6758 results about "Verification" patented technology

The invention discloses a large model enhanced code security detection method. The method comprises the following steps: dynamically associating CVE vulnerability features with code semantic representation through a knowledge graph construction module; extracting vulnerability features from a CVE vulnerability library by utilizing a knowledge graph construction module, and dynamically associating the vulnerability features with code semantics; based on the CodeQL standard, a query language is generated by adopting a large model and input into CodeQL for AST analysis, and code structure features are extracted; the large model generates a query language meeting the CodeQL standard, codes are analyzed through CodeQL, and key structure features are extracted; integrating a static analysis tool chain to carry out multi-dimensional credible verification on model output; integrating an SAST tool and a symbolic execution tool, and verifying the model output from different dimensions; and summarizing and fusing detection results of the enhancement layers, and determining a final detection result by adopting a decision optimization method. According to the method, through technical fusion and dynamic optimization, the problems of rule stiffness, one-sided detection and low result credibility are systematically solved.

A BIM-based prefabricated building design system and a simulated assembly method. The system comprises: a data collection module, a BIM modeling module, a drawing generation module, a component processing module, an assembly module, and a verification module; the data collection module is used for collecting data information of a prefabricated building; the BIM modeling module constructs a BIM model of the prefabricated building according to the data information; the drawing generation module is used for outputting a building component drawing according to the BIM model; the component machining module is used for performing machining and manufacturing according to the building component drawing; the assembly module is used for performing assembly path planning for a manufactured building component, and performing assembly on the basis of a planned path; and the inspection module is used for performing inspection on an assembled building. The present invention implements integration of prefabricated building design and implements prefabricated building installation intelligence, allowing assembly to be accurately completed.

The invention relates to the technical field of building engineering, in particular to a building data processing method and system based on multiple building specifications, and the method comprises the steps: building model data standardization processing: extracting geometric attributes, material attributes and spatial topological relations of components through a BIM software interface, and generating model data in a standard format; constructing a multi-source specification rule tree, performing semantic analysis on national standard, local standard and industrial standard provisions, extracting triple constraint conditions, and fusing to generate a unified rule tree containing hierarchical relationships and conflict marks; on the basis of a spatial topology mapping relationship between the model and the rule tree, identifying a specification conflict and generating an optimization rule set according to a specification effectiveness level and a spatial attribute priority; and finally, real-time compliance verification is executed, and a three-dimensional compliance report including conflict positioning, article basis and correction suggestions is output. According to the method, multi-source specifications can be automatically adapted, model violation components are accurately recognized, a correction scheme is given, and the specification compliance and the verification efficiency in the design stage are improved.

The invention relates to the technical field of firmware protection, in particular to a firmware protection method and system based on a security coprocessor. The method comprises the following steps: acquiring characteristic data of the coprocessor; extracting hardware entropy source data according to the coprocessor feature data; generating a hardware trust root key based on the hardware entropy source data; performing partition processing on the firmware based on the hardware trust root key to obtain firmware partition data; constructing hierarchical verification structure data according to the firmware partition data; implementing firmware starting security verification based on the hierarchical verification structure data to obtain integrity verification data; establishing a secure communication channel between the processors according to the integrity verification data; executing a bidirectional authentication process based on the secure communication channel, and generating session key data; a firmware runtime environment is monitored based on the session key data. According to the method, the hardware entropy source is extracted by measuring the physical characteristics of the coprocessor, and an unforgeable hardware trust root is established, so that the security is improved fundamentally.

The invention discloses a verification method and system of a chip simulation model, a medium and a program product, and relates to the technical field of chip verification, and the method comprises the steps: obtaining a simulation model and a performance parameter index of a to-be-verified chip; generating model training data based on the historical performance indexes, the corresponding historical use case set and the historical verification result, and performing model training based on the model training data to obtain a use case generation model; inputting the performance parameter indexes to a case generation model to obtain a test case set and a test verification expectation; executing a plurality of test cases in the test case set based on the simulation model, and recording an actual response result of the simulation model; based on the actual response result and the test verification expectation, generating a test log, and determining an abnormal mark in the test log; and marking the fault features corresponding to the exception, taking the test cases as newly added training data, and optimizing the case generation model based on the newly added training data. According to the invention, the chip verification efficiency can be improved.

The invention discloses an AI model automatic deployment platform based on a containerization technology, which relates to the technical field of automatic deployment of an artificial intelligence model, and comprises a container construction and intelligent configuration module, a transmission and cache management module, a security and multi-version warehouse module, a resource scheduling and optimization module and a deployment and interface management module, the container construction and intelligent configuration module adopts a three-layer mirror image construction strategy of a base layer, a framework layer and a model layer. According to the invention, all the modules cooperate to form a closed loop, after the container construction module generates an incremental packet and the incremental packet is subjected to security signature verification, the transmission module distributes the incremental packet according to network quality, the resource scheduling module dynamically adjusts bandwidth and quota, and the deployment module starts the container and monitors the container in real time. The problems of efficiency, safety and resource optimization of model deployment in an edge environment are solved, and cross-platform compatibility and service continuity are improved.

The invention provides a right and interest exchange verification system, belongs to the technical field of right and interest management systems, and effectively deals with complex right and interest fraud through multi-modal dynamic behavior trust chain construction. The system comprises a multi-modal behavior acquisition module, a real-time acquisition input behavior, equipment and environment characteristics, a federated behavior modeling module, a cross-platform construction dynamic behavior baseline library and trust score output module, a scene risk assessment module, a hierarchical verification strategy supporting module, a dynamic scheduling execution module, and an intelligent resource allocation and auditing module. Data desensitization and feature extraction are realized through lightweight edge calculation; federated learning fuses local and global credit scores, and combines a dynamic attenuation model and scene perception weight adjustment; the multi-modal cross validation is used for coping with different risk levels; the intelligent resource scheduling resists the DDoS attack; the system realizes balance between high-precision fraud identification and user experience through behavior continuity protection, confrontation sample detection and interpretable scoring atlas.

The invention provides an industrial agent decision-making method and device, electronic equipment and a storage medium. The method comprises the following steps: loading multi-source industrial data to a knowledge graph, and constructing a domain ontology and an MCP ontology; performing parameter fine tuning on a preset base large language model; receiving an industrial process planning demand input by a user, and performing semantic analysis on the industrial process planning demand; processing the query result by utilizing an MCP context strategy to generate a prompt context; inputting the prompt context and the industrial process planning demand into a fine tuning large language model to generate a first process planning scheme; performing rule verification on the first process planning scheme based on the knowledge graph, and judging whether the first process planning scheme meets a preset rule or not according to a verification result; and converting the final process planning scheme into a control script or an interface calling sequence which can be analyzed by an industrial execution system, and outputting the control script or the interface calling sequence. According to the method, the cross-software system compliance process planning with unified knowledge, automatic generation and rule verification can be realized.

A BIM-based engineering information management system of the present invention relates to the field of constructional engineering informatization management, and comprises a data processing module, a model construction module, a block chain evidence storage module, a verification module, a data fusion module and an authority management module. The data processing module receives original engineering data and generates a structured data signal. And the model construction module generates a BIM model according to the structured data signal. And the block chain evidence storage module encrypts and stores the metadata signal, generates an evidence storage completion signal and feeds back the evidence storage completion signal to the model construction module. And the verification module analyzes the dynamic operation log signal, and generates a risk early warning signal and a repair suggestion signal when abnormality is detected. And the data fusion module generates a fusion data signal, transmits the fusion data signal to the model construction module and triggers BIM model updating. And the authority management module adjusts the user authority according to the risk early warning signal. According to the invention, the problems of data islands, safety risks, low cooperation efficiency and insufficient intelligence in engineering information management can be solved.

The invention relates to the technical field of information management, and comprises a distribution automation operation and maintenance work log intelligent recording platform based on a CS architecture, and the platform comprises a log field analysis module, a task chain construction module, a task logic verification module, and a log storage tracing module. According to the method, missing and abnormal fields are effectively marked and corrected through meticulous analysis and comparison of uploaded log records, the accuracy and integrity of data are improved, task scheduling is optimized and the operation and maintenance management efficiency is improved through association of device numbers and task types and adjustment of execution time in sorting and grouping of task sequences, and the task scheduling efficiency is improved. Task chains are recombined to solve conflicts and adjust priorities, smooth execution of operation and maintenance tasks is guaranteed, delay is reduced, logic verification enhances task reliability through rule verification and error marking, unassociated tasks are deduced, analyzed and corrected, the exception processing capacity is enhanced, the data tracing and storage efficiency is improved, and the tracing capacity of data management is enhanced.

The invention provides a dynamic order making method and device based on multiple rounds of dialogues and electronic equipment, and belongs to the technical field of intelligent interaction. The method comprises the steps of guiding a user to gradually fill in a form through a multi-round dialogue mechanism, generating a question based on a business logic dependency relationship, and mapping a natural language answer of the user into a structured field value in combination with semantic analysis and field dependency management; integrating a business rule engine in a multi-round dialogue mechanism process, executing field-level, logic-level and process-level multi-dimensional verification in real time in a user input process, and dynamically adjusting a verification strategy based on context; constructing and adaptively adjusting a form structure in real time based on a dynamic form generation engine, multi-round dialogue contents and business rules; based on a low-code configuration development platform, in combination with a preset intelligent interaction component and a visual tool, an intelligent filling system supporting natural language interaction is constructed. And the intelligence, flexibility and accuracy of form filling are obviously improved.

The invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The invention discloses a PLC code generation and verification method based on a large language model, and the method comprises the steps: generating multi-stage multi-mode input data which is used for representing user demands and formulating a system design scheme according to a demand analysis result; the multiple stages comprise a demand analysis stage and a design stage of a complete engineering cycle; the multi-mode input comprises a Pamp (Passive Amplitude Point); an ID graph and a natural language; then, according to the multi-stage multi-mode input data, a PLC code is generated; then, verification testing is carried out on the generated PLC code, and the verification testing comprises code verification and compiler verification; and finally, after the verification test on the PLC code is completed, deploying the PLC code. The PLC code deployment method is high in accuracy and automation degree, an efficient and accurate PLC code deployment method can be provided for network security researchers, and the PLC code deployment method can be widely applied to the technical field of computers.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

The invention provides a low-code rule engine method and platform supporting dynamic service adjustment, and relates to the technical field of data processing.The method comprises the steps that original rule data are obtained, and the original rule data are subjected to structured conversion and unified format specification; generating a visual rule component, presenting rule logic through a graphical interface, supporting business personnel to edit and modify without coding, and converting a modification result into component rule data; performing real-time analysis and verification on the component rule data to obtain operation rule data; performing version identification on the operation rule data, and storing the operation rule data in a rule warehouse; based on the current service context, matching an applicable version, automatically selecting the operation rule data, and loading the operation rule data to a rule execution engine for execution; the autonomy and accuracy of the low-code rule engine are improved.

The invention relates to the technical field of system engineering modeling, and particularly discloses an MBSE optimization method based on a large language model, which realizes MBSE whole process automation and intelligentization by constructing a'demand-knowledge-model 'dynamic closed-loop framework and fusing RAG and LLM. The method specifically comprises the following steps: constructing a domain knowledge enhancement library, and integrating LLM to construct a demand analysis engine and a dynamic modeling optimization system; a domain expert inputs a demand through a natural language interaction interface, and the demand is analyzed into structured data through LLM; generating a parameterized model conforming to the MBSE specification by combining the RAG technology with the knowledge in the library; after the model runs through a simulation tool chain, the LLM adjusts parameters according to a simulation result to generate an iteration scheme; and after the modeler passes verification, storing the model and data into a database to form a knowledge source. According to the method, domain knowledge dual-drive modeling and cross-role collaboration are achieved, the knowledge base self-evolution capacity is achieved, the problems that traditional MBSE is high in manual dependence and insufficient in semantic fault and knowledge fusion are effectively solved, and the modeling efficiency and reliability are remarkably improved.

The invention discloses a data quality intelligent auditing system and method based on a dynamic rule base, and belongs to the technical field of data auditing, and the system comprises a rule base construction module which is used for analyzing business scene parameters through a scene analysis unit according to business scene demands and data type features to generate a rule configuration instruction; the multi-source monitoring engine module is connected to the rule base construction module and is used for collecting multi-source data in real time and loading corresponding checking rules; the automatic verification execution module is used for executing normalized quality verification on the multi-source data based on the verification rule base; and the feedback optimization module analyzes a rule hit rate and a false alarm rate in a verification result through a reinforcement learning algorithm, and dynamically iteratively updates a rule threshold value and a logic combination in the rule base. By constructing a full-automatic process of rule generation, execution, feedback and updating, the problems that a traditional system depends on manual intervention, response is slow, the industry average rule updating period is 3-7 days, and real-time updating is achieved through the scheme are solved.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention relates to the technical field of storage equipment control, and discloses a dynamic cooperative control method and system for digital twin-driven storage equipment. The method comprises the following steps: firstly, constructing a storage equipment digital twin model which covers a storage space three-dimensional model, equipment motion parameters and a real-time operation state; and then collecting sensor data in real time, and generating a collaborative operation path through a dynamic path planning algorithm containing conflict detection and priority allocation. And correcting the equipment motion track and updating the model state by using the space-time consistency verification model, finally inputting the corrected track into an equipment control instruction generation module, and outputting a driving signal to control the storage equipment. The system comprises a digital twinborn model construction module, a sensor data acquisition module, a collaborative operation path generation module, a motion trail correction module, a control instruction generation module, a driving module and the like. The storage space utilization efficiency and the equipment collaborative operation capability are improved, real-time monitoring and fault prevention are achieved, and intelligent upgrading of a storage system is promoted.

The invention relates to the technical field of building design, and discloses a BIM (Building Information Modeling) creation method based on a parameterization technology, which comprises the following steps: obtaining building project parameters, setting parameterization rules, generating a parameterization model framework, optimizing multi-level parameters, generating an intelligent BIM, verifying and simulating a model and automatically generating a construction scheme. According to the BIM creation method based on the parameterization technology, through collection of multi-dimensional parameter information, intelligent rule optimization, a multi-target optimization algorithm, comprehensive simulation verification and automatic construction scheme generation, high customization, real-time environment adaptability, intelligence and efficient cooperation of building design are achieved. According to the method, the design efficiency and quality are improved, and the optimal performance of the building in the aspects of structure, material, energy efficiency, construction and the like is ensured. Through the application of the block chain technology, the system further guarantees the security and transparency of the data, and guarantees the traceability and consistency of the design process.

The invention discloses a software test case generation method and system based on a large language model, and relates to the technical field of software testing, and the system comprises a to-be-tested module detection subsystem which is used for constructing a to-be-tested module set; the test case generation subsystem is used for constructing a prompt template according to the to-be-tested module and generating a test case by utilizing a program generation model based on an adaptive focus context generation mechanism and the prompt template; the correction verification subsystem is used for performing multi-stage correction processing on the test case, eliminating errors and generating an initial test suite; and the test case supplement subsystem is used for carrying out coverage rate analysis and variation test on the initial test suite, constructing a model input text according to a result, and generating a supplement test case by utilizing a program generation model so as to update the initial test suite and obtain a final test suite. By combining the functional annotation of the to-be-tested module with the logical reasoning ability of the LLM, misjudgment caused by assertion errors can be reduced, and the accuracy of test feedback is ensured.

The invention relates to an industrial control system security policy adaptive configuration method and device and a readable storage medium. According to the method, a control instruction stream, a process variable sampling value and link layer message metadata are synchronously collected, an association hypergraph fusing network semantics, control semantics and physical semantics is constructed to represent a system state, and a sampling period is divided into a plurality of time slots with fixed lengths. In each time slot, self-supervised learning is utilized to extract a topology embedded vector group, and a system operation rule is represented. A robustness envelope interval is generated by calculating a statistical distance between a current time slot and a historical baseline, and is used for driving a reinforcement learning model to generate a security policy parameter set. And executing corresponding access control, traffic shaping and integrity verification operations in the next time slot, generating receipt vector feedback model update according to an execution result, outputting a micro-policy patch with a hash signature and a timestamp, and sending the micro-policy patch to the edge security gateway to realize deployment, thereby realizing closed-loop adaptive optimization of the security policy in the dynamic environment.

The invention discloses a waterproof coating formula optimization method and system based on cloud computing, and relates to the technical field of coating formula optimization. A waterproof coating formula optimization system based on cloud computing comprises a data acquisition and management module, a data model construction module, a formula performance optimization module, a formula cost performance optimization module, a new formula verification module and a data recording and loop optimization module. The performance data model and the environment data model are constructed, and the performance range index of the waterproof coating is combined with the use environment adaptability; a forward optimization channel and a reverse optimization channel are designed, and optimization is carried out for improvement of existing formula performance and adaptation of target environment requirements; by combining the performance evaluation model and the optimization algorithm, efficient searching and updating of the formula parameters are achieved, and the formula optimization efficiency is greatly improved.

The invention provides an interactive integration method and system for an AI large model and a low-code platform, and relates to the technical field of interaction of the AI large model and the low-code platform, and the method comprises the steps: obtaining a task demand input by a user, analyzing the task demand into a structured task instruction, automatically matching an optimal AI model, and dynamically generating an adaptive API call parameter, according to the method, the AI large model and the low-code platform can be deeply integrated, the technical problems of integration complexity, interaction mode solidification, updating lag and the like are solved, and the method has the advantages that the method is simple and convenient to operate, and the method is suitable for popularization and application. According to the method, a user can call a complex model without AI professional knowledge, the low-code development efficiency is improved by more than 50%, and the security in a data processing process is enhanced by introducing a security verification mechanism, so that the integration efficiency of a low-code platform and an AI large model is greatly improved.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The invention provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation. The business module source code generation method comprises the steps that semantic analysis is conducted on business requirements, and a business association graph with weights is constructed; performing module division, and decomposing a complete task into a plurality of sub-tasks; calling a coordination agent, distributing a corresponding module code generation agent based on a large model for each decomposed subtask, and injecting context information to generate a corresponding module code; wherein the module code generation agent learns a corresponding relationship between tasks and injected context information and codes in advance based on field self-adaptive staged training; and calling the verification agent to perform code quality verification. The method has the beneficial effects that the module division is performed based on the business association graph with the weight, the multi-agent collaborative code generation is constructed by adopting the field-adaptive staged training, and the quality of the generated code is verified, so that the code generation efficiency and quality are improved.

The invention belongs to the field of test case generation in traditional software engineering, and discloses a Python program test case generation method based on a large language model. The method can automatically generate and optimize a unit test case of a Python program, and comprises the following steps of: extracting public basic context and function information by utilizing project dependent structure scanning and an AST abstract syntax tree, and constructing an accurate cue word template; verification of grammar, operation logic and coverage rate is carried out on the test case generated by the large language model; aiming at the failed test case, designing a new cue word template and an iteration process so as to automatically iterate and improve, and setting the maximum number of iterations so as to ensure the efficiency; and finally, outputting an optimized high-quality successful test case set so as to improve the test efficiency and reduce the waste of manpower and material resources.

The invention discloses a remote digital meeting management system combining a block chain and an AI large model, and relates to the technical field of information, and the system comprises a hierarchical dynamic verification chain module which is formed by connecting a real-time layer verification chain and an evidence storage layer auditing chain through a data synchronization interface, the real-time layer verification chain adopts a lightweight alliance chain architecture, and the evidence storage layer audit chain is constructed based on a public chain. According to the remote digital meeting management system combining the block chain and the AI large model, through deep fusion of the block chain and the AI large model, the safety and the intelligent level of remote meeting management are improved. A hierarchical dynamic verification chain is constructed by a block chain technology, so that the non-tampering property and the full-life-cycle traceability of the whole-process data of the conference are ensured, and the defects that a traditional centralized system is easily attacked and the data is easily altered are effectively overcome.

The invention discloses an operator migration method and device between hardware platforms and a migration model construction method. The migration method comprises the following steps: obtaining an operator code and a function label of an operator to be migrated; when the existing operator information of the target hardware platform does not contain the function label, obtaining first information of the target hardware platform and second information of the source hardware platform; training a large language model based on the first information and the second information, and taking the trained large language model as an operator automatic migration development model; generating an initial operator matched with the precision requirement of the target hardware platform based on the operator automatic migration development model and the operator code; analyzing the initial operator to obtain an operator precision difference; taking an initial operator corresponding to the operator precision difference passing verification as a target operator; and storing the target operator to a corresponding level of the target hardware platform. According to the method, automatic non-inductive migration of the model operator on different hardware platforms can be realized, and efficient and seamless adaptation with different platforms is realized.

The invention discloses an ECU calibration file parameter verification method, system and device and a medium, and relates to the field of parameter verification. In the method, key features are extracted from to-be-calibrated parameters of an ECU calibration file, and a structured parameter feature vector is constructed according to the key features; candidate parameters with the similarity with the structural parameter feature vectors higher than a threshold value are screened out from a preset calibration library through a mixed matching algorithm, each candidate parameter is endowed with a confidence score, and a candidate parameter set is constructed based on the candidate parameters; recommending an optimal calibration path according to the candidate parameter set, and calibrating the to-be-calibrated parameters according to the optimal calibration path to obtain calibration parameters; and performing basic rule verification on the calibration parameters by using a rule engine, performing anomaly detection on the calibration parameters by using a machine learning model, and determining whether the calibration parameters meet requirements or not according to a basic rule verification result and an anomaly detection result. By implementing the technical scheme provided by the invention, the parameter verification accuracy and efficiency of the ECU calibration file are improved.