Intelligent matching method and system for standard technical service demand based on deep learning

Abstract

The present application relates to the technical field of standardized information processing, intelligent recommendation and explainable artificial intelligence, in particular to a standard technology service demand intelligent matching method and system based on deep learning, which is based on a standard knowledge base and an evidence index system, obtains demand and service capability data and performs deep semantic vector representation to screen candidate services; scores and sorts the candidate services and constructs a clause evidence chain to form an evidence set; combines standard evolution and evidence verification to calculate risk indicators and classify; dynamically adjusts the sorting weight and explanation granularity according to the risk, prohibits the completion of the generated clause number for high risk, outputs the recommendation and explanation and generates an audit trace record, solves the evidence fault problem caused by version drift by constructing a clause evidence chain and combining standard evolution and evidence verification state, and effectively avoids the pseudo-compliance risk. Through dynamic risk control, the completion behavior of the generated clause is limited to ensure the authenticity and reliability of the generated explanation.

Description

Technical Field

[0001] This invention relates to the fields of standardized information processing, intelligent recommendation, and explainable artificial intelligence technology. More specifically, it relates to a method and system for intelligent matching of standard technical service demands based on deep learning. Background Technology

[0002] In the field of standard technical services, the continuous evolution of the standard system, including version updates, clause rearrangements, changes in scope of application, and stricter or looser indicators, all affect the matching and interpretation of service needs with technical service capabilities. Updates to standard clauses often render existing clause numbers, applicability thresholds, chains of evidence, and their indexes invalid or inapplicable, leading to uncertainty in service recommendation and interpretation output. Especially when facing standard evolution and clause invalidation, existing systems may encounter the following technical challenges: The evolution of standards can lead to a break in the chain of evidence: As standards evolve, older clauses may become invalid or be replaced. However, in practice, the system often relies on the old version of the clause number, the chain of evidence, and the mapping of related clauses, which can lead to a distortion of the relationship between clauses and consequently result in missing evidence or incorrect mapping.

[0003] Challenges of Static Mapping and Language Completion Capabilities: In current technologies, updated clause mappings in standard systems often lag behind and rely on static knowledge bases and clause indexes to associate services with clauses. However, when these indexes and mappings fail to update in a timely manner, the system falls into a state of relying on generative models to complete missing evidence. When evidence is missing or uncertain, generative models often fill in the gaps by automatically generating clause numbers, wording, or citation structures to form a "complete" interpretation.

[0004] The emergence of pseudo-compliance issues: Generative completion technology, in the absence of supporting real evidence, often "masks" the lack of evidence by completing clause numbers or generating similar clause references, providing an interpretation that "seems to conform to the standard." This process fails to accurately reflect the actual evolution of the standard, and because the generated evidence has not been effectively verified, the final interpretation may become a pseudo-compliant interpretation, affecting the credibility and compliance of the recommendation system.

[0005] Chain distortion of pseudo-compliance due to evidence completion: As standards evolve and clauses are updated, the problem of broken evidence chains caused by version drift, combined with generative completion mechanisms, forms what is known as "pseudo-compliance due to evidence completion." In this case, the "pseudo-evidence" automatically filled by the generative mechanism does not come from real, reliable clause mappings, but rather from assumptions and additions made by the system to maintain the "completeness" of the interpretation, thus creating structured evidence distortion. Such pseudo-evidence not only lacks genuine compliance but may also create legal risks in subsequent audits and reviews, and even make it difficult to trace responsibility during compliance reviews.

[0006] Therefore, in the context of dynamic standard evolution, how to effectively avoid evidence distortion caused by generative mechanisms, especially how to prevent pseudo-compliance, has become an urgent problem to be solved in current technology. To ensure the interpretability and compliance of the recommendation system, this invention avoids the problem of pseudo-evidence caused by generative completion mechanisms by constructing a refined chain of evidence for the terms and a risk control mechanism, thereby improving the authenticity and reliability of the recommendation service and interpretation, and reducing compliance risks. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for intelligent matching of standard technical service requirements based on deep learning.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A deep learning-based intelligent matching method for standard technical service demands includes the following steps: Step 1: Obtain standard technical service demand data and technical service capability data, and establish a standard knowledge base and evidence index system; Step 2: Semantically represent the demand data and capability data, and determine the candidate service set based on semantic similarity; Step 3: Score and rank the candidate service set, and construct a corresponding chain of evidence for each recommended service after ranking to form a set of evidence for the terms; Step 4: Calculate dynamic risk control indicators and determine risk levels based on the verifiable status of the standard evolutionary state and the evidence set of the clauses. The dynamic risk control indicators shall include at least the evolutionary factor and the verification consistency factor. Step 5: Dynamically adjust the recommendation ranking strategy and explanation generation strategy according to the risk level, and output the recommended service list and explanation information. Among them: the standard version identifier and clause identifier in the explanation information are generated / rendered by the structured fields of the clause evidence chain and only the verified evidence items are allowed to be cited; when the risk level increases, the weight of the clause evidence set in the ranking is increased and the granularity of clause citation in the explanation is restricted; when the risk level reaches the preset high-risk state, it is prohibited to infer the generation of missing clause identifiers, and the clause identifier whitelist verification is performed on the explanation output to ensure that the clause identifiers appearing in the explanation belong to the corresponding clause evidence chain. Step Six: Generate an audit trail record that includes a standard version identifier, clause identifier, risk level, and explanation of the generation strategy status.

[0009] Furthermore, acquiring demand and capability data includes: collecting demand text, standard number / version, industry sector, constraints and deliverables, as well as capability descriptions, qualifications and licenses, service scope, resources and project examples, and then performing field mapping, cleaning and noise reduction and unified encoding before storing them in the database.

[0010] Furthermore, establishing a standard knowledge base includes: collecting standard text and version information and clause hierarchy / reference relationships; segmenting by chapter and clause and generating unique clause identifiers; extracting keywords and entities from clauses and calculating clause semantic vectors; and associating and storing clause unique identifiers, version identifiers, original clause text, structural relationships, and clause semantic vectors.

[0011] Furthermore, establishing an evidence indexing system includes: using unique clause identifiers to index the original text of the clauses, version identifiers, hierarchical paths, citation relationships, scope of application, and change records; and associating source standards / versions and location information for evidence items, generating verifiable links or hash verification information.

[0012] Furthermore, the semantic representation includes: preprocessing the demand text and capability text and then inputting them into a dual-tower encoding network to encode the demand semantic vector and service semantic vector respectively. The dual-tower encoding network is trained based on matching samples through contrastive learning or metric learning.

[0013] Furthermore, the scoring and ranking process includes: calculating a comprehensive matching score based on semantic similarity, coverage of standard terms, satisfaction of qualifications and constraints, and historical delivery performance, and re-ranking candidate services based on a learned ranking model or rule-based strategy.

[0014] Furthermore, constructing the clause evidence chain includes: retrieving the original text, version identifier, and hierarchical path of the clause from the evidence index system based on the unique identifier of the matched clause, and organizing the clause evidence chain according to the matching relationship, while associating the clause citation relationship, scope of application, and location information.

[0015] Furthermore, the calculation of dynamic risk control indicators includes: calculating the historical change frequency and current applicability of clauses based on the standard evolution status; assessing the completeness and accuracy of the clause evidence chain based on the verification information of the clause evidence set; and generating dynamic risk control indicators by weighting them according to timeliness and relevance factors.

[0016] Furthermore, a deep learning-based intelligent matching system for standard technical service demands includes: The data acquisition module is used to acquire standard technical service requirement data and technical service capability data; The library construction module is used to build a standard knowledge base and evidence indexing system based on the standard system. The semantic representation module is used to perform deep semantic representation on standard technical service demand data and technical service capability data respectively, and generate demand semantic vector representation and service semantic vector representation. The candidate computation module is used to compute a set of candidate services based on the demand semantic vector representation and the service semantic vector representation; The ranking and interpretation module is used to match, score, and rank the candidate service set, construct a chain of evidence for the terms to obtain a set of evidence for the terms, and generate explanatory information corresponding to the list of recommended services. The risk control module is used to calculate dynamic risk control indicators and determine risk levels based on the standard evolution status and the verifiable status of the evidence set of the clauses. It dynamically controls the service recommendation ranking strategy and interpretation generation strategy according to the risk level, outputs the recommended service list and interpretation information, and generates audit traceability records containing standard version identifiers, clause identifiers, risk levels and interpretation generation strategy status.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention, based on the acquisition of standard technical service demand data and technical service capability data and their deep semantic vector representation, introduces a clause evidence chain construction mechanism. This mechanism structurally binds the matching results of candidate service sets with specific standard version identifiers and clause identifiers, ensuring that recommendation ranking no longer relies solely on semantic similarity or statistical features, but is supported by a verifiable and referential set of clause evidence. Simultaneously, by combining the standard evolution status and the verifiable status of the evidence chain, dynamic risk control indicators are calculated to effectively identify evidence gaps caused by standard version updates, clause rearrangements, or changes in the scope of application. This fundamentally suppresses the chain-like distortion of "evidence-filling pseudo-compliance" induced by version drift, improving the stability and authenticity of matching results in a dynamic standard environment. This invention dynamically adjusts the recommendation ranking strategy and interpretation generation strategy under the drive of risk level. When the risk level increases, the proportion of the clause evidence set in the ranking weight is increased, and the granularity and display scope of the clause reference in the interpretation information are limited. When a preset high-risk state is reached, the generative clause number completion behavior is directly prohibited, so that the language completion capability of the interpretation generation module is rigidly constrained by calculable risk indicators. As a result, when the evolution of standards leads to an incomplete or uncertain chain of real evidence, the system prioritizes exposing evidence gaps rather than automatically filling them, avoiding the disguise of uncertain states as certain compliance evidence, and blocking the output of structured false evidence formed by generative completion from the mechanism level. This invention generates an audit traceability record containing elements such as standard version identifier, clause identifier set, risk level, explanation generation strategy status, and timestamp while outputting a list of recommended services and explanation information. It also establishes an association index between recommendation ranking and evidence chain, so that each recommendation result can be traced back to a specific standard version and clause basis. When the standard system further evolves or when post-event compliance review is conducted, consistency verification and reproduction verification can be performed based on the fixed version status and strategy status snapshot. This achieves traceability, reproducibility, and auditability of the entire process of recommendation, explanation, and risk control, significantly improving the engineering credibility and governance capabilities of the intelligent matching system in scenarios of dynamic standard evolution. Attached Figure Description

[0018] Figure 1 A schematic diagram of a system for intelligently matching the demand for standard technical services based on deep learning. Figure 2 This is a flowchart illustrating the intelligent matching method for standard technical service requirements based on deep learning, as described in this invention. Figure 3 This is a schematic diagram illustrating the process of constructing the evidence chain and generating the evidence set for the clauses of this invention. Detailed Implementation

[0019] Example 1: Refer to Figures 2 to 3 The intelligent matching method for standard technical service demands based on deep learning includes the following steps: Step 1: Acquire standard technical service demand data and technical service capability data, and establish a standard knowledge base for matching and an evidence index system for interpretation output based on the standard system; complete the basic data preparation and knowledge organization required for the matching task, forming a unified foundation for subsequent calculation and interpretation output. On the one hand, by acquiring standard technical service demand data and technical service capability data, demand-side expression and supply-side capability expression enter the same processing flow, ensuring that subsequent semantic representation, candidate calculation, and ranking interpretation have data sources; on the other hand, by establishing a standard knowledge base for matching based on the standard system, standard clauses and their structural relationships can be retrieved and calculated, thereby supporting matching elements such as the coverage of standard-related clauses; at the same time, establish an evidence index system for interpretation output, associating the original text of the clause, version identifier, hierarchical path, citation relationship, scope of application, change record, and verifiable links or hash verification information with the unique identifier of the clause, providing a locationable and verifiable source of evidence for subsequently constructing the clause evidence chain, forming the clause evidence set, and outputting traceable and reviewable interpretation information. This step ensures that the matching calculation and interpretation output are carried out within the same standard system and version context, avoiding discrepancies between the recommendation results and the interpretation information due to inconsistencies in standard versions or unclear clause positioning. In one specific implementation, to achieve structured collection and standardized storage of standard technical service demand data and technical service capability data, the following steps can be followed: A demand-side data collection process is constructed, which collects demand text, standard number and corresponding standard version, industry category, constraints and expected deliverables through online form filling interface or API on the demand side. The standard number and standard version are formatted and version valid. The industry category is standardized and coded through an industry classification mapping table. For example, when a company requests "to carry out safety evaluation services based on a certain current national standard", the standard number it fills in is automatically matched to the unique version identifier in the standard knowledge base and verified to be the current valid version. Construct a supply-side data collection process, which collects capability description text, qualification certificate numbers and validity periods, service scope descriptions, available resources, and past project examples from the supply side. It also compares the validity periods and verifies the authenticity of qualification certificates, and associates the service scope with standard clauses. For example, when a service provider uploads a certificate of a specific level of qualification, it verifies the consistency of the qualification number with the issuing authority's database, and extracts the standard clause reference information from its project examples and associates it with the unique clause identifier in the standard knowledge base. Field mapping, cleaning, noise reduction, and unified encoding are performed on demand-side and supply-side data. Text fields from different sources are mapped to a unified field structure, duplicate records are deduplicated, invalid symbols and noisy characters are cleaned up, and the data is converted using a unified character encoding and time format standard before being stored in the database. This ensures that the standard number, version identifier, industry domain code, and qualification number maintain a consistent format and searchability in the database, thereby providing a semantically consistent, structurally standardized, and traceable data foundation for subsequent deep semantic representation and candidate service set calculation.

[0020] In one specific implementation, to establish a standard knowledge base for matching based on a standard system, the following steps can be taken: The process involves collecting and organizing standard text and version information, as well as structural relationships. This includes acquiring standard text, version information, publication and implementation information within the target domain, and simultaneously collecting clause hierarchy and citation relationships. It also involves merging and recording the correspondence, replacement, and invalidation relationships between different versions of the same standard, and parsing external and internal citations within clauses to form a traceable citation relationship record. For example, when collecting current and historical versions of a national standard in parallel, the changes in clause numbers, additions, and deletions of clauses are recorded in the version relationship table, and other standard numbers referenced within the clauses are parsed into linkable citation nodes. The standard text is divided into chapters and sections and a unique clause identifier is generated. The standard text is divided into the smallest clause units according to the hierarchical rules of chapters, sections, articles, clauses and items. A unique clause identifier is generated for each clause. The unique clause identifier includes at least the standard number, version identifier, hierarchical path and clause position index to ensure the consistency of clause location across versions and standards. For example, when a standard clause is changed from Article 2 of Chapter 5 to Article 1 of Chapter 6 in a new version, the combination of hierarchical path and clause position index can still maintain the traceable mapping of the semantic unit of the same clause. The process involves clause cleaning, keyword and entity extraction, clause semantic vector calculation, and associated storage. Noise removal and format normalization are performed on the original clause text. Keywords and entities relevant to the standard domain are extracted, retaining entity types and contextual positions. Simultaneously, clause semantic vectors are calculated to represent the clause's semantic features. The clause's unique identifier, version identifier, original text, structural relationships, and semantic vectors are associated and stored to form a searchable standard knowledge base. For example, for clauses containing descriptions of detection conditions, judgment thresholds, and scope of application, entities such as detection objects, detection methods, and threshold parameters are extracted, and clause semantic vectors are calculated. This allows subsequent standard technical service requests to be precisely located based on the clause's unique identifier or to achieve semantic approximate matching based on the clause's semantic vector, thereby improving matching stability and cross-version consistency.

[0021] In one specific implementation, to establish an evidence index system for interpreting outputs based on a standard system, the following steps can be taken: The execution evidence index entries are constructed by creating index entries with the unique identifier of the clause as the primary key. For each index entry, the original text of the clause, version identifier, hierarchical path, citation relationship, scope of application, and change record are written. The hierarchical path is used to accurately mark the chapter position of the clause in the standard text. The citation relationship is used to record the clause's reference to other clauses or other standards. The change record is used to record the addition, deletion, replacement, and semantic adjustment information of the clause between different versions. For example, when parsing two versions of the same standard, the differences in the original text of the clause and the change of the clause number are written into the change record while keeping the unique identifier of the clause traceable. When constructing the evidence chain for each clause, the source standard, version, and location information are associated with each evidence item in the chain. The location information includes at least the page number, paragraph number, character range, or clause level path of the standard text to ensure that the interpretation output can point back to a unique location. At the same time, the citation relationship and scope of application are also included in the evidence item, so that the evidence item can not only be located, but also explain the applicable boundaries and the basis for citation. For example, when the interpretation information cites a clause as the basis for matching the scope of services, the scope of application field of that clause is also attached and its citation relationship to the superior clause is marked. Verifiable links or hash verification information are generated for each piece of evidence. Verifiable links point to the controlled storage location and corresponding segment of the standard text, while hash verification information is used to verify the consistency of the original clause or segment. The verifiable links or hash verification information are written into the evidence index entries and evidence items, enabling the explanatory information to be reviewed and verified by a third party after output. For example, when outputting explanatory information corresponding to the recommendation service, the explanatory information carries a unique clause identifier and version identifier, and the corresponding clause segment of the standard version can be directly located through the verifiable link. At the same time, the hash verification information verifies that the original clause has not been tampered with, thus making the explanatory information traceable and verifiable.

[0022] Step Two: Perform deep semantic representation on standard technical service demand data and technical service capability data respectively to obtain demand semantic vector representations and service semantic vector representations. Calculate a candidate service set based on these representations. This transforms heterogeneous, unstructured, or semi-structured text information into a computable, unified representation to support efficient and scalable similarity retrieval and recall. Deep semantic representation of standard technical service demand data yields demand semantic vector representations that characterize demand intent, industry domain, constraints, and deliverables. Deep semantic representation of technical service capability data yields service semantic vector representations that characterize capability scope, service boundaries, resource conditions, and project examples. Calculating a candidate service set based on these representations allows the system to quickly filter candidate services from the full set of service capabilities that are semantically similar to the demand or meet basic constraints. This reduces computational load and noise interference in subsequent ranking stages and provides a candidate range for further consideration of comprehensive factors such as standard clause coverage, qualification and constraint satisfaction, and historical delivery performance. This step serves as "semantic alignment + candidate recall," laying the foundation for subsequent fine-tuning and interpretation.

[0023] In one specific implementation, to achieve deep semantic representation of standard technical service demand data and technical service capability data, the following steps can be followed: The text preprocessing and field concatenation process is performed. The requirement text and service capability text are segmented into words to remove stop words, meaningless symbols and duplicate fragments. The structured fields such as standard number, version identifier, industry field, constraints, qualification certificates, service scope and project examples are concatenated and combined into a unified semantic input sequence according to a preset order. At the same time, the numerical constraints are standardized into intervals. For example, the detection cycle within three months is uniformly converted into a time interval identifier, so that the semantic input sequence has both text semantics and structured constraint expression. The dual-tower coding network performs independent coding processing. The preprocessed requirement text sequence is input into the first coding tower, and the preprocessed service capability text sequence is input into the second coding tower. The structural parameters of the two coding towers are independent of each other, but they adopt the same semantic space mapping rules. Through multi-layer semantic feature extraction and context association modeling, fixed-dimensional requirement semantic vectors and service semantic vectors are output respectively. The vectors are normalized to enhance the stability of similarity calculation between vectors. For example, when a requirement involves a specific industry field and a specific standard version, the first coding tower can strengthen the semantic features of the standard clauses, while the second coding tower can strengthen the semantic expression of the service capability coverage clauses. The training process involves performing contrastive learning or metric learning based on matching samples. This constructs positive sample pairs containing real matching relationships and negative sample pairs containing non-matching relationships. The objective function is optimized so that the distance between positive sample pairs and negative sample pairs in the vector space is smaller. A hard negative sample mining strategy is introduced during the training process to improve semantic discrimination ability. For example, service capabilities that belong to the same industry but do not meet the key standard terms are used as hard negative samples for training. This enables the dual-tower coding network to accurately distinguish between matching and non-matching relationships in complex semantic scenarios. Finally, a demand semantic vector and a service semantic vector that can effectively represent the semantic features of demand and service capabilities in a unified semantic space are obtained.

[0024] In one specific implementation, the calculation of the candidate service set based on the demand semantic vector representation and the service semantic vector representation can be carried out according to the following steps: The process of performing vector storage and index building involves storing the service semantic vectors corresponding to all technical service capabilities in association with the service identifier, and establishing an index structure oriented towards vector similarity retrieval. At the same time, service scope, validity period of qualification certificates, industry domain codes, standard version coverage, etc. are stored as filterable attributes to enable constraint filtering before and after vector retrieval. The process involves performing similarity retrieval and initial screening. For each standard technical service requirement, the semantic vector of the requirement is generated. The similarity between the requirement and the semantic vector of each service is calculated in the index structure. A preset number of candidate entries are recalled according to the similarity from high to low. A threshold gating strategy is used to remove low similarity entries. For example, when a requirement text contains a specific standard version and deliverable requirements, service capability entries in the semantic space that are closer to the semantic features of the standard clause are recalled first to avoid false recalls caused by keyword overlap. The process involves constraint fusion and candidate set finalization. Recalled items undergo secondary screening and expansion compensation based on constraints. Secondary screening includes verification of qualification certificate validity, service scope coverage, and industry domain consistency. Expansion compensation is used to relax non-critical constraints based on similarity or introduce synonymous domain mapping items when constraints are too strict, resulting in too few candidates. This ensures the candidate service set maintains semantic matching while meeting basic compliance constraints. For example, when a requirement demands specific qualifications and certificates and deliverables include an assessment report, items with valid qualifications and certificates and service scope covering assessment services are retained first. If the number is insufficient, non-critical descriptions of resource conditions are relaxed without relaxing qualification constraints, ultimately forming a candidate service set that can be used for subsequent matching, scoring, and recommendation ranking.

[0025] Step 3: Match, score, and rank the candidate service set. For each ranked recommended service, construct a corresponding clause evidence chain to obtain a clause evidence set for interpretation output. The clause evidence chain is a traceable evidence chain organized by multiple evidence items according to matching relationships, and the clause evidence set is the collection of clause evidence chains corresponding to the recommended service list. This achieves the transformation from "candidate recall" to "available recommendation results" and establishes a one-to-one correspondence between the recommendation results and the traceable clause evidence. Matching, scoring, and ranking the candidate service set ensures that each candidate service forms a comparable matching score under the influence of factors such as semantic matching of requirements, coverage of standard clauses, satisfaction of qualifications and constraints, and historical delivery effects, and outputs a recommended service list accordingly. Furthermore, for each ranked recommended service, construct a corresponding clause evidence chain so that the recommendation outputs not only the ranking result but also the corresponding standard basis. The clause evidence chain is formed by organizing multiple evidence items according to matching relationships. The evidence items originate from the evidence index system and carry the original clause text, version identifier, hierarchical path, citation relationship, scope of application, and positioning information, thus forming a traceable evidence chain. By compiling the chain of evidence corresponding to the recommended service list into a set of evidence, the interpretation output has a systematic form of evidence, which makes it easier to provide clear, verifiable and reviewable interpretation information for each recommended service, and also provides evidence input for subsequent risk level determination.

[0026] In one specific implementation, the following steps can be taken to match, score, and rank the candidate service set: Multi-dimensional feature quantification is performed. For each service item in the candidate service set, four basic indicators are calculated: semantic similarity, standard clause coverage, qualification and constraint satisfaction, and historical delivery effect. Semantic similarity is calculated based on the vector distance or angle similarity between the semantic vector of the demand and the semantic vector of the service. Standard clause coverage is obtained by statistically analyzing the coverage ratio and hierarchical matching depth between the unique identifier of the clause associated with the service capability text and the unique identifier of the clause involved in the demand. Qualification and constraint satisfaction is quantified according to rules after verifying the validity period of qualification certificates, consistency of service scope, and compliance with constraint conditions item by item. Historical delivery effect is normalized based on historical project evaluation, performance completion rate, and delivery cycle stability. For example, when a demand explicitly requires coverage of several key clauses of the current version of a standard, if the corresponding service can cover all key clauses and the qualification is within the validity period, its standard clause coverage and qualification satisfaction index scores will be high. The comprehensive matching score is calculated by normalizing the four basic indicators and then summing them according to the preset weight coefficients to form a comprehensive matching score. The weight coefficients can be dynamically adjusted according to the industry or risk level. For example, in areas with high compliance requirements, the weight of standard clause coverage and qualification satisfaction can be increased, and in areas with innovative technology requirements, the weight of semantic similarity and historical delivery effect can be increased, so that the comprehensive matching score can reflect the matching focus in different scenarios. The re-ranking process is performed based on a learning ranking model or rule-based strategy. Based on the comprehensive matching score, the candidate service items and their features are input into the learning ranking model for ranking optimization, or hierarchical ranking is performed according to a preset rule-based strategy. The learning ranking model is trained with historical real matching data so that service items with actual transactions or high satisfaction are displayed first in the ranking results. The rule-based strategy can set strong constraint logic such as direct downgrading if key terms are not covered or direct elimination if qualifications are not met. For example, when a candidate service has high semantic similarity but does not cover the key terms specified in the requirements, it is processed in descending order during the re-ranking stage. The process generates recommendation results and outputs scores. The re-ranked candidate service items are arranged into a recommendation list based on their comprehensive matching scores from highest to lowest. For each recommended item, the corresponding comprehensive matching score and sub-scores for each dimension are output to support the construction of subsequent explanatory information and risk assessment. At the same time, the weight parameters and ranking strategy status used for ranking are recorded so that the scoring basis can be traced during subsequent review or audit. For example, when outputting the top three recommended services, their semantic similarity scores, standard clause coverage ratios, qualification compliance, and historical delivery evaluation levels are also displayed, so that the recommendation ranking results have both a quantitative basis and a transparent and interpretable scoring structure.

[0027] In one specific implementation, constructing a corresponding chain of evidence for each ranked recommended service and forming a set of evidence for interpreting the output can be carried out according to the following steps: The process involves extracting key matching points from recommended services. For each recommended service after sorting, the service scope description, capability description text, project examples, and set of unique identifiers for associated terms are read one by one. At the same time, the key unique identifiers, constraints, and deliverable requirements of the demand side are aligned and parsed. Matching points that can support the establishment of a match are extracted and labeled with the point type. For example, consistent scope of application, coverage of key terms, and consistency of qualifications and deliverables are labeled as different points to maintain structural consistency when organizing evidence items later. The process involves generating evidence items and organizing evidence chains. At least one evidence item is generated around each matching point. Each evidence item includes a unique clause identifier, version identifier, hierarchical path, original clause text fragments, citation relationships, scope of application, change records, and a label indicating the correspondence between the evidence item and the matching point. Multiple evidence items are then organized into a clause evidence chain according to the matching relationships. The organization method can adopt a chain structure with key clause evidence items as the main body, cited clause evidence items as branches, and scope of application and change records as constraint annotations. For example, when a recommendation service is determined to cover a key clause in a current version of the standard, the key clause evidence item is used as the main node, and the terminology definition clauses and testing method clauses it cites are linked as branch nodes to form a traceable evidence chain. The execution clause evidence set aggregation and traceable encapsulation process binds each recommended service in the recommended service list to its clause evidence chain, forming a clause evidence set corresponding to the recommended service list. Each evidence item is supplemented with verifiable links or hash verification information to support review. Simultaneously, the matching point type, clause version selection criteria, and evidence item sorting rules used when generating the evidence chain are recorded. This allows the interpretation output to simultaneously display the corresponding evidence chain and support backtracking verification when showing the recommendation results. For example, when outputting the top three recommended services, each service is accompanied by a summary of its clause evidence chain and a list of unique clause identifiers. Users can trace back along the evidence chain to the specific clause's original text, version, and hierarchical position, and verify the consistency of the evidence item text through hash verification information, thus obtaining a traceable, verifiable clause evidence set that strictly corresponds to the recommendation ranking.

[0028] Step Four: Based on the standard evolution status and the verifiable status of the clause evidence set, calculate dynamic risk control indicators and determine the risk level; quantify the credibility and stability of the recommended output and interpretation output to provide a basis for subsequent dynamic control decisions. The standard evolution status reflects changes in the standard text and version information, embodying characteristics such as the frequency, magnitude, or applicability changes of clauses between different versions; the verifiable status of the clause evidence set reflects whether each piece of evidence in the clause evidence chain can be verified through verifiable links or hash verification information, and whether the evidence chain is complete, accurately positioned, and consistent in version—all verifiable elements. Calculating dynamic risk control indicators based on the standard evolution status and the verifiable status of the clause evidence set allows the risk assessment to simultaneously cover both the "evolutionary uncertainty of the standard itself" and the "verifiable reliability of the interpretation evidence," thus dynamically characterizing the risks of recommendation ranking and interpretation generation. The risk level determination results reflect the risk level of the current recommended service list and interpretation information at the traceability, verifiability, and reproducibility levels, providing clear triggering conditions and a basis for control intensity for subsequent strategy adjustments.

[0029] In one specific implementation, the calculation of dynamic risk control indicators based on the verifiable state of the standard evolution state and the evidence set of the clauses can be carried out according to the following steps: The system analyzes the evolution status of the execution standards and extracts and processes the evolution parameters of the clauses. It traces the version trajectory of each clause's unique identifier associated with the recommendation service, retrieves its release time, implementation time, repeal time, and replacement relationship, and calculates the historical change frequency, clause content change range, and clause number migration within a unit of time. It also determines whether the clause is currently in a valid state, in a transitional implementation state, or in a state about to be replaced by the current date, thereby generating clause evolution parameters. For example, if a key clause has been revised multiple times in the past three years and there is an implementation transition period, its historical change frequency is high and its current applicability is subject to phased constraints. The applicability of the enforcement clauses is quantified by converting the clause evolution parameters into calculable indicators and constructing an applicability scoring model. Clauses that are currently valid and have no replacement announcement are assigned higher applicability scores, while clauses that are in the process of being repealed or about to be replaced are assigned lower applicability scores. At the same time, deductions are made for situations where there are broken chains or version mismatches in cross-version citation chains. For example, when the version of a clause cited in a certain evidence chain has been replaced by a new version but has not been updated synchronously, it is downgraded in the applicability score. The execution clause evidence set verification status assessment process verifies the verifiable link validity, hash verification information consistency, matching consistency between the original clause fragment and the clause unique identifier, and the integrity of the evidence chain structure for each clause evidence chain in the clause evidence set. It assesses whether there are missing evidence items, broken citation relationships, or inconsistent clause versions, and quantifies the integrity and accuracy scores respectively. For example, when the clause evidence chain of a certain recommendation service covers all key clauses and the hash verification information of each evidence item is consistent and the citation path is continuous, the integrity and accuracy scores are high. The dynamic risk control indicator generation process unifies and normalizes the historical change frequency of clauses, current applicability score, evidence chain integrity score, and evidence chain accuracy score. It then weights and integrates these factors based on timeliness and relevance. Timeliness strengthens the impact of recently revised clauses on risk, while relevance strengthens the weight of key clauses in the overall matching process. This results in a dynamic risk control indicator that serves as a risk warning when displaying recommendation results. For example, if a recommendation service relies on key clauses that have been frequently revised recently and there is version lag in the evidence chain, the dynamic risk control indicator will significantly increase, prompting a need for manual review or re-matching. This allows for dynamic quantitative control of the uncertainty of standard evolution and the verification status of the evidence chain.

[0030] In one specific implementation, to determine the risk level based on dynamic risk control indicators, the following steps can be followed: The risk level mapping rule establishment process is implemented, and the dynamic risk control indicators are normalized to a unified dimension and divided into multiple continuous intervals. Each interval is configured with a risk level and triggering conditions. The triggering conditions include at least the key clause applicability threshold, the evidence chain integrity threshold, and the verification consistency threshold. At the same time, a risk amplification factor is introduced to raise the level when the key clause has been frequently revised or a version replacement announcement. For example, when the dynamic risk control indicator is in the medium interval but the key clause applicability is below the threshold, it is directly raised to a higher risk level. The process involves risk level assessment and handling labeling. For each recommended service, the dynamic risk control indicators are matched within a range and the risk level is output. At the same time, handling labels are generated based on triggering conditions. The handling labels include at least suggestions for review, suggestions for updating the evidence chain, suggestions for re-matching, or allowing automatic approval. The key factors that led to the assessment are recorded for explanation and output. For example, if the evidence chain hash verification of a recommended service is consistent but there are outdated versions of key clauses and frequent recent revisions of the clauses, it is assessed as a high-risk level and the evidence chain is suggested to be updated before the recommendation result is output.

[0031] Dynamic risk control indicators Standard evolution factor Consistency factor with evidence verification Together they constitute: ;in: ; , representing the standard evolution factor; , representing the evidence verification consistency factor; Let be the weighting coefficient, satisfying . , This is used to characterize the relative contributions of "standard evolution risk" and "evidence verification uncertainty" to the overall risk. Following existing weighted scoring / risk assessment practices, the weights can be dynamically adjusted according to industry scenarios and risk levels. In this embodiment, standard evolution has a more fundamental and broader impact on the applicability of clauses and the effectiveness of the chain of events; therefore, α=0.6 and β=0.4 are preferred. Alternatively, the weights can be optimized by combining historical review results / false positive / false negative rates.

[0032] Standard evolution factor It is obtained by weighting the following sub-factors: ;in: For version change frequency factor, As a factor for the magnitude of changes in the clause structure, This is an applicability variation factor. , , This method is used to characterize the relative contributions of version change frequency, clause structure changes, and applicability changes to the risk of standard evolution. Following the conventional practice of multi-factor risk assessment and weighted fusion in existing technologies, the weights of each sub-factor are typically set empirically or calibrated based on their impact on the results and stability. In this embodiment, version change frequency has a more direct leading indicator effect on the overall evolution trend, and therefore is assigned a higher weight of 0.4; clause structure changes and applicability changes have a supplementary impact on risk, and are each set to 0.3. These weights can also be adaptively adjusted based on historical sample statistics or model validation results.

[0033] Version change frequency factor : ; in, This represents the number of revisions to the target standard (or the set of target standards related to the recommendation) within the most recent M months. As a frequency reference value, this embodiment preferably uses it. For example, if a certain standard Revised within a month Next, then .

[0034] Clause Structure Variation Factor : ;in, This represents the number of clauses that have undergone structural adjustments such as rearrangement, numbering changes, splitting, or merging in the current version (the differences can be counted based on the clause's unique identifier). The total number of clauses. For example, if There is in the article If the item number changes, then .

[0035] Applicability variation factor If the terms mentioned in the recommendation are in the current version: ; ; ; .

[0036] Evidence verification consistency factor Calculation method: In this embodiment, the evidence verification consistency factor V is defined as the evidence reliability score. , ;in: For the completeness ratio of the chain of evidence, This represents the hash consistency ratio. This is the proportion of version consistency. Preferred weight settings. , , This is used to quantify the relative importance of the three sub-factors (chain of evidence integrity, hash consistency, and version consistency) in the evidence verification consistency factor. These weights are set according to standard practices for assessing evidence reliability in existing technologies, ensuring that standard evolution and chain of evidence verification are reasonably reflected under different risk scenarios. Based on technical requirements, changes caused by standard evolution and evidence verification information are crucial to the overall risk assessment, with chain of evidence integrity and hash consistency typically given priority, hence their higher weights. Version consistency has a slightly lower impact on risk assessment, therefore it is assigned a smaller weight. The three factors are relatively balanced to meet the risk control needs in practical applications.

[0037] Completeness of the chain of evidence : ;in, To explain the number of key terms required, This represents the number of successfully verified terms. (Hash consistency) : ;in, The number of evidence items that passed the hash check. Version consistency. : ;in, The number of evidence items consistent with the current valid standard version.

[0038] The preferred risk classification is as follows: 1. Risk value R range: Risk level: Low risk (L), Explanation: Evidence is complete and standards are stable; 2. Risk value R range: Risk level: Medium risk (M), Explanation: There are some evolutionary or verification gaps; 3. Risk value R range: Risk level: High risk (H1), indicating a significant decrease in the reliability of the evidence; 4. Risk value R range: Risk level: High risk (H2), Explanation: There is a gap in evidence or a major version drift; Step 5: Based on the risk level, dynamically adjust the service recommendation ranking strategy and explanation generation strategy to obtain a dynamically adjusted list of recommended services and corresponding explanation information. This dynamic adjustment includes at least: increasing the weight of the clause evidence set in the ranking and limiting the granularity of clause citation in the explanation information when the risk level increases; and prohibiting the generation of clause number completion when the risk level reaches a preset high-risk state. Under different risk levels, adaptively adjust the output method of recommendations and explanations using the risk level as a control variable to ensure that the output meets both matching requirements and the requirements of credible explanation and compliant controllability. Dynamically adjusting the service recommendation ranking strategy based on the risk level ensures that the ranking is based not only on factors such as semantic similarity or historical effects, but also on increasing the weight of the clause evidence set in the ranking when the risk level increases. This encourages recommendations to favor services with more sufficient standard clause basis, more complete evidence chains, and more reliable verifiable status. Dynamically adjusting the explanation generation strategy based on the risk level ensures that the presentation and scope of explanation information change with risk, limiting the granularity of clause citation in the explanation information when the risk level increases, thereby reducing the risk of miscitation, incorrect citation, or cross-version deviation caused by overly detailed citations. Furthermore, when the risk level reaches a preset high-risk state, the generational completion of clause numbers is prohibited. This ensures that explanatory information does not generate clause numbers in high-risk situations, preventing the inclusion of clause numbers that cannot be verified by the evidence indexing system. This mechanism reduces the risk of unverifiable or unreviewable explanatory outputs. Through this step, the recommendation ranking and explanatory output are linked and controlled, enabling the system to output more stable, reliable, and auditable recommended service lists and explanatory information under different risk scenarios.

[0039] In one specific implementation, to dynamically adjust the service recommendation ranking strategy and explanation generation strategy according to the risk level, and obtain a dynamically adjusted list of recommended services and corresponding explanation information, the following steps can be taken: The risk level-driven strategy parameter loading process maps the risk level of each recommended service to executable ranking and interpretation parameters. The ranking parameters include at least semantic similarity weight, standard clause coverage weight, qualification and constraint satisfaction weight, historical delivery effect weight, and clause evidence set weight. The interpretation parameters include at least clause citation granularity level, set of displayable evidence item types, range of displayable version information, and verification information presentation method. A constraint rule is set that monotonically adjusts as the risk level increases, so that the higher the risk level, the higher the weight of the clause evidence set and the coarser the granularity of the interpretation information clause citation. The ranking weight is dynamically reallocated and re-ranked. Based on the comprehensive matching score calculated for candidate services, the clause evidence set score is introduced as an incremental item. The clause evidence set score is composed of the completeness of the evidence chain, the accuracy of the evidence chain, the verifiable consistency, and the sufficiency of evidence covering key clauses. The weight of this incremental item is increased when the risk level increases. At the same time, a penalty factor is applied to services with missing evidence chains or inconsistent versions to lower their ranking position. For example, when a service has high semantic similarity but has a broken evidence chain and the version of the key clause is outdated, its ranking is lowered under medium or high risk conditions. Services with complete evidence chains and verifiable consistency are given priority. The execution of interpretation generation strategy convergence and clause citation granularity control processing, when constructing interpretation information for each recommended service based on interpretation parameters, allows the display of clause-level paths and original clause fragments with multi-evidence item chain explanations under low-risk conditions. Under medium-risk conditions, the granularity of clause citation is limited to clause-level summaries and key evidence item indexes, only displaying the unique identifier and version identifier of clauses directly related to the matching points and weakening non-key citation branches. Under high-risk conditions, it is further limited to citations and verification conclusion summaries at the clause group level or clause topic level to avoid outputting overly detailed clause content that could lead to misuse. At the same time, when the risk level reaches the preset high-risk state, generative clause number completion is prohibited. Specifically, it does not allow inferring missing clause numbers based on context, does not allow automatic completion of suspected clause numbers, and only allows citation of clause unique identifiers and original number fields that have been verified to exist in the evidence chain and have consistent hashes. The process involves dynamically controlled result encapsulation and consistency verification. The reordered list of recommended services is bound to the explanatory information generated by granular control, and the output is then subjected to consistency verification. This verification ensures that at least the unique identifiers of the clauses appearing in the explanations exist in the corresponding evidence sets and have passed verification; that the explanations do not contain any prohibited generative clause number completion traces; and that the mapping relationship between the weights used in the sorting and the risk level is traceable. For example, when a recommended service is determined to be in a high-risk state, the output explanation only provides the conclusion of the coverage of the key clause group, whether the evidence chain has passed verification, and a prompt that the relevant version is in a recent revision. It also clearly lists the set of verified unique identifiers of the clauses without completing the missing numbers. This results in a list of recommended services that is more biased towards sufficient evidence and verifiable consistency in high-risk scenarios, along with explanatory information that corresponds strictly to it and has controlled granularity.

[0040] After determining the risk level, this embodiment implements a graded control strategy.

[0041] The overall recommended score is: ;in, For semantic matching, The reliability of the evidence is scored. It is used to characterize the semantic similarity between standard technical service requirements and candidate technical service capabilities, preferably obtained based on the similarity calculation between the requirement semantic vector and the service semantic vector. This is used to characterize the reliability of the chain / set of evidence for the terms corresponding to the recommendation service in terms of "traceability, verifiability, and reproducibility".

[0042] The optimal weighting based on risk is as follows: 1. Risk Level: L 0.7 0.3; 2. Risk level: M 0.5 0.5; 3. Risk level: H1 0.3 0.7; 4. Risk level: H2 0.1 It is 0.9; The granularity of interpretation is controlled according to the risk level, with the following preferred options: 1. Risk level: L, Interpretation strategy: Allow clause number + original text citation; 2. Risk level: M, Interpretation strategy: Allow clause number + summary description; 3. Risk level: H1, Interpretation strategy: Only allow clause unique identifier + summary; 4. Risk level: H2, Interpretation strategy: Prohibit the display of clause number, only output risk warnings such as "Evidence requires manual review"; Prohibit the generation of clause number completion mechanism: When the risk level reaches a preset high-risk state (e.g., risk level...) When interpreting, the following mandatory rules apply: Standard version identifiers and clause identifiers in the interpretation must be generated from structured fields of the clause evidence chain; the model-generated text must not contain clause numbers not in the evidence chain; a clause identifier whitelist verification is performed during the output phase. If a non-whitelist clause is detected, the output will be automatically rejected, or downgraded to only outputting risk warning information. This is the set of clause identifiers parsed from the output text; A set of identifiers for the chain of evidence clauses; Structured reference generation mechanism: The interpretation output adopts a template rendering mechanism, preferably including: fields such as clause number and version number are filled by the system according to the structured fields of the clause evidence chain; the model only generates interpretive language content; consistency verification is performed on all clause identifiers and evidence item fields in the interpretation output; if the verification fails, the audit log is recorded and the corresponding result is entered into the manual review queue.

[0043] Through the above mechanism, when the standard version drift causes a gap in evidence, the system no longer automatically completes the clause number through the language model, but explicitly exposes the risk and limits the granularity of interpretation, thereby structurally suppressing the chain distortion of "evidence completion-type pseudo-compliance".

[0044] Step Six: Output the recommended service list and explanatory information, and generate an audit trail record containing standard version identifiers, clause identifiers, risk levels, and the status of the explanation generation strategy. This completes the final delivery to users or business systems and implements a traceable and auditable record-keeping mechanism. Outputting the recommended service list and explanatory information allows users to obtain the sorted recommended services and their underlying explanations, supporting the decision-making and implementation of standard technical service requirements. Generating an audit trail record containing standard version identifiers, clause identifiers, risk levels, and the status of the explanation generation strategy ensures that each output can be traced back to the corresponding standard version context and clause basis, recording the risk level and explanation generation strategy status at the time. This allows for tracking of "the standard version used, the cited clause identifier, the risk level judgment result, and the status of the explanation generation strategy" during subsequent reviews, dispute resolution, compliance audits, or model iterations. This step enables the recommended output to have traceable and verifiable evidentiary capabilities, forming a closed loop with the evidence index system, clause evidence chain, and risk control.

[0045] In one specific implementation, to output a list of recommended services and explanatory information, and to generate an audit traceability record containing a standard version identifier, a clause identifier, a risk level, and the status of the explanation generation strategy, the following steps can be followed: The process involves compiling the recommended results and assembling explanatory information. The dynamically adjusted list of recommended services is output in sorted order, and each recommended service is equipped with its corresponding explanatory information. The explanatory information includes at least the comprehensive matching score, the verification conclusion of the clause evidence chain, the conclusion of the key clause coverage, and the risk warning. At the same time, the display granularity and the types of evidence items that can be displayed are controlled according to the status of the explanation generation strategy. For example, when a recommended service is at a medium risk level, only the clause identifier, standard version identifier, and evidence item index that are directly related to the key clauses of the requirement are displayed, and non-key reference branches are not expanded. The process involves collecting and generating audit traceability elements for each recommended service in the recommended service list. These elements are then written into the audit traceability record. Each audit traceability element includes at least a standard version identifier, a set of clause identifiers, a risk level, an interpretation generation strategy status, a ranking weight status, an evidence chain verification summary, a generation timestamp, and a verifiable consistency check result. Consistency checks are performed between the clause identifier and the standard version identifier to ensure that interpretation references can be verified. For example, if the evidence chain for a recommended service shows that the standard version is a current version and the clause identifier covers several key clauses, the audit traceability record is simultaneously written to the version identifier and clause identifier list, and the interpretation generation strategy status is written as medium-risk with limited granularity. The process involves encapsulating and verifying traceability records, establishing an association index between audit traceability records and the recommended service list, and outputting a verifiable carrier. This verifiable carrier supports backtracking queries based on recommendation order, standard version identifier, or clause identifier. It also solidifies the risk level determination criteria and strategy status snapshots to ensure consistency in reproducibility. For example, when a subsequent reviewer queries based on a clause identifier, they can directly locate the corresponding recommended service, find that the current risk level is high-risk, and that the interpretation generation strategy status is "prohibited generation clause number completion." Furthermore, it links back to the evidence chain verification summary, thus achieving full-process traceability, verifiability, and auditability for both recommendation output and interpretation output.

[0046] Example 2: Refer to Figure 1 A deep learning-based intelligent matching system for standard technical service demands includes: The data acquisition module is used to acquire standard technical service requirement data and technical service capability data; The library construction module is used to build a standard knowledge base and evidence indexing system based on the standard system. The semantic representation module is used to perform deep semantic representation on standard technical service demand data and technical service capability data respectively, and generate demand semantic vector representation and service semantic vector representation. The candidate computation module is used to compute a set of candidate services based on the demand semantic vector representation and the service semantic vector representation; The ranking and interpretation module is used to match, score, and rank the candidate service set, construct a chain of evidence for the terms to obtain a set of evidence for the terms, and generate explanatory information corresponding to the list of recommended services. The risk control module is used to calculate dynamic risk control indicators and determine risk levels based on the standard evolution status and the verifiable status of the evidence set of the clauses. It dynamically controls the service recommendation ranking strategy and interpretation generation strategy according to the risk level, outputs the recommended service list and interpretation information, and generates audit traceability records containing standard version identifiers, clause identifiers, risk levels and interpretation generation strategy status.

[0047] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0048] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent matching of standard technical service requirements based on deep learning, characterized in that: Includes the following steps: Step 1: Obtain standard technical service demand data and technical service capability data, and establish a standard knowledge base and evidence index system; Step 2: Semantically represent the demand data and capability data, and determine the candidate service set based on semantic similarity; Step 3: Score and rank the candidate service set, and construct a corresponding chain of evidence for each recommended service after ranking to form a set of evidence for the terms; Step 4: Calculate dynamic risk control indicators and determine risk levels based on the verifiable status of the standard evolutionary state and the evidence set of the clauses. The dynamic risk control indicators shall include at least the evolutionary factor and the verification consistency factor. Step 5: Dynamically adjust the recommendation ranking strategy and explanation generation strategy according to the risk level, and output the recommended service list and explanation information. Among them: the standard version identifier and clause identifier in the explanation information are generated / rendered by the structured fields of the clause evidence chain and only the verified evidence items are allowed to be cited; when the risk level increases, the weight of the clause evidence set in the ranking is increased and the granularity of clause citation in the explanation is restricted; when the risk level reaches the preset high-risk state, it is prohibited to infer the generation of missing clause identifiers, and the clause identifier whitelist verification is performed on the explanation output to ensure that the clause identifiers appearing in the explanation belong to the corresponding clause evidence chain. Step Six: Generate an audit trail record that includes a standard version identifier, clause identifier, risk level, and explanation of the generation strategy status.

2. The intelligent matching method for standard technical service requirements based on deep learning according to claim 1, characterized in that, Acquiring requirement and capability data includes: collecting requirement text, standard number / version, industry field, constraints and deliverables, as well as capability descriptions, qualifications and licenses, service scope, resources and project examples, and then performing field mapping, cleaning and noise reduction and unified encoding before storing them in the database.

3. The intelligent matching method for standard technical service requirements based on deep learning according to claim 1, characterized in that, Establishing a standard knowledge base includes: collecting standard text and version information and clause hierarchy / reference relationships; segmenting by chapter and clause and generating unique clause identifiers; extracting keywords and entities from clauses and calculating clause semantic vectors; and associating and storing clause unique identifiers, version identifiers, original clause text, structural relationships, and clause semantic vectors.

4. The intelligent matching method for standard technical service requirements based on deep learning according to claim 1, characterized in that, Establishing an evidence indexing system includes: using unique clause identifiers to index the original text of the clauses, version identifiers, hierarchical paths, citation relationships, scope of application, and change records; and associating source standards / versions and location information for evidence items, generating verifiable links or hash verification information.

5. The intelligent matching method for standard technical service requirements based on deep learning according to claim 1, characterized in that, Semantic representation includes: preprocessing the demand text and capability text and then inputting them into a dual-tower encoding network to encode the demand semantic vector and service semantic vector respectively. The dual-tower encoding network is trained based on matching samples through contrastive learning or metric learning.

6. The intelligent matching method for standard technical service requirements based on deep learning according to claim 1, characterized in that, The scoring and ranking process includes: calculating a comprehensive matching score based on semantic similarity, coverage of standard terms, satisfaction of qualifications and constraints, and historical delivery performance, and re-ranking candidate services based on a learning ranking model or rule-based strategy.

7. The intelligent matching method for standard technical service requirements based on deep learning according to claim 1, characterized in that, Constructing a chain of evidence for a clause includes: retrieving the original text, version identifier, and hierarchical path of the clause from the evidence index system based on the unique identifier of the clause involved in the matching, organizing the chain of evidence for the clause according to the matching relationship, and at the same time as associating the clause citation relationship, scope of application, and location information.

8. The intelligent matching method for standard technical service requirements based on deep learning according to claim 1, characterized in that, The calculation of dynamic risk control indicators includes: calculating the frequency of historical changes and current applicability of clauses based on the standard evolution status; assessing the completeness and accuracy of the clause evidence chain based on the verification information of the clause evidence set; and generating dynamic risk control indicators by weighting them according to timeliness and relevance factors.

9. A deep learning-based intelligent matching system for standard technical service requirements, applied to the deep learning-based intelligent matching method for standard technical service requirements as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire standard technical service requirement data and technical service capability data; The library construction module is used to build a standard knowledge base and evidence indexing system based on the standard system. The semantic representation module is used to perform deep semantic representation on standard technical service demand data and technical service capability data respectively, and generate demand semantic vector representation and service semantic vector representation. The candidate computation module is used to compute a set of candidate services based on the demand semantic vector representation and the service semantic vector representation; The ranking and interpretation module is used to match, score, and rank the candidate service set, construct a chain of evidence for the terms to obtain a set of evidence for the terms, and generate explanatory information corresponding to the list of recommended services. The risk control module is used to calculate dynamic risk control indicators and determine risk levels based on the standard evolution status and the verifiable status of the evidence set of the clauses. It dynamically controls the service recommendation ranking strategy and interpretation generation strategy according to the risk level, outputs the recommended service list and interpretation information, and generates audit traceability records containing standard version identifiers, clause identifiers, risk levels and interpretation generation strategy status.

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