves a rapid check of the "material integrity" indicator in the structured feature vector. If the check fails, the process is terminated, and the pre-verification results are synchronized to the judicial evidence storage node.

[0041] Step S303: Dynamic subnet division. After the qualification acceptance node passes the pre-verification, the review group management, on-site review, and qualification approval nodes are dynamically divided into multiple consensus subnets according to the field classification and risk level of the approval items. The judicial evidence storage node independently accesses each subnet for synchronous evidence storage, avoiding performance bottlenecks caused by network-wide broadcasting.

[0042] Step S304: Parallel Approval. Based on the prior review approval, each consensus subnet independently and in parallel executes the corresponding approval tasks. For example, technical review and on-site verification can be carried out in parallel without waiting for the previous step to be completely completed. The on-site review node can manually verify data such as laboratory temperature and humidity recorders and equipment operation logs to check whether the standard requirements are met. The approval process of each subnet is synchronized to the judicial evidence storage node in real time.

[0043] Step S305: Opinion Aggregation. The (t, n) threshold signature technology is used to aggregate the approval opinions of each consensus subnet, where n is the number of subnet nodes and t is a preset threshold. When more than t valid signatures are collected, a subnet consensus opinion can be formed, without the need for all nodes to agree. Finally, the qualification approval node summarizes the opinions of each subnet to form the final review opinion.

[0044] Step S306: Smart Contract Recording. The smart contract automatically records the approval opinions, review basis, and operation time of each node, and generates a unique timestamp for on-chain storage, ensuring that the entire approval process is traceable and tamper-proof.

[0045] Step 4: Blockchain-based evidence storage throughout the entire process.

[0046] This step is implemented collaboratively by the consortium blockchain consensus network module and the intelligent decision-making center module. By writing the hash digest of the entire approval process data into the consortium blockchain ledger jointly maintained by multiple parties, the integrity protection and traceable evidence of the approval process data are achieved. See [link / details]. Figure 3 As shown, the packaging structure of the evidence storage data is illustrated, clarifying the construction logic of the Merkle tree and the data integrity guarantee mechanism. The entire structure includes leaf nodes (application material hash, approval log hash of each node, decision result hash) → intermediate nodes (leaf node hash aggregation result) → root node (final aggregated hash, anchored to the judicial chain). The specific process of this step includes: Step S401: Data Packaging. The hash values ​​of the application materials, the approval logs of each node (including timestamps, approval opinions, and signature information), the decision basis, and the final results are packaged according to the Merkle tree structure to generate the Merkle tree root hash.

[0047] Step S402: Hash Anchoring. Anchor the Merkel root hash to a third-party authoritative blockchain evidence storage platform with judicial evidence storage capabilities to obtain a trusted timestamp with judicial validity.

[0048] Step S403: Smart contract monitoring; pre-set the list of required approval materials, approval time limits, and process rules in the smart contract; when any step is detected to be not completed within the specified time limit, the smart contract automatically triggers an alert and records the violation status.

[0049] Step S404: Evidence Package Generation. After the approval process is completed, the system automatically generates a standardized electronic evidence package containing application material hashes, approval logs, Merkle tree path proofs, and trusted timestamp proofs, for use in subsequent administrative review or judicial review.

[0050] Example 1: Accreditation of Environmental Testing and Inspection Agencies (Application for "Water and Wastewater" Testing Qualification) (a) Prerequisites for implementation The system has completed the construction of a dynamic knowledge graph in the field of inspection and testing, which includes technical specifications such as the "General Requirements for the Evaluation of the Competency of Inspection and Testing Institutions for Accreditation" (RB / T 214-2017) and historical approval cases; The consortium blockchain consensus network has been deployed, with nodes including: qualification certification acceptance node (market supervision bureau window), review group management node (expert database system), on-site review node (mobile law enforcement terminal), qualification certification approval node (decision-making level), and judicial evidence storage node (notary office / court).

[0051] The applicant (an environmental testing organization) submits electronic application materials, including: quality manual (PDF version), personnel qualification certificates (scanned copies), equipment ledger (Excel version), typical test reports (PDF version), and laboratory site use right certificate (JPG, image file).

[0052] (II) Specific Implementation Steps Step S1: Dynamic Knowledge Graph Construction and Adaptation The system extracts key requirements related to "water and wastewater" testing from RB / T 214-2017 through entity extraction units, including "4.2.4 The authorized signatory of the inspection and testing organization shall have an intermediate or higher professional technical title or equivalent ability and be approved by the qualification accreditation department. Unauthorized signatories shall not issue inspection and testing reports or certificates." and "4.3 Site environment." Analysis of historical case database revealed that "failure to provide verification records during the period of standard material provision" is a frequently rejected item in this field. This item was automatically added to the key points of this review, automatically triggered spectrum evolution, and updated the cross-validation rule database.

[0053] Step S2: Intelligent Analysis of Multimodal Materials Format Classification: The system automatically distributes application materials. Quality manuals and test reports are distributed to the text parsing channel; personnel certificates and site certificates are distributed to the visual parsing channel; equipment ledgers are distributed to the numerical parsing channel. Text semantic parsing: A pre-trained model based on document layout understanding was used to identify the "Appointment Letter of Quality Supervisor" section in the quality manual and the "Appointment Date: 2024-01-01" was extracted. However, the signature area was identified as being in standard printed font rather than handwritten handwriting, and the feature was marked as [Signature_Type: Printed, Status: Warning]. Visual Authenticity Verification: The visual authenticity verification unit analyzes the "Laboratory Fixed Location Usage Certificate". The adversarial discriminant model detects abnormal edge sharpness in the area of ​​the "XX Testing Company" official seal, and spectral analysis shows that the ink color exhibits "printing ink" characteristics rather than "oil ink" characteristics in the RGB color gamut, classifying it as "suspected color photocopy / non-original stamp" (confidence level 92%), and marking the feature as [Seal_Status: Suspected_Copy, Risk_Level: High]; Logical consistency check: The system checks the "Equipment List and Testing Capabilities". The application for the "Water and Wastewater - Heavy Metals" project includes "Atomic Absorption Spectrometer (AAS)" in the ledger, which conforms to the spectral rules; Check "Personnel Qualifications": The testing personnel certificates show 3 intermediate professional titles, but the spectral verification found that the issuing unit of one of the professional title certificates was not in the "Official Professional Title Database" whitelist, marked as [Qual_Check: Unverified_Source]; Generate structured feature vectors: Integrate the above analysis results to generate structured feature vectors containing "incorrect signature format", "not genuine seal", and "questionable source of qualification".

[0054] Step S3: Cross-chain multi-node collaborative approval (triggering interception) Pre-verification (automatic interception): Upon receiving the structured feature vector, the smart contract automatically triggers the pre-verification rules. Due to the presence of the "high-risk (seal not genuine)" label, the pre-verification is judged as "failed". Intelligent feedback: The system directly blocks the triggering of subsequent steps such as "forming a review panel" (to avoid wasting expert resources) and feeds back any anomalies to the applicant through the API gateway.

[0055] Evidence Preservation Record: The judicial evidence preservation node records the operation log for this operation: "Time: 2025-XX-XX, Event: Pre-verification rejected, Reason: Authenticity of materials is questionable".

[0056] Step S4: Applicant provides supplementary information Following the system prompts, the applicant re-uploaded a scanned copy of the original appointment letter with a handwritten signature, the original proof of the premises with an official seal, and supplemented the screenshot of the professional title inquiry.

[0057] Step S5: Secondary Intelligent Analysis and Collaborative Approval Secondary analysis: The system repeats step S2, confirms that the signature feature is a "handwritten trace", the spectral feature of the official seal meets the "oil-based ink" standard, and all risk labels are cleared.

[0058] Dynamic grouping: Pre-verification passed. Based on the classification code "Environmental Testing - Water Quality (Code 18401)," the system dynamically divides consensus subnets, incorporating acceptance nodes, technical review nodes (automatically matched with water quality experts), and on-site review nodes into the same subnet.

[0059] Parallel approval: At the technical review stage, the system automatically compares equipment records with standard requirements. Experts only need to verify calibration certificates for special equipment, significantly reducing processing time. At the on-site review stage, reviewers upload laboratory temperature and humidity records and photos of ventilation facilities via mobile devices during on-site verification. The system automatically compares these with spectral standards to confirm compliance.

[0060] Opinion aggregation: After each node completes its approval process, it generates a signature fragment. The system uses (t,n) threshold signature technology (setting n=4, t=3). After collecting more than 3 valid signature fragments, it automatically synthesizes a unique subnet consensus opinion "pass".

[0061] Step S6: Blockchain-based evidence storage throughout the entire process Data packaging: Package the material hashes before and after correction, pre-verification rejection records, logs of each node in the secondary approval, and the final consensus signature according to the Merkle tree structure; Smart contract solidification: Once the smart contract verification process meets the time limit, the "certificate issuance" instruction is automatically triggered.

[0062] Evidence package generation: An electronic qualification certificate is generated, and the certificate hash is uploaded to the blockchain. The applicant receives a "permission granted" result, and the entire rectification process has a complete evidence chain, which can be traced by subsequent regulators.

[0063] Example 2: Identification of Material Forgery (An organization forged a "Senior Engineer" certificate to apply for qualifications) (I) Scene Background When applying for "main structure testing" qualification, the applicant (a building materials testing company) lacked an authorized signatory with a senior professional title. So, it used image processing software (Photoshop) to modify the qualification certificate of an ordinary employee to "senior engineer" and forged the official seal of the issuing authority. It then uploaded the document to the system in an attempt to deceive the applicant.

[0064] (II) Specific Implementation Steps Step S1: Multimodal Intelligent Analysis Text semantic layer parsing: The text semantic parsing unit performs OCR recognition on the certificate content, extracting key fields such as "Name: Zhang San", "Title: Senior Engineer", and "Issuance Date: 2023". Simultaneously, the system reads the employee's age from social security payment records (supporting materials) as 26 years old. The knowledge graph immediately triggers a logical contradiction flag: According to the "Title Evaluation Rules" in the graph, obtaining a senior engineer title typically requires 10 years of work experience after a bachelor's degree or 2 years after a doctoral degree. There is a high probability that this person's age conflicts with the logical requirements for obtaining the title.

[0065] Visual authenticity layer analysis: The visual authenticity verification unit uses Error Level Analysis (ERP) technology to detect compression artifacts in the image. The results show that the compressed noise distribution in the "Senior Engineer" text area is significantly different from the noise distribution in the certificate background texture (inconsistent JPEG quantization table), and this area is determined to be "post-processed composite / PS tampering" (98% confidence level).

[0066] Generate feature vector: The system generates a structured feature vector with a high-risk label: [Tamper_Region:(x,y,w,h), Type: Digital_Manipulation, Severity: Critical].

[0067] Step S2: Cross-chain multi-node collaborative approval (triggered interception): Pre-verification (automatic interception): Upon receiving the structured feature vector, the smart contract automatically triggers the pre-verification rules. Due to the presence of the "high-risk (seal not genuine)" label, the pre-verification is judged as "failed". Intelligent feedback: The system directly blocks the triggering of subsequent steps such as "forming a review panel" (to avoid wasting expert resources) and feeds back any anomalies to the applicant through the API gateway.

[0068] Evidence Preservation Record: The judicial evidence preservation node records the operation log for this time: "Time: 2025-XX-XX, Event: Pre-verification rejected, Reason: Authenticity of materials is questionable".

[0069] Example 3: Dynamic Updates of Knowledge Graphs (New Review Criteria Released) (I) Scene Background When the State Administration for Market Regulation releases the new version of the "Accreditation Criteria for Inspection and Testing Institutions" (2023 Edition), the system detects the changes in the standard text and automatically adapts to the new regulations to complete the iteration of the approval basis.

[0070] (II) Specific Implementation Steps Step S1: Regulatory Change Monitoring and Cold Start Access Monitoring and Data Extraction: The incremental learning unit in the knowledge graph engine module monitors the official website of the State Administration for Market Regulation in real time through the API interface, captures signals of changes in regulatory texts, and automatically downloads the new version of the guidelines PDF file.

[0071] Semantic extraction (cold start): The ERNIE-Gram (or BERT-wwm) pre-trained language model is used to perform text-level reading comprehension of the new regulations and identify the newly added "remote review" section. Entity extraction technology is used to extract 12 core clause rules, such as entity pairs for [remote review]—must ensure—[video recording integrity], [electronic archives]—must have—[anti-tampering measures].

[0072] Initial knowledge graph update: The system automatically creates a "remote review" concept node in the dynamic knowledge graph and establishes a [is_supplement_to] (supplement) relationship between it and the existing "on-site review" node to complete the initial rule entry into the database.

[0073] Step S2: Graph Thermal Adaptation Based on Historical / Pilot Cases Association pattern mining: Although the new regulations have just been released, the system uses **Graph Convolutional Network (GCN)** to retrospectively analyze pilot cases or related complaint records about "remote video connection" in the historical database over the past three months.

[0074] Points of contention identified: GCN model analysis revealed that "video stuttering" and "corrupted recording files" were the main reasons why the review could not make a determination in historical cases (high-weight features).

[0075] Rule refinement and enhancement: Based on the mining results, the system automatically generates more detailed technical verification indicators under the "Video Recording Integrity" node - automatically creating new nodes "Record File MD5 Verification" and "Continuous Frame Rate Detection", and associating them with the "Recording Control" clause.

[0076] Result: Through "new regulation import + case mining", the system successfully transformed abstract legal provisions into executable technical verification rules (such as: detecting whether there are hash breakpoints in video files).

[0077] (III) Implementation Results In this embodiment, it takes only 25 working days for the applicant to go from submitting materials to receiving an "approval" opinion, which is significantly shorter than the traditional approval cycle (58 working days); issues such as forged official seals and abnormal signatures are accurately identified, preventing unauthorized institutions from obtaining qualifications; all data in the approval process is stored on the blockchain to ensure traceability and immutability.

[0078] This invention has been deployed in three provincial-level market supervision departments, supporting the qualification certification of over 10,000 inspection and testing institutions, processing over 1 million application materials, and operating stably for over 38,000 hours, proving the reliability and practicality of the application. Those skilled in the art can reproduce the technical solution of this invention by following the description of the above embodiments, using existing mature artificial intelligence models (such as BERT-CRF, LayoutLMv3, GAN), blockchain technology (such as consortium blockchain, smart contracts), and hardware devices (servers), without any creative effort.

[0079] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for intelligent examination and approval of inspection and qualification based on multi-modal semantic understanding and blockchain storage, characterized in that, The specific steps of the method include constructing a dynamic knowledge graph in the field of inspection and detection: constructing an initial graph based on field technical specifications and historical approval cases, and automatically updating incrementally according to specification updates or high-frequency rejection cases; Intelligent analysis of multi-modal application materials: feature extraction, semantic analysis and logical verification are performed on various types of application materials such as text, images and numerical values to generate structured feature vectors; Cross-node collaborative approval based on alliance chain: deploy an alliance chain network containing qualification recognition, evaluation group management, on-site evaluation, qualification recognition and approval, and judicial evidence, and use fault-tolerant algorithms for consensus approval between nodes; Full-process evidence: calculate the hash digest of the approval process data and write it into the alliance chain ledger maintained by multiple parties to protect the integrity and traceability of the approval data.

2. The method of claim 1, wherein, The steps for constructing a dynamic knowledge graph include: Step S101: Entity relationship extraction, a bidirectional encoder representation transformation-conditional random field (BERT-CRF) model is used to extract entity relationship triples including subject qualification, personnel requirements and detection equipment from unstructured policy text; Step S102: Establish a mapping model to identify the correspondence between technical specification clauses and approval points through attention mechanism, and construct a specification-point mapping network; Step S103: Graph evolution trigger, set the evolution trigger, when the national specification is updated or the high-frequency rejection items appear in the historical approval cases, automatically activate the graph incremental learning process, analyze the case correlation mode through graph convolution network, and supplement the new requirements or risk points to the knowledge graph. 3.The method of claim 1, wherein, The specific steps of multi-modal application material intelligent analysis include: Step S201: Material format classification, automatically identify the type of application materials, including text: quality manual, program file; image: legal status proof file, fixed place use right proof file; numerical value: instrument and equipment standard substance configuration table, detection capacity application table, and assign corresponding analysis channels; Step S202: Text semantic layer analysis, a pre-training model combining document layout features, text semantic features and spatial location information is used for OCR recognition and semantic understanding of text materials, and key tables are implemented for cell-level semantic annotation to generate structured text features; Step S203: Visual authenticity layer analysis, an adversarial discrimination model trained based on the difference between real seal texture distribution and fake samples is used to learn the micro-texture features of existing real seals, including ink penetration marks and deformation caused by uneven stamping pressure, to distinguish real and fake seals and signatures in image materials: Step S204: Logical consistency layer verification, based on the cross-validation rules in the dynamic knowledge graph, the logical consistency of numerical data and text information is verified, and when there is logical contradiction or inconsistency with compliance, it is marked as abnormal; Step S205: Generate structured feature vectors, integrate text semantic features, visual authenticity features and logical verification results to generate a unified structured feature vector to provide data support for subsequent approval.

4. The method of claim 3, wherein, The specific steps for distinguishing true and false include: The first re-verification: analysis of the seal topology, including comparison of the spline, character spacing, and core features of the border style of the official seal; The second re-verification: analysis of the color distribution characteristics of the seal area to determine whether the RGB color domain meets the preset standard; The third re-verification: mechanical trace detection of the seal area, which analyzes the ink overflow, shadow gradient, and edge sharpness in the two-dimensional image to identify whether there are flat edge traces left by image editing software processing.

5. The method of claim 3, wherein, The specific steps of cross-chain multi-node collaborative approval include: Step S301: Approval process decomposition, combined with the responsibilities of multiple nodes, the qualification approval process is standardized into four consensus links, and the correspondence between each link and node is clear; Step S302: Pre-verification stage, led by the qualification acceptance node, pre-verification is carried out, and the "material integrity" index in the structured feature vector is quickly checked. If the check fails, the process is terminated, and the pre-verification results are synchronized to the judicial evidence node; Step S303: Dynamic subnet division, after the qualification acceptance node passes the pre-verification, according to the domain classification and risk level of the approval matter, the evaluation group management, on-site evaluation, and qualification approval nodes are dynamically divided into multiple consensus subnets, and the judicial evidence node is independently connected to each subnet for synchronous evidence storage; Step S304: Parallel approval, based on the pre-check, each consensus subnet independently and in parallel executes the approval task of the corresponding link without waiting for the previous link to complete. The on-site evaluation node checks the laboratory temperature and humidity recorder and equipment operation log data through manual verification to verify whether they meet the standard requirements. The approval process of each subnet is synchronized in real time to the judicial evidence node; Step S305: Opinion aggregation, using (t, n) threshold signature technology, the approval opinions of each consensus subnet are aggregated, where n is the number of subnet nodes and t is the preset threshold. When more than t valid signatures are collected, the subnet consensus opinion is formed, without the need for unanimous agreement of all nodes. Finally, the qualification approval node aggregates the opinions of each subnet to form the final evaluation opinion; Step S306: Smart contract recording, using a smart contract to automatically record the approval opinions, review basis, and operation time of each node, and generate a unique timestamp for on-chain storage.

6. The method of claim 5, wherein, The correspondence in step S301 includes the material preliminary review link: the qualification acceptance node is responsible for material integrity and format compliance review; the technical evaluation link: the evaluation group is responsible for detection capability, personnel qualification, and equipment compliance review; the on-site verification link: the on-site evaluation node is responsible for laboratory environment and operation process compliance review; the comprehensive evaluation link: the qualification approval node is responsible for the aggregation of opinions from each link and the final compliance judgment; and the judicial evidence node: synchronous follow-up storage throughout the process.

7. The method of claim 1, wherein, The specific steps of the whole-process blockchain storage include: Step S401: Data packaging, the hash value of the application materials, the approval log of each node, the decision basis, and the final result are packaged according to the Merkle tree structure to generate a Merkle tree root hash; Step S402: Hash anchoring, anchoring the Merkle tree root hash to the third-party authority blockchain notarization platform with judicial notarization capability to obtain a trusted timestamp with judicial effect; Step S403: Smart contract monitoring; pre-set the list of required materials for approval, the time limit requirement for approval and the process rules in the smart contract; when it is detected that any link is not completed within the specified time limit, the smart contract automatically triggers an early warning and records the violation status; Step S404: Evidence package generation, after the end of the approval process, the system automatically generates a standardized electronic evidence package containing application material hash, approval log, Merkle tree path proof and trusted timestamp proof, which is used for subsequent administrative reconsideration or judicial review.

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