8356 results about "Exploit" patented technology

The invention relates to the technical field of data processing, in particular to an intelligent document updating processing method and system.The intelligent document updating processing method comprises the steps that part parameters and coding rules are extracted from engineering design file metadata, a part list and a PDM and PLM system, the semantic association relation between parts is analyzed through a knowledge graph, and the mapping relation between logic identifiers and physical files is constructed; generating an initial version file library; and monitoring file names and version changes in real time based on a micro-service architecture, calling a simulation service in combination with a knowledge graph to verify parameter compatibility, screening an optimal version, updating the optimal version to a main version library, optimizing historical conflict data in a block chain evidence storage index by using a genetic algorithm to generate a standardized code, and outputting a cross-platform parameter mapping table. According to the method, the problems of file name change, non-standard coding conflict, version control missing and cross-platform adaptation incoherence are solved, and the data tracing efficiency, the version management reliability and the system compatibility are improved.

The present invention discloses a system and method for performing artificial intelligence (AI) safety red-teaming with integrated policy fuzzing and adversarial prompting to systematically identify, characterize, and mitigate unsafe or non-compliant behaviors in AI models. The disclosed invention automates the process of generating, executing, and analyzing adversarial test cases through coordinated functional units comprising a policy fuzzing unit, an adversarial prompting unit, an execution sandbox, a telemetry processing unit, a scoring and triage processor, and a cryptographic provenance processor. The system applies grammar-driven and reinforcement-based fuzzing techniques to vary policy descriptors, model configuration parameters, and instruction hierarchies, while a learned adversarial prompt generator synthesizes contextually coherent adversarial prompts optimized for maximum policy violation likelihood. The generated prompts and policy vectors are executed in an isolated, instrumented sandbox that records input-output interactions, timing characteristics, and intermediate representations.

The embodiment of the invention provides an information generation method and device, electronic equipment and a storage medium. The information generation method comprises the following steps: in response to an obtained question and user information, calling a first agent to determine a target intention based on the question and the user information; a second agent is called to determine a to-be-executed task corresponding to the target intention, the second agent is called to execute the to-be-executed task to generate target information, the target information serves as reply information of the target intention to be sent to the client side, and the first agent and the second agent are responsible for intention confirmation and task planning respectively. According to the technical scheme, the intelligent agent has the learning ability and can make corresponding actions based on the change of the environment, so that the target intention can be determined according to the problem of the user and the experience of similar problems appearing before, the to-be-executed task is further determined, the efficiency of processing the user problem is finally improved, and the user experience is improved.

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention relates to the technical field of front-end resources, in particular to an intelligent caching method and device for loading front-end resources on demand, equipment and a medium, and the method comprises the following steps: obtaining user behavior data, predicting an access page, identifying required resources, and constructing a resource dependency graph; calculating resource use frequency and importance, and determining a target resource; selecting a resource format and a compression mode according to the user equipment type; according to the resource dependency relationship graph, loading and caching target resources required by a next page possibly accessed by the user in advance; meanwhile, the caching strategy is dynamically adjusted based on the resource use frequency and the importance index; the method comprises the following steps: monitoring a network condition, dynamically adjusting a resource loading strategy, pre-loading resources before a user requests by using a resource dependency graph and a server pushing technology, and determining a target loading mode when the user accesses. Therefore, the problems that in the prior art, resource loading is low in efficiency, a caching strategy is rigid, environment adaptability is poor, user behavior analysis is insufficient, and performance optimization automation is lack are solved.

A process scheduling method and apparatus, a device and a storage medium, relating to the technical field of production scheduling. The method comprises: receiving a process scheduling request (S201); in response to the process scheduling request, determining an instruction being executed, an instruction to be executed, and scheduling resource information (S202); establishing an objective function and a constraint condition on the basis of the instruction being executed, the instruction to be executed, the scheduling resource information and corresponding actual scheduling information (S203); optimizing the objective function under the constraint condition by means of a swarm intelligence optimization algorithm to obtain an optimal solution of the objective function (S204); and using the optimal solution to determine an instruction execution policy, so as to perform process scheduling (S205). According to the process scheduling method, scheduling resources such as instruments and devices can be fully utilized, and the continuously growing demand for production scheduling is met.

Methods and systems for assessing a software bill of materials (SBOM) include building a knowledge graph from repositories, using function fingerprints of software packages in the repositories. Dependencies of an application are identified using function fingerprints of the application and comparing to function fingerprints of the software packages. A quality score for an SBOM of the application is generated based on a comparison of the identified dependencies to claimed dependencies of the SBOM. An action is performed responsive to the quality score.

The invention discloses a memory fault repairing method and device, equipment, a medium and a computer program product, and relates to the technical field of computers.The memory fault repairing method includes the steps that memory error information sent by a memory controller is obtained, a fault target memory page can be positioned, and a standby memory page is obtained from a standby memory pool; the memory address mapping table is updated, the physical address of the target memory page with the fault is mapped to the physical address of the standby memory page, the repairing process does not depend on triggering of a system management interrupt mechanism, memory repairing in the system running stage is achieved, the system downtime caused by memory fault repairing is shortened, and therefore the memory repairing efficiency is improved. The problem of high delay in memory fault processing can be solved, and the technical effect of improving the memory fault repairing efficiency is achieved.

The invention relates to the technical field of hard disk testing and verification, in particular to a multi-thread-based solid state disk reliability automatic testing method and system.The method comprises the steps that firstly, SMART information is deeply analyzed through microsecond-level high-granularity continuous performance monitoring, and multi-thread parallel processing is assisted; according to the method, fine performance fluctuation of the solid state disk under the concurrent load can be quickly captured, a fault mode can be identified, then early warning is realized by utilizing the extracted multi-dimensional features and a machine learning model, and a detailed fault diagnosis report is generated; and through dynamic error correction code strength verification and data integrity verification under pressure, an internal error correction mechanism of the solid state disk is actively detected and optimized. And finally, in combination with prediction reliability modeling, the system can estimate the remaining service life and predict faults, and provides product optimization suggestions for design, manufacturing and firmware optimization of the solid state disk, so that automation, intelligence and full life cycle management of the fault detection reliability of the solid state disk are realized.

The embodiments of the present application disclose a speculative decoding optimization method, an electronic device and a storage medium, wherein a speculative decoding optimization method is used for a draft model and a target verification model, the method comprising: inputting the draft content generated by the draft model in the previous round into the target verification model; performing parallel verification on the tokens in the draft content in the target verification model; using a lightweight draft length predictor to analyze the latent vector of the last received token position and the last token generated, and predicting the optimal draft length for the next round; and autoregressively generating a corresponding number of drafts based on the optimal draft length predicted by the lightweight draft length predictor through the draft model.

The invention relates to a fuzzy testing method, device and equipment based on a large language model, and the method comprises the steps: analyzing and analyzing RFC standard documents of various network protocols through the large language model, and extracting protocol rules and constraint conditions; according to the protocol rule and the constraint condition, a large language model driving method is adopted to generate a test case conforming to protocol characteristics; and performing a differential test through the test case, and verifying the consistency between the actual software of the network protocol and the RFC standard document. According to the invention, the accuracy and security of implementation of the complex network protocol can be improved, and the test efficiency and coverage rate are optimized.

Accurate prompting improves the operation, efficiency, and output of computer models, such as language models. The disclosed systems and methods improve interaction with computer models by facilitating the generation of accurate prompts for targeted interactions with computer models. For example, disclosed systems can be configured to store prompts and responses generated with computer models in session memories. The system can use information in session memories to generate combined prompts when receiving prompts that refer to previous prompts or answers. The system improves prompt accuracy by generating combined prompts—formed by combining the context from the stored information (e.g., in context sub-prompts) and instructions in the new prompt (e.g., in instructions sub-prompts). The system can generate responses based on the combined prompts allowing the computer models to leverage the context in previous interactions, without burdensome or complicated prompts, for more meaningful or accurate responses.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The embodiment of the invention discloses a causal chain-based front-end state anomaly detection method and system. The method comprises the following steps: S1, identifying a calling relation among an event monitor, an asynchronous task and a state change logic by analyzing a source code, and constructing an expected causal chain model; s2, dynamically verifying whether the actually occurring causal chain conforms to an expected model or not during the application running period; wherein in the step S1, the analysis of the source code comprises but is not limited to an AST technology; in the step S2, the hijacking mode of the key function comprises but is not limited to a Proxy mechanism, function packaging and a Hook technology. According to the method, the expected causal chain model is constructed, the actual path is dynamically verified during operation, system inconsistency caused by causal chain abnormity is avoided, the AI model is utilized to generate the repair strategy suggestions, a traditional low-efficiency troubleshooting process depending on logs and breakpoint debugging is upgraded to intelligent decision, and the repair efficiency is remarkably improved.

Systems and methods for detecting and mitigating prompt injection attacks on a generative LLM are disclosed. A deployment scenario is considered, in which the generative LLM supports a task automation function. Prompts are received and interpreted by the generative LLM, and outputs from the generative LLM are used to trigger automation actions. The prompts are constructed based on a combination of user input and external data and are, therefore, vulnerable to prompt injection attacks though manipulation of the external data. To mitigate this risk, a separate discriminative classification, decoupled from the generative LLM, engine is configured to identify malicious prompts, and filter out any malicious prompts before they reach the generative LLM.

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 test case determination method and device, a computer readable storage medium and a processor. The method comprises the following steps: identifying a source code of software by utilizing a pre-trained large model to obtain a plurality of functional modules in the software and a dependency relationship among the plurality of functional modules; in response to updating of the source code of the software, determining a code updating part in the source code; determining a test case matched with the code updating part from a test case library of the software based on the code updating part and a dependency relationship among the plurality of functional modules, the test case being used for testing the code updating part; based on a test result of the test case, adjusting a selection strategy of the test case; and re-determining the test case for testing the code updating part based on the adjusted selection strategy of the test case. The technical problem that a traditional regression test strategy is low in test efficiency is solved.

The invention relates to a multi-type industrial equipment PLC control method and device based on a hardware abstraction layer, and the method comprises the steps: constructing the hardware abstraction layer comprising a core layer and an adaptation layer; the core layer is provided with a standardized interface set, the adaptation layer is provided with a special adaptation module, and a control protocol is converted through a protocol analysis engine. And generating a unified instruction set based on the standardized interface set, and establishing a data interaction channel supporting priority scheduling. And identifying the newly-added equipment, matching the equipment configuration library, and generating a newly-added adapter drive if the equipment configuration library fails. And acquiring a device state by using a data interaction channel, and constructing a cooperative control logic to realize linkage. Equipment load and task queues are monitored in real time, and dynamic task scheduling and grading fault processing are carried out. And when an adapter configuration updating instruction is detected, the adaptive layer thermally loads the updated drive, and unified, intelligent, efficient and stable PLC control of multiple types of industrial equipment is realized through cooperation of the core layer and the adaptive layer.

The invention belongs to the technical field of computing power scheduling, and particularly relates to a computing power scheduling method in a distributed computing environment. According to the task scheduling method and system, the task needing to be processed at present is obtained through the task management system, the task is divided into the real-time task set, the priority task set and the common task set, and the related tasks are scheduled by adopting the corresponding scheduling strategy based on the set corresponding to the current task and the priority in the set. Resource scheduling and allocation in a distributed computing environment are realized; according to the method, through the steps of task classification, real-time resource monitoring, resource demand estimation, priority ranking, reasonable scheduling strategy, load calculation, resource migration and the like, the technical problem of computing resource scheduling and allocation in a distributed environment is effectively solved, timely processing of high priority is ensured, the system load is balanced, and the system performance is improved. And efficient utilization of resources and stable operation of the system are realized, so that the performance and reliability of the whole distributed computing environment are improved.

Methods, systems, and non-transitory computer readable storage media are disclosed for controlling access to artificial intelligence systems based on determined intent of queries. The disclosed system utilizes one or more digital content analysis models to determine an intent of one or more queries to an artificial intelligence system. The disclosed system utilizes the one or more digital content analysis models to determine an intended use of the artificial intelligence system. Additionally, the disclosed system determines whether the intent of the one or more queries aligns with the intended use of the artificial intelligence system by generating a similarity score and comparing the similarity score to a similarity threshold. Based on whether the intent aligns with the intended use, the disclosed system executes computing instructions to enable or block the one or more queries from being processed by the artificial intelligence system.

The invention relates to the technical field of distributed file processing, in particular to a distributed file processing method and system, and the method comprises the following steps: based on a task distribution node and a data exchange interface, extracting path characteristics of a task input / output file, analyzing a file exchange sequence of a task path, and calculating a topological structure and length of the path. According to the method, the flexibility and the adaptability of task allocation are realized by combining task state monitoring and real-time adjustment of dependent path weights and task priority marks, resource allocation and a short-time task sequence are optimized by utilizing request frequency and time period records, high-frequency request resource conflicts are effectively relieved, the resource allocation efficiency is improved, and the resource allocation efficiency is improved. Through node historical resource distribution data and time distribution trend analysis, task distribution is adjusted and resource loads are balanced in combination with differences, the stability and balance of the system are enhanced, a file transmission path is optimized based on a load state and a task execution path, and a concurrent task distribution strategy is improved.

The present invention relates to adaptive multi-state encoding and processing in virtualized computing environments. The system includes a virtualized state selection module that dynamically transitions between binary, ternary, quaternary, and higher-order encoding states based on workload, bandwidth, security posture, and compliance requirements. A virtual encoding engine utilizes hardware-accelerated components, such as vFPGAs, vGPUs, and cTPUs, to enhance encoding throughput. A compliance-driven feedback controller continuously monitors encoding efficiency, threat levels, and adherence to mandates such as GDPR, HIPAA, and FIPS 140-3. Additional features include AI-based anomaly detection, federated model refinement, distributed ledger-backed audit trails, and quantum-resistant encoding techniques. By integrating intelligent encoding decisions with scalable compliance enforcement, the system enables high-performance, secure, and regulation-ready data processing across distributed cloud and edge environments, delivering measurable improvements in system responsiveness, data integrity, and operational trust.

The invention relates to the technical field of artificial intelligence, and provides an operator parallel processing method, electronic equipment, a storage medium and a program product, and the method comprises the steps: determining a step of data dependence based on each processing step of a first operator and a second operator; and performing parallel execution on each processing step of the first operator and the second operator by applying a synchronization strategy and a segmentation strategy based on the plurality of cooperative thread bundles. According to the method, the parallel fusion processing of operators is realized through the steps of determining data dependence, segmenting the data, realizing parallel execution by utilizing the cooperative thread bundle, inserting synchronization between the data dependence steps and the like, the calculation performance is remarkably improved, and meanwhile, the data blocks after segmentation are multiplexed, so that the data processing efficiency is improved. The data dependence in the operators and between the operators can be reduced, and the overhead of data reading, instruction synchronization and the like can be reduced.

Systems, apparatus, articles of manufacture, and methods are disclosed to utilize large language artificial intelligence models to convert computer code. An example apparatus includes instructions and processor circuitry to execute the instructions to at least: train a large language model based on a computer instructions repository that includes code of a first type; utilize the large language model to convert an input set of instructions of the first type into output code of a second type; cause execution of the output code; determine if the execution is successful; and when the execution is not successful, utilize the output code for fine-tuning training of the large language model with incorrect data.

The embodiment of the invention provides a data processing method, end side equipment, a storage medium, a chip system and a computer program product, and relates to the technical field of terminals. Due to the fact that the second expert modules in the first storage are formed in the mode that the second thread executes the preloading process based on the expert preference strategy of the first request under the condition that the first thread executes the prefilling stage process through the first model, parallel execution of the preloading process and the prefilling stage process is achieved, I / O delay is reduced, and the processing efficiency is improved. And the reasoning efficiency of the first model is improved. And because the first request has expert preference, and the plurality of second expert modules stored in the first memory are determined according to the first request, the first thread performs the decoding stage flow by using the first model, and the second thread performs the decoding stage flow by using the second model. The first thread can directly obtain the second expert module corresponding to the first expert module from the first storage, so that the cache hit rate is improved, and the reasoning efficiency of the first model is improved.

The invention discloses an intelligent agent automatic arrangement method and system based on a large language model, and relates to the technical field of artificial intelligence. The method comprises the following steps: decomposing a natural language instruction of a user into a structured subtask sequence by utilizing a first large language model; based on the agent portrait library, matching and allocating agents for each sub-task to generate an initial execution plan; task execution is scheduled and monitored in real time through an event-driven architecture; when abnormity is monitored, a self-adaptive adjustment mechanism is triggered, the affected plan part is re-planned, and an updating instruction is issued. According to the invention, efficient, flexible and robust multi-agent automatic arrangement is realized.

The invention relates to the technical field of chip task allocation, and discloses a task allocation optimization method and device for a multi-core heterogeneous ASIC (Application Specific Integrated Circuit) computing mainboard. Computing core setting information of the computing mainboard is collected, a mainboard computing performance model is generated through digital feedback processing, an executable task set is obtained, and the computing requirement of each task is analyzed; performing simulation analysis on the calculation demand set based on the performance model by using an optimization algorithm, generating a task allocation plan, identifying and generating task execution characteristics of a currently executed task, allocating the task execution characteristics according to the task allocation plan, and scheduling calculation resources to process the current task. The resource performance is accurately simulated through the performance model, the optimization algorithm ensures efficient resource allocation, responds to task changes in real time, maximizes the utilization of heterogeneous computing resources, improves the overall computing efficiency, and solves the problem of execution delay when real-time task allocation is carried out on the multi-core heterogeneous ASIC computing mainboard in the prior art.

The invention relates to the technical field of computing power scheduling, provides a distributed computing power scheduling method and system, breaks through the limitation that a traditional computing power scheduling system depends on a single index or a static strategy by constructing an integrated architecture of data perception, load prediction and intelligent scheduling, and constructs a self-adaptive and closed-loop optimized intelligent scheduling system. The scheduling system obtains multi-source data in real time and fuses the multi-source data into a perception vector to accurately describe the running state of the system, accurate pre-judgment of load changes is achieved through hierarchical load prediction, and resources can be efficiently allocated in different scenes in combination with double scheduling paths and execution feedback optimization; according to the scheduling system, the resource utilization rate of the distributed system is effectively improved, the energy consumption cost is reduced, the task execution delay is reduced, the system stability and the fault-tolerant capability are enhanced, and an innovative computing power scheduling solution is provided for a large-scale distributed computing scene.

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.