HBS database construction method using thinking chain and snowball method

Abstract

The invention discloses an HBS database construction method using a thinking chain and a snowball method, and belongs to the technical field of digital signatures, and the method comprises the following steps: extracting structured information of a first type of related academic literature and a second type of related academic literature, and obtaining a first data set; performing literature expansion on the seed literature by using a snowball rolling method to obtain an expanded academic literature set; extracting structured information of the expanded academic literature set based on a preset thinking chain prompt template to obtain a second data set; extracting candidate relation pairs of each academic literature and relation types of the candidate relation pairs; performing automatic logic consistency check and modification on the second data set based on the first data set to obtain a modified second data set; and constructing an HBS database based on the first data set, the modified second data set, the candidate relation pair of each academic literature and the relation type of the candidate relation pair. According to the method, HBS domain knowledge can be integrated, and the evolution relation of the HBS algorithm can be effectively mined.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital signature, and more particularly to a HBS database construction method using thought chain and snowball method. BACKGROUND

[0002] At present, the database construction of the existing HBS (Hash-based digital signature) algorithm can be mainly divided into two categories: traditional manual sorting method and automatic extraction method. The traditional manual sorting method relies on domain experts to manually annotate algorithm features, which can ensure accuracy, but has significant defects such as low efficiency. For example, the cryptographic algorithm validation program (CAVP) and the cryptographic module validation program (CMVP) of the National Institute of Standards and Technology (NIST) require experts to continuously participate in maintenance, which ensures high accuracy. However, with the rapid development of cryptographic algorithm research, this manual method is difficult to effectively cope with the explosive growth of literature, leading to problems such as database update lag and incomplete coverage. Therefore, many scholars have turned to using automatic extraction technology to collect algorithm performance data. For example, the ECRYPT symmetric cipher benchmark test (eBACS) project collects algorithm performance data through automated testing, and the SUPERCOP tool developed by Bernstein et al. realizes the automatic performance evaluation of encryption primitives.

[0003] However, the existing automatic method has improved processing speed, but still faces major challenges in constructing a comprehensive HBS algorithm database. On the one hand, there is a lack of domain-specific knowledge. The existing automatic method relies on general extraction technology and does not integrate cryptographic professional knowledge, resulting in poor performance in algorithm feature correlation analysis (such as the difference in tree structure between XMSS and SPHINCS+) and cross-document conflict resolution (such as the contradictory expressions of WOTS+ parameter definitions in different papers). On the other hand, it is difficult to effectively mine the evolution relationship between algorithms. The existing technology cannot effectively identify and represent the derivation, improvement and optimization relationship between algorithms, which is crucial for understanding the details of cryptographic algorithms.

[0004] Therefore, how to provide a HBS database construction method that can integrate HBS domain knowledge and effectively mine the evolution relationship of HBS algorithms is a problem that needs to be solved by those skilled in the art. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a HBS database construction method using thought chain and snowball method.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] In a first aspect, a HBS database construction method using a thought chain and a snowball method is provided, comprising the following steps:

[0008] Based on the preset thought chain prompt template, structured information of the first type of related academic literature and the second type of related academic literature is extracted to obtain a first data set; wherein the first type of related academic literature is a basic academic article of the HBS algorithm; and the second type of related academic literature is a standard file of the HBS algorithm;

[0009] The snowball method is used to expand the seed literature to obtain an expanded academic literature set; wherein the seed literature is the first type of related academic literature involved in the third type of related academic literature; and the third type of related academic literature is a review article of the HBS algorithm;

[0010] Based on the preset thought chain prompt template, structured information of the expanded academic literature set is extracted to obtain a second data set;

[0011] Each academic literature in the first type of related academic literature, the second type of related academic literature, the third type of related academic literature and the expanded academic literature set is extracted to obtain a candidate relationship pair and a relationship type of the candidate relationship pair;

[0012] Based on the first data set, the second data set is automatically checked and modified for logical consistency to obtain a modified second data set;

[0013] Based on the first data set, the modified second data set, the candidate relationship pair of each academic literature and the relationship type of the candidate relationship pair, a HBS database is constructed.

[0014] Preferably, the preset thought chain prompt template includes algorithm basis information identification, mathematical basis analysis, proposed technical details, performance characteristic analysis, security attribute evaluation, algorithm relationship mapping, application scenario identification and special attribute description.

[0015] Preferably, the basic academic article of the HBS algorithm includes Lamport-OTS algorithm, WOTS algorithm, WOTS+ algorithm, WOTS-PRF algorithm, MSS algorithm, XMSS algorithm, LMS algorithm, SPHINCS algorithm, SPHINCS-Simpira algorithm, Gravity-SPHINCS algorithm and SPHINCS+ algorithm.

[0016] The standard file of the HBS algorithm includes standard document RFC 8554, standard document RFC 8391, standard document NIST SP 800-208 and standard document NIST FIPS 204.

[0017] The review article of the HBS algorithm includes the role of hash-based signatures in quantum-secure IoT: current solutions and future directions and a review of post-quantum cryptography: techniques, challenges, and standardization.

[0018] Preferably, the extended academic literature set is obtained based on the following formula:

[0019] ;

[0020] Wherein, i = 1, 2,..., n; n represents the number of rounds of expansion; S represents the extended academic literature set; F i represents the academic literature obtained by forward expansion in the i-th round; B i represents the academic literature obtained by backward expansion in the i-th round; F1 is the reference literature of the seed literature; B1 is the literature referring to the seed literature; F i+1 is the reference literature of F i ; B i+1 is the literature referring to B i .

[0021] Preferably, the expression of the candidate relationship pair is:

[0022] ;

[0023] Wherein, RC(d) represents the candidate relationship pair extracted in the academic literature d; d represents each academic literature in the first type of related academic literature, the second type of related academic literature, the third type of related academic literature and the extended academic literature set; A i represents the source algorithm in the academic literature d; A j represents the target algorithm in the academic literature d; A represents the HBS algorithm in all academic literatures in the first type of related academic literature, the second type of related academic literature, the third type of related academic literature and the extended academic literature set; c ij represents the confidence score of the relationship between A i and A j , and the candidate relationship pair RC(d) is retained when c ij is greater than a preset threshold γ.

[0024] Preferably, the expression of the relationship type of the candidate relationship pair is:

[0025] ;

[0026] ;

[0027] Wherein, represents the relationship type of the candidate relationship pair RC(d); This represents the probability that candidate relation pair RC(d) belongs to relation type t, based on a set of academic documents. Description A i and A j The frequency of the words extends, improves, and based on in academic literature was obtained; The set of academic documents consists of the first type of related academic documents, the second type of related academic documents, the third type of related academic documents, and the extended set of academic documents; T represents the set of relation types; Predecessor represents the predecessor relation, Extension represents the extension relation, Improvement represents the improvement relation, Component represents the component relation, and Variant represents the variant relation.

[0028] Preferably, the second dataset is subjected to automated logical consistency checks and modifications based on the first dataset to obtain the modified second dataset, including the following steps;

[0029] Source identification algorithm A i Check if the first matching relation in the second dataset is the same as the first matching relation in the first dataset; if they are different, then the source algorithm A will be modified. i The first matching relationship in the second dataset is modified to the original algorithm A. i The first matching relationship in the first dataset; wherein, the first matching relationship is the source algorithm A. i The matching relationship between security level and key length; the security level and key length are contained in the proposed technical details extracted from the preset thought chain prompt template;

[0030] Source identification algorithm A i Check if the second matching relationship in the second dataset is the same as the second matching relationship in the first dataset; if they are different, then the source algorithm A will be modified. i The second matching relationship in the second dataset is modified to the source algorithm A. i The second matching relationship in the first dataset; wherein, the second matching relationship is the source algorithm A. i The matching relationship between tree height and signature size; tree height and signature size are contained in the proposed technical details extracted from the preset mind chain prompt template;

[0031] Source identification algorithm A i Winternitz parameters in the second dataset Does it meet the requirements? If the conditions are not met, then the source algorithm A will be... i The Winternitz parameter in the second dataset is modified to match the original algorithm A. iThe Winternitz parameter in the first data set, wherein the Winternitz parameter is contained in the proposed technical details extracted by the preset thought chain prompt template.

[0032] Preferably, the HBS database comprises a SignatureAlgorithm class, a HashFunction class, an AlgorithmRecord class and an AlgorithmRelationship class.

[0033] The SignatureAlgorithm class is used to store and manage the basic information of various HBS algorithms, wherein the SignatureAlgorithm class and the HashFunction class are in a one-to-many relationship.

[0034] The HashFunction class is used to record different hash functions.

[0035] The AlgorithmRecord class is used to provide complete performance characteristics and scenario requirement characteristics for each combination of HBS algorithms and hash functions; wherein the combination of algorithmID in the SignatureAlgorithm class and hashFunctionID in the HashFunction class constitutes a composite key, which uniquely identifies each record in the HBS database.

[0036] The AlgorithmRelationship class is used to store the relationship between various HBS algorithms.

[0037] Preferably, the SignatureAlgorithm class, the HashFunction class, the AlgorithmRecord class and the AlgorithmRelationship class are managed as data models of a relational database mysql, structured records are created for each data model, and a unified storage format is designed.

[0038] In a second aspect, an electronic device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the HBS database construction method using thought chains and snowball method according to the first aspect.

[0039] According to the above technical solution, compared with the prior art, the present application provides an HBS database construction method using thought chains and snowball method, which can integrate HBS field knowledge and effectively mine the evolution relationship of HBS algorithms. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only aim at the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0041] Figure 1 A flowchart of the HBS database construction method using the thought chain and the snowball method provided in one or more embodiments of the present application;

[0042] Figure 2 A schematic diagram of an electronic device provided in one or more embodiments of the present application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0044] In a first aspect, as shown in the accompanying drawings, Figure 1 The embodiments of the present application disclose an HBS database construction method using a thought chain and a snowball method, comprising the following steps:

[0045] Extracting structured information of the first type of related academic literature and the second type of related academic literature based on a preset thought chain prompt template to obtain a first data set; wherein the first type of related academic literature is a basic academic article of the HBS algorithm; and the second type of related academic literature is a standard file of the HBS algorithm;

[0046] In an embodiment, the basic academic article of the HBS algorithm includes Lamport-OTS algorithm, WOTS algorithm, WOTS+ algorithm, WOTS-PRF algorithm, MSS algorithm, XMSS algorithm, LMS algorithm, SPHINCS algorithm, SPHINCS-Simpira algorithm, Gravity-SPHINCS algorithm and SPHINCS+ algorithm;

[0047] The standard file of the HBS algorithm includes standard document RFC 8554, standard document RFC 8391, standard document NIST SP 800-208 and standard document NIST FIPS 204;

[0048] In a certain embodiment, the preset thinking chain prompt template includes algorithm basis information identification, mathematical basis analysis, proposed technical details, performance characteristic analysis, security attribute evaluation, inter-algorithm relationship mapping, application scenario identification, and special attribute description.

[0049] It can be understood that, taking the LMS algorithm as an example, the structured information extracted by the preset thinking chain prompt template includes publication time (1995), standardization time (2019 - RFC8554), author (Leighton, Micali), core mathematical basis (collision resistance of cryptographic hash function), key operation (hash operation), technical details (including key generation, signature generation and verification steps), performance characteristics, security attributes (such as existential unforgeability), and its relationship with variants such as HSS, etc.

[0050] The seed literature is the first type of related academic literature involved in the third type of related academic literature, and the third type of related academic literature is a review article of the HBS algorithm;

[0051] In a certain embodiment, the review article of the HBS algorithm includes the role of hash-based signatures in quantum secure Internet of Things: current solutions and future directions and post-quantum cryptography: a review of technology, challenges, and standardization.

[0052] In a certain embodiment, the expanded academic literature set is obtained based on the following formula:

[0053] ;

[0054] Wherein, i = 1, 2,..., n; n represents the number of expansion rounds; S represents the expanded academic literature set; F i represents the academic literature obtained by forward expansion in the i th round; B i represents the academic literature obtained by backward expansion in the i th round; F1 is the reference literature of the seed literature; B1 is the literature referring to the seed literature; F i+1 is the reference literature of F i ; B i+1 is the literature referring to B i .

[0055] Based on the preset thinking chain prompt template, the structured information of the expanded academic literature set is extracted to obtain a second data set;

[0056] Extract candidate relation pairs and relation types of each academic document from the first type of related academic documents, the second type of related academic documents, the third type of related academic documents, and the expanded set of academic documents;

[0057] In one embodiment, the expression for the candidate relation pair is:

[0058] ;

[0059] Wherein, RC(d) represents the candidate relation pairs extracted from academic literature d; d represents each academic literature in the first type of related academic literature, the second type of related academic literature, the third type of related academic literature, and the expanded set of academic literature; A i A represents the source algorithm in academic paper d; j c represents the target algorithm in academic literature d; A represents the HBS algorithm in all academic literature of the first type of related academic literature, the second type of related academic literature, the third type of related academic literature, and the expanded set of academic literature; ij A represents i and A j The confidence score of the relationship between them, when c ij Candidate relation pairs RC(d) are retained when the value is greater than the preset threshold γ.

[0060] In one embodiment, the expression for the relation type of the candidate relation pair is:

[0061] ;

[0062] ;

[0063] in, Indicates the relation type of candidate relation to RC(d); This represents the probability that candidate relation pair RC(d) belongs to relation type t, based on a set of academic documents. Description A i and A j The frequency of the words extends, improves, and based on in academic literature was obtained; The set of academic documents consists of the first type of related academic documents, the second type of related academic documents, the third type of related academic documents, and the extended set of academic documents; T represents the set of relation types; Predecessor represents the predecessor relation, Extension represents the extension relation, Improvement represents the improvement relation, Component represents the component relation, and Variant represents the variant relation.

[0064] Based on the first dataset, perform automated logical consistency checks and modifications on the second dataset to obtain the modified second dataset;

[0065] In one embodiment, the second dataset is automatically logically consistent and modified based on the first dataset to obtain the modified second dataset, including the following steps;

[0066] Source identification algorithm A i Check if the matching relationships between the second dataset and the first dataset are the same; if they are different, then the source algorithm A will be modified. i The matching relationships in the second dataset are modified to match the original algorithm A. i The matching relationships in the first dataset; the specific information of the matching relationships is each of the 8 types of information extracted from the mind chain prompt template.

[0067] In one embodiment, the second dataset is automatically logically consistent and modified based on the first dataset to obtain the modified second dataset, including the following steps;

[0068] Source identification algorithm A i Check if the first matching relation in the second dataset is the same as the first matching relation in the first dataset; if they are different, then the source algorithm A will be modified. i The first matching relationship in the second dataset is modified to the original algorithm A. i The first matching relationship in the first dataset; wherein, the first matching relationship is the source algorithm A. i The matching relationship between security level and key length; the security level and key length are contained in the proposed technical details extracted from the preset thought chain prompt template;

[0069] It is understandable that if the security level and key length match, then the formula is satisfied. ;in, Source Algorithm A i Security level; Source Algorithm A i The key length.

[0070] Source identification algorithm A i Check if the second matching relationship in the second dataset is the same as the second matching relationship in the first dataset; if they are different, then the source algorithm A will be modified. i The second matching relationship in the second dataset is modified to the source algorithm A. i The second matching relationship in the first dataset; wherein, the second matching relationship is the source algorithm A. i The matching relationship between tree height and signature size; tree height and signature size are contained in the proposed technical details extracted from the preset mind chain prompt template;

[0071] It is understandable that if the tree height and the signature size match, then the formula is satisfied. ;in, Source Algorithm A i The signature size; Source Algorithm A i The height of the tree.

[0072] Source identification algorithm A i Winternitz parameters in the second dataset Does it meet the requirements? If the conditions are not met, then the source algorithm A will be... i The Winternitz parameter in the second dataset is modified to match the original algorithm A. i The Winternitz parameters in the first dataset, wherein the Winternitz parameters are contained in the proposed technical details extracted from the preset mind chain prompt template.

[0073] The HBS database is constructed based on the first dataset, the modified second dataset, the candidate relation pairs of each academic document, and the relation types of the candidate relation pairs.

[0074] In one embodiment, the HBS database includes the classes SignatureAlgorithm, HashFunction, AlgorithmRecord, and AlgorithmRelationship;

[0075] The SignatureAlgorithm class is used to store and manage basic information of various HBS algorithms, wherein the SignatureAlgorithm class and the HashFunction class have a one-to-many relationship;

[0076] The HashFunction class is used to record different hash functions;

[0077] The AlgorithmRecord class is used to provide complete performance characteristics and scenario requirements for each combination of HBS algorithm and hash function; wherein, the combination of algorithmID in the SignatureAlgorithm class and hashFunctionID in the HashFunction class constitutes a composite key, which uniquely identifies each record in the HBS database;

[0078] The AlgorithmRelationship class is used to store the relationships between various HBS algorithms.

[0079] In an embodiment, the SignatureAlgorithm class, the HashFunction class, the AlgorithmRecord class and the AlgorithmRelationship class are as shown in Tables 1-4:

[0080] Table 1. SignatureAlgorithm class (signature algorithm table)

[0081]

[0082] Table 2. HashFunction class (hash function table)

[0083]

[0084] Table 3. AlgorithmRecord class (algorithm record table)

[0085]

[0086] Table 4. AlgorithmRelationship class (algorithm relationship table)

[0087]

[0088] In an embodiment, the SignatureAlgorithm class, the HashFunction class, the AlgorithmRecord class and the AlgorithmRelationship class are managed as data models of a relational database mysql, and a structured record is created for each data model, and a unified storage format is designed.

[0089] It should be noted that: in order to prove the effectiveness of the method of the present application, a benchmark data set containing 100 HBS algorithms is manually sorted, and the specific evaluation indexes include: information extraction accuracy (IE-Acc), information extraction integrity (IE-Comp), relationship recognition consistency (RIC) and algorithm result consistency (ARC).

[0090] As shown in Table 5, the method of the present application is compared with existing method 1, existing method 2 and existing method 3. The results show that: the accuracy of the method of the present application in extracting structured knowledge of HBS algorithms reaches 94.3%; the accuracy of the method of the present application in extracting technical details such as key generation and signature verification reaches 95.5%; the accuracy of the method of the present application in identifying algorithm evolution and variant relationships reaches 91.7%, which is significantly better than the other three existing methods.

[0091] As shown in Table 6, for the SPHINCS+ case study, the method of the present application successfully identified 19 / 20 key parameters, 3 / 3 variant types, and 5 / 6 relationship types, with an overall accuracy of 93.5%, significantly better than the other three existing methods.

[0092] Table 5. Experimental comparison results of the method of the present application and the other three existing methods

[0093]

[0094] Table 6. Comparison of SPHINCS parameters identified by the method of the present application and the other three existing methods

[0095]

[0096] Existing method 1: OpenAI: ChatGPT (GPT-4). https: / / chat.openai.com (2023);

[0097] Existing method 2: Anthropic: Claude. https: / / claude.ai (2023);

[0098] Existing method 3: Liu, F.: Language database construction method based on big data and deep learning. Alexandria Engineering Journal 61(12), 9437-9446 (2022);

[0099] In a second aspect, in another aspect, the present application also provides an electronic device, such as Figure 2 As shown, the electronic device can include a processor 201, a communications interface 202, a memory 203, and a communications bus 204, wherein the processor 201, the communications interface 202, and the memory 203 communicate with each other through the communications bus 204. The processor 201 can invoke the logic instructions in the memory 203 to execute the HBS database construction method using the thought chain and the snowball method.

[0100] In addition, the logic instructions in the memory 203 described above can be implemented in the form of software function units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0101] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0102] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a HBS database using a thought chain and a snowball method, characterized in that, The method comprises the following steps: Based on the preset thought chain prompt template, the structured information of the first type of related academic literature and the second type of related academic literature is extracted to obtain a first data set; wherein the first type of related academic literature is a basic academic article of the HBS algorithm; and the second type of related academic literature is a standard file of the HBS algorithm; The snowball method is used to expand the seed literature to obtain an expanded academic literature set; wherein the seed literature is the first type of related academic literature involved in the third type of related academic literature; and the third type of related academic literature is a review article of the HBS algorithm; Based on the preset thought chain prompt template, the structured information of the expanded academic literature set is extracted to obtain a second data set; The candidate relationship pairs and the relationship types of the candidate relationship pairs of each academic literature in the first type of related academic literature, the second type of related academic literature, the third type of related academic literature and the expanded academic literature set are extracted; Based on the first data set, the second data set is automatically checked and modified for logical consistency to obtain a modified second data set; Based on the first data set, the modified second data set, the candidate relationship pairs of each academic literature and the relationship types of the candidate relationship pairs, an HBS database is constructed.

2. The HBS database construction method using the thought chain and the snowball method according to claim 1, characterized in that, The preset thought chain prompt template includes algorithm basis information identification, mathematical basis analysis, proposed technical details, performance feature analysis, security attribute evaluation, algorithm relationship mapping, application scenario identification and special attribute description.

3. The HBS database construction method using thought chains and snowball method according to claim 1, characterized in that: The basic academic articles of the HBS algorithm include Lamport-OTS algorithm, WOTS algorithm, WOTS+ algorithm, WOTS-PRF algorithm, MSS algorithm, XMSS algorithm, LMS algorithm, SPHINCS algorithm, SPHINCS-Simpira algorithm, Gravity-SPHINCS algorithm and SPHINCS+ algorithm; The standard files of the HBS algorithm include standard document RFC 8554, standard document RFC 8391, standard document NIST SP 800-208 and standard document NIST FIPS 204; The review articles of the HBS algorithm include the role of hash-based signatures in quantum secure Internet of Things: current solutions and future directions and review of post-quantum cryptography: techniques, challenges and standardization.

4. The HBS database construction method using the thought chain and the snowball method according to claim 1, characterized in that, The expanded academic literature set is obtained based on the following formula: ； Wherein, i = 1, 2,..., n; n represents the round of expansion; S represents the academic literature set after expansion; F i represents the academic literature obtained by forward expansion in the i th round; B i represents the academic literature obtained by backward expansion in the i th round; F1 is the reference literature of the seed literature; B1 is the literature referring to the seed literature; F i+1 is the reference literature of F i ; B i+1 is the literature referring to B i .

5. The HBS database construction method using the thought chain and the snowball method according to claim 2, characterized in that, The expression of the candidate relationship pair is: ； wherein RC(d) represents a candidate relation pair extracted from an academic literature d; d represents each of the first type of relevant academic literature, the second type of relevant academic literature, the third type of relevant academic literature, and the expanded academic literature set; A i represents a source algorithm in the academic literature d; A j represents a target algorithm in the academic literature d; A represents an HBS algorithm in all of the first type of relevant academic literature, the second type of relevant academic literature, the third type of relevant academic literature, and the expanded academic literature set; c ij represents a confidence score of a relation between A i and A j , when c ij is greater than a preset threshold γ, the candidate relation pair RC(d) is retained.

6. The HBS database construction method using the thought chain and the snowball method according to claim 5, wherein, The expression of the relationship type of the candidate relationship pair is: ； ； wherein, denotes the relation type of the candidate relation pair RC(d); denotes the probability that the candidate relation pair RC(d) belongs to the relation type t, which is based on the academic literature collection describing A i and A j obtained from the frequency of the occurrence of extends, improves, based on in the academic literature; is the academic literature collection consisting of the first type of relevant academic literature, the second type of relevant academic literature, the third type of relevant academic literature, and the extended academic literature collection; T denotes the set of relation types; Predecessor denotes predecessor relation, Extension denotes extension relation, Improvement denotes improvement relation, Component denotes component relation, and Variant denotes variant relation.

7. The HBS database construction method using the thought chain and the snowball method according to claim 5, characterized in that, Based on the first data set, the second data set is automatically checked and modified for logical consistency to obtain a modified second data set, comprising the following steps: Judgment source algorithm A i whether the first matching relationship in the second data set is the same as the first matching relationship in the first data set; if not, modifying the first matching relationship in the second data set to the source algorithm A i i the first matching relationship in the first data set; wherein the first matching relationship is the source algorithm A i the matching relationship of the security level and the key length; the security level and the key length are contained in the proposed technical details extracted by the preset thinking chain prompt template​ Judgment source algorithm A i whether the second matching relationship in the second data set is same as the second matching relationship in the first data set; if not, modifying the second matching relationship in the second data set as source algorithm A i i the second matching relationship in the first data set; wherein the second matching relationship is source algorithm A i the matching relationship of the tree height and the signature size; the tree height and the signature size are contained in the proposed technical details extracted by the preset thinking chain prompt template​ Judgment source algorithm A i Winternitz parameters in the second data set Whether the condition is met If the condition is not met, the source algorithm A i Winternitz parameters in the second data set are modified to the source algorithm A i Winternitz parameters in the first data set, wherein the Winternitz parameters are contained in the proposed technical details extracted by the preset thought chain prompt template.

8. The HBS database construction method using the thought chain and the snowball method according to claim 1, wherein, The HBS database comprises a SignatureAlgorithm class, a HashFunction class, an AlgorithmRecord class and an AlgorithmRelationship class; The SignatureAlgorithm class is used for storing and managing basic information of various HBS algorithms, wherein the SignatureAlgorithm class and the HashFunction class are in a one-to-many relationship; The HashFunction class is used for recording different hash functions; The AlgorithmRecord class is used for providing complete performance characteristics and scene requirement characteristics for each combination of HBS algorithms and hash functions; wherein a combination of algorithmID in the SignatureAlgorithm class and hashFunctionID in the HashFunction class constitutes a composite key, and uniquely identifies each record in the HBS database; The AlgorithmRelationship class is used for storing relationships among various HBS algorithms.

9. The HBS database construction method using the thought chain and the snowball method according to claim 8, wherein, The SignatureAlgorithm class, the HashFunction class, the AlgorithmRecord class and the AlgorithmRelationship class are managed as data models of a relational database mysql, structured records are created for each data model, and a unified storage format is designed.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the HBS database construction method using the thought chain and the snowball method according to any one of claims 1 to 9 when executing the computer program.

Images