35 results about "Initial Seed" patented technology

The invention relates to the technical field of security calculation and privacy protection, in particular to a large model privacy reasoning method and device based on trusted hardware and electronic device.The method comprises the steps that before a large model reasoning task is executed, a client and a server initialize respective pseudo-random number generators based on information interaction so as to synchronize initial seeds, and the pseudo-random number generators are initialized; generating a random number based on the seed; in the reasoning execution process, homomorphic encryption is used for completing word embedding of large model reasoning, a reasoning instruction set is used for converting all operators into instruction combinations, privacy reasoning is carried out based on a privacy reasoning protocol and a random number generated by a pseudo-random number, and the random number is used for masking privacy data; and after the task is completed, obtaining an execution result and proof information provided by the server, performing verification, and refusing to receive the result if verification fails. Therefore, the problems of data leakage risk, difficulty in considering safety and efficiency and the like existing in related technologies are solved.

The invention provides a large model assisted Fuzz test Harness generation method and system, and the method comprises the steps: migrating a library of a QNX platform to a Linux environment for operation, and carrying out the glue code packaging of interface difference and system call difference through the generality that the library of the QNX platform and the Linux environment share a POSIX standard, thereby achieving the automatic adaptation of key API and context constraint; then analyzing binary semantics by means of an intelligent Agent constructed by a large model, automatically generating a Harness skeleton, focusing potential dangerous points (such as memory management, character string processing functions and the like), generating an initial seed in combination with a static or dynamic analysis result, and finally executing a Fuzz test in a controllable execution environment. And high coverage and rapid vulnerability positioning of the multi-dimensional attack vector of the target library are realized. The technical problems that Harness writing highly depends on expert experience, a customized generation strategy for target binary semantics is lacked, and comprehensive understanding on the binary semantics and context constraints is lacked are solved.

The invention discloses a method and a system for detecting the inconsistency between Solidence compilers, and solves the technical problem that the existing method for detecting the inconsistency between the Solidence compilers lacks comprehensiveness in detection of the defect of the inconsistency between the Solidence compilers and other types of cross-compiler inconsistency between the Solidence compilers. The method comprises the following steps: acquiring initial seed programs, complete source codes, official difference documents and complete source codes of corresponding actuators of a plurality of Solidiity compilers; generating explicit and implicit code block data of each compiler and a target variation rule matched with the initial seed program through the large model, and repairing through the reference compiler and the large model to obtain an initial test program variant; then finely adjusting the large model, generating a target test program variant, identifying, compiling and executing an inconsistent result; and finally, in combination with the official difference document, generating an inconsistency detection report between Solidity compilers.

The invention relates to the technical field of tool path planning, and discloses a combined curved surface constant residual height fixed-axis machining tool path generation method and a storage medium, and the method comprises the steps: firstly, discretizing a combined curved surface into a triangular mesh, and calculating the height of a collision-free tool location point above a machining part through adaptive quadtree sampling; forming a net-shaped structure of a discrete three-dimensional configuration space; projecting the processing area to the space for cutting, and determining an effective processing area; taking the boundary of the area as an initial seed polygon, outwards expanding line segments of the boundary into a cylindrical surface in a limited range, intersecting the cylindrical surface with the configuration space to generate a next circle of equidistant cutter location points, connecting the equidistant cutter location points to form a new cutter path, and iterating the process until the processing area is filled. According to the method, tool path selfing is fundamentally eradicated, the method is completely independent of the normal direction of the curved surface, the method is not sensitive to the model quality, the interference-free and equal-residue-height high-quality tool path can be generated in a full-automatic and high-robustness mode, and the reliability, efficiency and quality of complex curved surface numerical control programming are greatly improved.

The present disclosure provides computer-implemented method for training a model using a central node and a plurality of client nodes, comprising, at one or more of the plurality of client nodes: receiving a set of model parameters and an initial seed from the central node; determining an update direction based on the initial seed; computing scalar reflecting a sign and a size of an approximation of a projection of a gradient of a loss function on the update direction, using the set of model parameters; sending the scalar of the approximated gradient projection to the central node; receiving a model update scalar from the central node; and updating the set of model parameters in the update direction based on the model update scalar.

The invention discloses an LLM-based fuzzy test initial seed generation method and system, a computer and a storage medium. Lightweight static analysis is performed on target software source codes, an expansion function call graph is constructed, two key features of hard coding constraint and security sensitive operation are extracted, and explicit tags are injected in situ into the source codes; a semantic aggregation strategy based on a recursive sub-function abstract is adopted, the sub-function source code injected with the label is converted into a semantic abstract from bottom to top through a large language model, and a global semantic abstract retaining key constraint information is generated through hierarchical cue word construction; constructing a seed generation task containing a thinking chain guide instruction, forcing the model to perform reasoning according to logic steps of constraint solution, abnormal construction and enhanced generation, and generating a targeted initial input sample (namely, an initial seed) for fuzzy testing; and performing static validity check on the grammar by utilizing a general format analyzer, and performing fault-tolerant processing by combining a multi-stage backspacing mechanism. Through the recursive abstract aggregation and thinking chain reasoning technology, deep semantic understanding of complex code logic and automatic generation of high-coverage-rate test cases are achieved.

The invention discloses a method for dispersedly generating private keys in an asymmetric algorithm, and relates to the technical field of cryptography and information security. The method comprises the following steps: acquiring entropy values of various system indexes, and extracting and expanding to obtain an initial seed; generating a complete private key based on the seed, and splitting the complete private key into a plurality of sub-private key segments by adopting threshold secret sharing; after integrity verification, the fragment registers a position and a reconstruction path by means of a virtual mapping structure, is distributed to distributed computing nodes through an encryption channel, and is confirmed and stored by a signature receipt. When a signature request arrives, a path combination meeting a threshold is selected according to a mapping structure to reconstruct a temporary private key, and verification and unrecoverable destruction are carried out in a trusted environment after completion; and if detecting that the node is abnormal, isolating and starting a standby path to replace the fragment. According to the method, integrated management of private key generation, scattered storage, dynamic reconstruction and safe destruction is realized, and the security and the anti-attack capability of an asymmetric algorithm are remarkably improved.

The application discloses a mirror structure topology optimization method and device based on thermal coupling and a mirror, and relates to the technical field of metal mirror research and development. The application provides a mirror structure optimization method driven by thermal coupling simulation, which can self-adaptively control seed density and complete constrained Voronoi partition in a mirror body design domain. The method comprises the following steps: creating an initial model of a mirror to be optimized by using a three-dimensional modeling software; assigning initial Voronoi seed points; matching a corresponding stress value closest to each initial Voronoi seed point; dividing the Voronoi seed data into a high-stress-area seed point set and a low-stress-area seed point set; merging after increasing the seed point density in the high-stress-area and decreasing the seed point density in the low-stress-area; and performing 3D Voronoi partition between a front mirror surface of the mirror and a support frame body. The optimization method is suitable for optimizing the structural parameters of an existing mirror.

The invention discloses a fuzzy testing and model detection combined hardware verification method and system, and belongs to the technical field of hardware testing. The method comprises the steps that multiple rounds of fuzzy testing guided by the coverage rate are executed on a processor based on an initial seed set, snapshots generated in the fuzzy testing process are recorded, value scoring is conducted on the snapshots to construct a snapshot pool, and the processor is designed and generated based on a processor to be verified; selecting an optimal snapshot from the snapshot pool, and initializing a processor design based on the optimal snapshot; under the condition that points which are not covered exist in the processor design, a new test seed is generated through bounding model detection; performing fuzzy testing on the processor design based on the new test seed to obtain a verification result of the optimal snapshot; and synthesizing verification results of a plurality of optimal snapshots in the optimal snapshot set to obtain a hardware verification result of the to-be-verified processor design. According to the method, the efficiency and the applicability of the BMC can be remarkably improved on the premise of not sacrificing the verification precision.

The invention discloses a fuzzy test case generation and self-repairing method and system based on logical thinking chain guidance. According to the method, interface and boundary knowledge is extracted from documents and formalized constraints, coverage and abnormal targets are tasked to generate candidate prompts according to a logical thinking chain, an optimal prompt p * is obtained through verification and scoring, and initial seeds are precipitated; constructing a training set by using a real defect sample and a structure-maintained approximate sample, and performing parameter fine adjustment on the model to enable the model to generate a required fuzzy test case under the guidance of p *; and uniformly compiling / executing use cases in a sandbox, measuring coverage and abnormity, improving failure samples according to hierarchical self-repairing and lightweight variation, and recharging the minimized high-value samples and constraints to prompt to form a self-evolution closed loop. Under the same computing power and time budget, the implementation rate and coverage increment of the use case are remarkably improved, the exception triggering diversity is enhanced, and the method has reproducible, auditing and cross-library / multi-back-end adaptation capabilities.

The invention relates to a stateful protocol grey box fuzzy test method based on heuristic variation scheduling, which comprises the following steps: S1, initializing a test environment, and establishing a high-speed communication channel based on a shared memory, S2, dividing an initial seed set into a plurality of source seed systems according to sources; s3, selecting a target test state based on an inferred protocol state machine, and selecting a message sequence capable of reaching the state from a corpus, S4, selecting an enabled mutation operator to perform havoc mutation on candidate subsequences according to a current generation offspring mutation strategy of a current source seed system; s5, the test case is sent to the server through the shared memory channel, and the execution state is monitored; s6, generating a new generation of filial generation strategy after the first generation of filial generation strategy of the current source seed system is evaluated; and S7, repeating the steps S3 to S6 until a test termination condition is met. According to the method, global exploration and local development can be effectively balanced, the diversity and pertinence of test cases are improved, and the coverage capability of protocol states and code paths and the discovery probability of deep vulnerabilities are remarkably enhanced.

This invention discloses a method and system for detecting inconsistencies between Solidity compilers, addressing the technical problem that existing Solidity compiler inconsistency detection methods lack comprehensiveness in detecting cross-compiler inconsistencies. The method includes obtaining initial seed programs, complete source code, official discrepancy documents, and corresponding executor complete source code for multiple Solidity compilers; then generating explicit and implicit code block data for each compiler and target mutation rules adapted to the initial seed programs using a large model; obtaining initial test program variants through benchmark compiler and large model repair; subsequently, fine-tuning the large model to generate target test program variants and identify inconsistent compilation and execution results; finally, combining the official discrepancy documents to generate a Solidity compiler inconsistency detection report.

This invention provides a rapid analysis method for large-scale land space compliance review, comprising the following steps: receiving a compliance review task submitted by a user, the review task including the review scope geometry, target planning layer, and review rules; calculating the area of ​​the bounding rectangle (AreaEnvelope) and the area of ​​the geometric shape (Area) within the review scope geometry; if both AreaEnvelope and Area are present, it is determined to be a small-scale review, and the traditional single-step compliance analysis process is executed; otherwise, it is determined to be a large-scale review, triggering a parallel optimization process; performing balanced region division for large-scale reviews, including initial seed point generation, Voronoi diagram construction and pruning, and iterative optimization steps; performing parallel compliance analysis and generating a review report. This invention transforms a single long-duration calculation into multiple short-duration parallel calculations, reducing the review time from minutes / hours to seconds, meeting the needs of real-time review.

The application discloses a business space layout method based on a centroid Voronoi diagram, comprising the following steps: step 1, determining the boundary of the business space layout and dividing it into multiple levels; step 2, randomly generating initial seed points of the first level in the boundary of the business space and optimizing them; taking the boundary of the business space and the optimized seed points of the first level as inputs of the centroid Voronoi diagram, dividing multiple regions of the first level, and obtaining passable paths in the first level and the regions after refinement adjustment; step 3, for the Nth level, generating initial seeds from the boundary of each region in the (N-1)th level and optimizing them, dividing multiple regions of the Nth level based on the rectangular centroid Voronoi diagram, and obtaining passable paths in the Nth level and the regions after refinement adjustment. The method is simple, easy to deploy on a computer, highly automated, and has high controllability and flexibility.

The invention discloses an intelligent query method for structured data in the field of oil-gas exploration, which innovatively constructs initial seed data and a'thinking chain 'strategy expansion data set aiming at the intelligent query problem of the structured data in the field of oil-gas exploration. On the basis of a large natural language processing (NLP) model with a large amount of world knowledge, a low-rank adaptation algorithm (LoRA) is applied to carry out fine adjustment on the model, and meanwhile, by means of assistance of a vectorization knowledge base, query of data in the field of oil-gas exploration can be completed while the world knowledge is used. Compared with a traditional method, the method has remarkable advantages in the aspects of question-answering performance, accuracy and robustness, and a new and effective data question-answering scheme is provided for the field of oil-gas exploration.

The embodiment of the application provides a vulnerability detection method and a computing device. Test cases are generated according to a target optimization PASS of a compiler, which can ensure that the test cases are highly related to the characteristics of the target optimization PASS. Basic blocks of vulnerabilities are determined based on intermediate representation data obtained after the test cases are compiled, so that the position of potential vulnerabilities is located more accurately. The test cases are sliced according to the control flow characteristics of the basic blocks of vulnerabilities, and initial seeds generated based on this can have more targeted execution paths to reach the basic blocks of vulnerabilities, thereby significantly improving the directionality and efficiency of fuzz testing, increasing the vulnerability triggering probability and detection accuracy. By fusing the target distance and the optimization path coverage, high-value seeds are selected for mutation, and efficient and accurate detection of the Rust compiler optimization vulnerabilities is realized.

The invention relates to the technical field of computer vision and three-dimensional point cloud processing, in particular to a three-dimensional model data processing method for automobile assembly tolerance analysis, and the method comprises the steps: calculating the probability of point cloud data belonging to an initial seed point according to the curvature, reflection intensity and local density of the point cloud data, determining an initial seed point in the point cloud data; obtaining a growth area by using the initial seed point, and calculating the fitting probability of the edge point through the curvature variance and the normal vector angle variance of the edge point; fitting is carried out after fitting edge points are determined from the edge points of the growth area based on the fitting probability, and a spline curve of the growth area is obtained; the virtual points are inserted in the tangential direction of the spline curve of the growth area, the growth area and the virtual points are fitted, and a detection path is planned after the automobile surface contour is obtained, so that the assembly tolerance analysis of the automobile surface contour is realized, and the accuracy of the assembly tolerance analysis of the automobile surface contour is effectively improved.

The invention discloses a password system protocol state machine testing method and system based on directional testing, and the method comprises the steps: S1, carrying out the source code instrumentation of a target protocol implementation program, and deducing a target protocol state machine through a model learning technology; s2, according to the set target code position t, performing instrumentation compiling on the target protocol implementation program by using a fuzzy test tool; s3, according to the initial seed bank, performing directional fuzz testing on the compiled target protocol implementation program based on state awareness, in the directional fuzz testing, generating a response code-based protocol state machine through a fuzz testing engine, dynamically updating a state score in the protocol state machine, probabilistically selecting a target state according to the state score, and determining the target state according to the target state. Selecting seeds capable of triggering a target state from the initial seed bank for variation; and S4, screening test cases found in the directional fuzzy test, and correcting the model structure of the target protocol state machine. According to the invention, full verification and testing of the cryptographic system protocol state machine are realized.

The application relates to the technical field of secure computation and privacy protection, in particular to a large model privacy inference method and device based on trusted hardware and electronic equipment, wherein the method comprises the following steps: before performing a large model inference task, a client and a server initialize respective pseudo-random number generators based on information interaction, so as to synchronize initial seeds and generate random numbers based on the seeds; in the inference process, homomorphic encryption is used to complete word embedding of the large model inference, all operators are converted into instruction combinations by using an inference instruction set, privacy inference is performed based on a privacy inference protocol and the random numbers generated by the pseudo-random number generator, and privacy data is masked by using the random numbers; after the task is completed, execution results and proof information provided by the server are acquired, verification is performed, and the results are rejected if the verification fails. Thus, the problems that related technologies have data leakage risks and are difficult to balance security and efficiency are solved.

A molecular structure acquisition method, an electronic device and a storage medium, which relate to the field of artificial intelligence such as deep learning, are disclosed. The method may include: performing, for an initial seed, the following first processing: generating M molecular structures according to the seed, M being a positive integer greater than one; taking the M molecular structures as candidate molecular structures, and selecting some molecular structures from the candidate molecular structures as progeny molecular structures; and performing evolutionary learning on the progeny molecular structures, taking the progeny molecular structures after evolutionary learning as the seed, and repeating the first processing until convergence reaches an optimization objective, and when the convergence reaches the optimization objective, a newly selected molecular structure is taken as a desired molecular structure.

The invention provides a multimedia protocol fuzz testing diversified seed generation method based on a large model, and belongs to the technical field of fuzz testing. The method comprises the following steps: inputting a target program source code into a large language model; extracting all resource file types involved in the source code of the target program by using a large language model; generating a corresponding request URL (Uniform Resource Locator) for each type of resource file by using the large language model; request traffic in actual communication is captured, and a fuzz test seed for the request traffic is generated based on the request URL. According to the method, the analysis capability of a large language model is utilized, multimedia resource types supporting processing in source codes of a tested program are comprehensively and accurately analyzed, and on this basis, construction of diversified seeds is completed in combination with methods such as structured variation and the like. The problem that in the prior art, resource types in an initial seed bank are few, and consequently a fuzzy test tool cannot fully test a target program is mainly solved.

The invention is suitable for the technical field of computer system verification, and provides an RISC-V dynamic random instruction generator and an instruction generation method.The RISC-V dynamic random instruction generator is characterized in that a seed generation unit is used for generating random seeds related to addresses and simulation environment initial seeds; the dynamic assembly line unit is used for generating a constrained random instruction and comprises an instruction information unit and an instruction constraint unit, and the instruction information unit serves as an information transmission medium and comprises a next layer constraint file name, an instruction code, an instruction length and an address; and the basic configuration unit is used for acquiring an initial seed generation unit name, a hash operation unit name, an instruction information unit name, an instruction constraint unit name group and a dynamic assembly line layer, and transmitting the initial seed generation unit name, the hash operation unit name, the instruction information unit name, the instruction constraint unit name group and the dynamic assembly line layer to the seed generation unit and the dynamic assembly line unit for instantiation. And instruction stream simulation and deterministic randomization with low memory occupation are realized.

The application discloses a kind of real BOM construction methods and systems for complex equipment, it is related to product life cycle management technical field, including: EBOM structure tree and PBOM structure tree are mapped matrix is constructed;With EBOM structure tree as basic skeleton, traverse EBOM node, to each EBOM node, if there is corresponding PBOM node in mapping matrix, then the attribute of corresponding PBOM node is attached to current EBOM node;If it does not exist, then keep current EBOM node, and obtain initial seed tree;According to the process assembly sequence and node type of PBOM structure tree, the initial seed tree is adjusted in structure, conflict resolution and deduplication are executed, and the real BOM structure tree skeleton is obtained;Real BOM structure tree skeleton is filled with real object data, and the real BOM structure tree is obtained, and tree structure difference comparison method is used for updating.It realizes the mapping conversion and dynamic synchronization from digital BOM to real BOM.

This invention relates to the field of thermal power units and discloses a wide-load intelligent coordination optimization system for thermal power units based on a multi-objective optimization algorithm. This system aims to achieve intelligent coordination of economic efficiency, environmental friendliness, and rapid load response during dynamic processes across all operating conditions. The system includes obtaining Pareto candidate solutions with behavioral feature labels under typical load nodes and building an offline database. During online operation, it combines the current unit load command with the deviation direction of the actual operating condition to quickly screen the initial seed set. It then completes local feasible region optimization through neighborhood exploration with the minimum step size of the DCS, and finally selects the optimal control strategy through a multi-objective satisfaction balance index. After smoothing, the strategy is output to the DCS for execution. This invention can periodically correct the mechanistic model parameters based on the unit's operational response data, effectively improving the parameter stability and overall operating performance of the unit under wide-load variable operating conditions, and adapting to the complex operating scenarios of deep peak shaving in thermal power plants under the new power system.

The application discloses a hardware verification method and system combining fuzzy testing and model detection, and belongs to the technical field of hardware testing. The method comprises the following steps: performing multiple rounds of coverage-guided fuzzy testing based on an initial seed set on a processor, recording snapshots generated in the process of fuzzy testing, and performing value scoring on the snapshots to construct a snapshot pool, wherein the processor is generated based on a processor design to be verified; selecting a best snapshot from the snapshot pool, and initializing a processor design based on the best snapshot; in the case that there are still uncovered points in the processor design, generating a new test seed by using bounded model detection; performing fuzzy testing on the processor design based on the new test seed to obtain a verification result of the best snapshot; and comprehensively integrating the verification results of a plurality of best snapshots in the best snapshot set to obtain a hardware verification result of the processor design to be verified. The application can significantly improve the efficiency and applicability of the BMC without sacrificing the verification accuracy.

The invention provides a secret key generation method of a distributed point function and a secret sharing fragment generation method. The key generation method of the distributed point function comprises the following steps: acquiring a puncture value represented by an n-bit binary string and a corresponding target mapping value; initial seed values corresponding to the two participants are determined; the method comprises the following steps: constructing an n-layer tree structure, aiming at an ith layer, firstly inputting a seed value of a previous layer of each participant into a first oracle machine to obtain a first bit string, determining a correction word of the ith layer according to the first bit string and a value of an ith bit of a binary puncture value, and updating the seed value of the ith layer; and determining an additional correction word according to the seed value of the nth layer of each participant and the target mapping value. And finally, for each participant, combining the initial seed value of the participant, the n layers of correction words and the additional correction words to obtain the key of the participant. The correction word of the ith layer is determined through the first bit string, the value of the ith bit of the binary puncture value and other data. And the correction word length is shortened.

The invention provides a BMC (Baseboard Management Controller) firmware fuzzy test method based on target function guidance, which belongs to the technical field of BMC firmware fuzzy test, and specifically comprises the following steps: S101, scanning target BMC firmware by using a target function discovery algorithm based on a predefined danger function, searching for a potential fuzzy test target function, and performing fuzzy test on the target BMC firmware; the target function of the final fuzzy test is determined through analysis; s102, writing a fuzzy test driving program based on the target function, the driving program being used for guiding a fuzzy tester to operate the target function and constructing a fuzzy test initial seed; s103, reading all seeds in the initial seed path, generating a plurality of fuzzy testers through an LLMP mechanism in the LibAFL, guiding the plurality of fuzzy testers to operate the target function through the fuzzy test driving program, monitoring fuzzy test operation information and state, storing an abnormal sample occurring in the target program in the fuzzy test, and storing the abnormal sample in the target program in the fuzzy test. And the generation of the next round of fuzzy test input is fed back and adjusted.

This invention proposes a fuzzing method and system for WebAssembly engines, relating to the field of software testing. It involves acquiring a large number of WebAssembly binary samples as initial seed samples; parsing the initial seed samples into an attribute abstract syntax tree (API) with node position and type information; applying a mutation method to the API using a type-aware mutation strategy and a structure-aware instruction mutation method to generate a new API with valid structure and type; pre-validating the mutated API and quickly correcting invalid node positions; and generating WebAssembly binary samples from the pre-validated API, which are then input into the target engine for fuzzing. This invention, by using an API based on attribute abstract syntax trees and a WebAssembly mutation method with pre-validation and rapid correction, more effectively generates WebAssembly module mutations with correct binary format, legality, and compliance with security constraints, enabling efficient fuzzing of the WebAssembly engine.