1211results about "Execution paradigms" patented technology

The invention discloses an automatic compiling and adapting method for an RISC-V extension instruction, and belongs to the technical field of compilers. According to the method, a dynamic DSL (Digital Subscriber Line) for registering a custom instruction, adding register use constraints and defining a specific code mode is designed. The LLVM plug-in is used for automatically integrating the registered custom instruction in the compiling process. The invention relates to a self-defined instruction rapid adaptation mechanism which is specially provided for a continuously provided RISC-V self-defined instruction expansion scene, and by automatically generating correct assembly codes of registered self-defined instructions, correct registers are automatically distributed and reserved according to register use constraints; the method comprises the following steps of: searching a defined specific code mode and semantics, and automatically generating and inserting a registered custom instruction in a program, so as to realize the rapid adaptation of a compiler to an RISC-V custom extension instruction and the automatic use of a program code to the custom instruction, and the whole process does not need a developer to manually modify the compiler or a program source code.

The invention provides a tool calling method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data and the technical field of artificial intelligence. The method comprises the steps of obtaining target task information, inputting the target task information into a large language model for analysis, and generating a task planning response; a tool calling strategy is determined based on the task planning response, and the process of determining the tool calling strategy comprises the steps of judging whether an external tool is called or not and determining the type of the tool; under the condition that an external tool needs to be called, utilizing a first cue word template to guide the large language model to extract parameter information of a target tool from the target task information; and calling the target tool based on the parameter information to obtain first task response information returned by the target tool.

The invention discloses an intelligent routing and unified adaptation method for a large language model, and the method comprises the steps: defining all access details of the model through a declarative configuration file, and achieving the zero-code access of the large language model without writing any code for a newly-added model; a completely consistent calling interface is provided for an upstream application, the isomerism of all downstream large language models is shielded, and a unified request and response abstraction layer is constructed; through a strategy engine and a JSON path technology, complex streaming response including content thinking is precisely processed, and intelligent analysis and content extraction are carried out; dynamic configuration and intelligent strategy hot update are supported; intelligent routing of the model is realized, and an optimal large language model instance is dynamically selected; and meanwhile, enterprise-level governance capability is provided, governance functions such as fusing, degradation, current limiting and monitoring are integrated, and stability guarantee is provided for model calling. Therefore, the maintainability, the expandability and the user experience consistency of the system are comprehensively improved.

The invention relates to the technical field of data security, in particular to a data desensitization method and system based on combination of an MCP protocol and rule driving. The method comprises the following steps: loading a service rule through an MCP Server and registering a desensitization tool as a standardized tool; user query is received through the MCP Client, and user intention is analyzed by the AI agent; the AI agent dynamically calls a standardized tool to form a desensitization task chain according to a user intention and a business rule; and executing the desensitization task chain to process the target data, wherein the calling process comprises dynamic strategy adjustment based on context information. The system comprises an MCP service layer, a desensitization tool library module and an intelligent decision and integration layer. Through MCP protocol standardization integration, service rule driving and AI agent decision making, dynamic weight distribution and probabilistic algorithm selection of a desensitization strategy are achieved, the flexibility, the intelligent level and the system integration efficiency of data desensitization are effectively improved, and meanwhile the safety and compliance guarantee capacity is enhanced.

The invention discloses a large model calling system and method based on an MCP tool chain, and the method comprises the steps: receiving user input, and carrying out the preliminary matching of tools: querying whether the user input contains an entity in an entity database or not through a fuzzy matching technology, and carrying out the preliminary screening of a plurality of candidate tools based on a matching result, so as to obtain limited to-be-selected tools; user input is converted into semantic vectors, semantic matching is conducted in a vector database, and semantic similarities of all tools to be selected are ranked through a similarity calculation method; extracting key information in user input; abstracting the user input into a specified format; and matching the specified format with the historical workflow template, and if the historical workflow template is matched, replacing the key information to a corresponding position of the historical workflow template by adopting a structure-preserving parameter replacement algorithm to realize workflow reuse. According to the method and the device, a complete technical link from accurate tool screening to intelligent workflow multiplexing can be realized.

The invention relates to the technical field of e-commerce, and discloses an e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities. According to the invention, millisecond index calculation and real-time alarm of full customer service dialogues are realized through a bypass monitoring technology, a traditional lagging mode of post sampling inspection is changed, and management response is converted from passive remedy to active intervention; a high-concurrency pressure simulation module reproduces a business scene in an e-commerce peak period by simulating a script of a real user behavior and a distributed cluster, and a real basis is provided for system capacity planning and stability guarantee; the integrated intelligent verbal skill evaluation module deeply analyzes the dialogue quality from multiple dimensions such as semantics, compliance and emotional change based on a natural language processing technology, and generates an evaluation report with guidance, so that the professional level of customer service is improved; and the service quality, the operation robustness and the management intelligence level of the e-commerce customer service system are improved.

The invention relates to the field of DevOps intelligent execution, in particular to an MCP-based DevOps task intelligent arrangement and execution method and system, and the method comprises the steps: obtaining a tool function of each MCP server to form a global tool library; receiving a natural language task instruction of a user, and inputting the natural language task instruction into the large language model; performing task decomposition on the natural language task instruction to generate a structured tool calling plan; the client side executes the structured tool calling plan and calls target tool functions on the corresponding MCP server in sequence or in parallel through the MCP according to the execution logic and the dependency relationship; monitoring execution of each tool function, determining a subsequent process or triggering a preset rollback operation according to an execution state, and feeding back a final execution result to the large language model; and generating a natural language abstract containing the overall task execution state based on the execution result, and outputting the natural language abstract to a user. And cross-environment seamless operation automation is realized.

The invention discloses an intelligent tool calling method based on knowledge base matching, and the method comprises the following steps: S1, carrying out the blocking processing of prompt words of an MCP tool set, marking a feature tag for each tool, and forming a tool prompt word knowledge base; s2, obtaining a question text input by a user, carrying out semantic matching on the question text of the user and the feature tag of the tool cue word knowledge base, calculating the similarity and obtaining a corresponding matching result; s3, according to the matching result, entering the tool cue word knowledge base to recognize and automatically output a matched tool name; s4, if the user confirms that the tool name is accurate, tool execution is started; if the user neglects the tool name, displaying a tool list arranged according to a similarity descending order to the user for manual selection; s5, calling a tool confirmed or selected by the user and obtaining an execution result; and S6, summarizing the execution result through a large language model, and returning the summarized execution result to the user. The problem that a user cannot accurately call the optimal MCP tool is solved.

The invention provides a DPU-based remote direct memory access queue pair state machine implementation method and device, and the method comprises the steps: creating a flag bit, and setting the value of the flag bit according to each state of a queue pair state machine, so as to achieve state switching; creating a queue pair, and entering a reset state; configuring attributes of a queue pair, and switching to an initialization state; setting parameters of a receiving end, and switching to a ready receiving state; establishing communication connection, setting parameters of a sending end, switching to a ready-to-send state, and executing remote direct memory access operation so as to perform data transmission; if all the data in the sending queue is sent, switching to a sending completion state; if the sending queue has an error, switching from a sending preparation state or a sending completion state to a sending error state; if the queue pair has an error, switching from the current state to the queue pair error; and switching to a reset state after processing an error. According to the method, the flexibility and expansibility of the system are improved, and the processing efficiency of error and abnormal conditions is improved.

The invention is suitable for the technical field of communication, and provides an equipment control instruction generation method and device, equipment and a medium, and the method comprises the steps: obtaining a plurality of instruction channel sampling data related to a command instruction under the condition that the time sequence initial characteristic quantity of any instruction channel is detected to exceed a preset threshold value; sampling preprocessing and feature fusion processing are carried out on the multiple instruction channel sampling data, and an initial scene block is generated; and performing semantic analysis and entity anchoring processing on the compression vector through a lightweight convolutional network constructed based on the initial scene blocks to obtain a deterministic intention map. And carrying out topological sequence disassembly and sparse dependency matrix expansion on the deterministic intention graph to generate an action account book. And performing parameter completeness verification and hash calculation on the action account book according to a preset device interface rule, generating a device control instruction, and calling a plurality of devices corresponding to the command instruction. False execution caused by clock drift, model uncertainty or resource competition can be eliminated.

The invention belongs to the technical field of simulation, and particularly relates to a simulation result display method and device, a storage medium and equipment, and the display method comprises the steps: obtaining a global program counting address and instruction information according to a firmware logic file, and constructing a reference address mapping table based on the global program counting address and the instruction information; wherein the firmware logic file is a file for storing firmware execution logic related information; obtaining a firmware program counting address according to the firmware signal file; wherein the global program counting address comprises the firmware program counting address, and the firmware signal file is a file for storing response signal related information generated when the firmware executes the instruction information; in the execution process of playing back the instruction information, displaying the firmware program counting address based on the firmware program counting address and the reference address mapping table; according to the method, the debugging efficiency between the software and the firmware is improved.

The invention relates to the technical field of computer data processing and network security, in particular to an illegal character automatic cleaning system, which comprises a rule compiling module for monitoring rule change, performing semantic fusion and topological mapping on a rule set, constructing a deterministic finite automaton and mapping the deterministic finite automaton into a state transition table; the state switching module is used for constructing a double-buffer context and operating lock-free switching through an atomic pointer to realize hot updating; the speculation execution module is used for carrying out vectorization pre-scanning based on a state transition table by utilizing single-instruction multi-data stream parallel loading, identifying a walk path, falling into a safe state for releasing and falling into a trap state for triggering external verification; the self-adaptive feedback module is used for counting trap state triggering frequency, generating a rule allergy report and dynamically adjusting the size of a read fragment; according to the method, the contradiction between rule flexibility and execution efficiency is solved, and high-performance cleaning based on speculative execution is realized.

Techniques may generate realistic synthetic data by programmatically generating a configuration file object type and relationship data. This configuration file may be used to retrieve source data matching the object type(s) and / or specific records indicated by the configuration file. The techniques may detect and anonymize private / proprietary information and may determine statistical characteristic(s) of the source data. A batch of prompt(s) may be generated using the source data, the statistical characteristic(s), and the configuration file and may be transmitted to one or more instances of a transformer-based machine-learned model. Sets of synthetic data received from the model instance(s) may be de-duplicated, checked for similarity to the source data (e.g., via embedding the synthetic data and the source data), and may be used to generate synthetic object(s) using the relationship(s) and / or other data indicated by the configuration file. These synthetic object(s) may then be deployed in a software environment.

The invention discloses a construction and execution method of an interactive AI class based on structured data, and related equipment and system. According to the invention, each event of an interactive AI class is described by using structured data to generate a script. When the AI class is executed, a script composed of the structured data is analyzed to form events, and the events are distributed to the execution modules to be executed. The events comprise core events and non-core events, and are also divided into playing events and interaction events. Moreover, when the event is the interaction event, the interaction operation of the user is detected, and the execution of the interaction event is controlled according to the detection result. According to the method, the source data standard of the interactive AI course is unified, so that the interactive AI course has universality and good expansibility, and a foundation is established for accessing more intelligent interaction functions.

The invention discloses an interface calling control method for a cake electronic coupon API (Application Program Interface) docking system. The method relates to the technical field of interface call control, and comprises the following steps: evaluating high concurrency stability of an interface; high-concurrency stability optimization and circulation are realized; evaluating management and control compliance and problem tracing efficiency; and controlling compliance optimization and iterative closed loop. According to the method, interface calling characteristic parameters and compliance-traceability parameters are collected in real time, a double-layer evaluation optimization system from interface stability to management and control compliance is constructed, bimodal regulation and control of a current-limiting threshold value and cache frequency are intelligently triggered based on stability evaluation, the verification request amount and a positioning mode are dynamically adjusted based on compliance efficiency evaluation, and the reliability of the system is improved. A complete closed loop of monitoring-evaluation-optimization-iteration is formed, the interface calling stable operation capability and compliance control efficiency of the cake electronic coupon API system in a high-concurrency scene are improved, and the problem of low interface calling control stability in the high-concurrency scene in the prior art is solved.

The invention provides an automatic operation and maintenance platform implementation method. The method comprises the steps that operation and maintenance work responsible for a plurality of operation and maintenance groups is split into basic function units; for each basic function unit, in response to acquisition of a task configuration instruction for the basic function unit, establishing a corresponding atomic task for the basic function unit; based on all the atomic tasks and the operation and maintenance work execution sequence of the operation and maintenance groups, constructing a directed acyclic graph reflecting the execution dependency sequence of the atomic tasks; determining an executable task sequence based on the directed acyclic graph; and according to the executable task sequence, calling a uniform interface to distribute the atomic tasks in the executable task sequence to an agent program for execution. According to the technical scheme, the automation level of the operation and maintenance platform is remarkably improved.

The invention discloses a code quality verification method and device, equipment and a storage medium. The method comprises the steps that a to-be-verified target code file and verification context information corresponding to the target code file are received; based on the verification context information, screening out a verification rule set meeting a preset requirement from a rule base, and arranging the verification rule set to generate a rule set arrangement task; calling a corresponding verification execution engine to perform automatic verification on the target code file according to the characteristics of each rule in the rule set arrangement task, and generating an initial verification result; and carrying out fusion processing on the initial verification result to obtain a fusion result, and carrying out risk grading on the fusion result based on a preset priority strategy to generate a standardized verification result. According to the code quality verification method and device, the verification rule set meeting the preset requirement is screened out from the rule base based on the verification context information, the target code file is automatically verified according to the rule set arrangement task, and the reliability and the verification efficiency of code quality verification are improved.

The invention provides a truss structure autonomous design system and method based on multiple agents. A layered multi-agent architecture is adopted, a general control agent is taken as a core, and a high-level design intention is analyzed and a plurality of downstream special agents are arranged for cooperative work; the whole design process realizes automatic closed loop through a graph-based workflow engine; the core innovation of the method is a hybrid intelligent optimization algorithm of LLM heuristic exploration and genetic algorithm accurate fine tuning, and the algorithm is driven by an original and quantifiable design quality scoring system to efficiently carry out analysis-diagnosis-optimization iteration so as to cooperatively improve the safety and economy of the design. The design time can be shortened from several hours to several minutes, about 7.0% of material consumption is saved on average, it is ensured that the scheme completely conforms to industry specifications, the design efficiency, economy and reliability are greatly improved, and meanwhile the use threshold of professional design software is remarkably reduced through natural language interaction.

The invention provides a security alarm noise reduction and automatic disposal method based on an intelligent agent, and belongs to the technical field of network security. Comprising the steps of collecting and preprocessing security alarm data, and constructing a noise reduction rule base, a noise reduction knowledge base and an alarm processing knowledge base; a noise reduction small model is finely adjusted by using an open source large model, a noise reduction knowledge base and a knowledge distillation technology, and a script generation large model is finely adjusted by using an alarm disposal knowledge base based on a general reasoning large model; performing two-stage noise reduction on the safety alarm by using a noise reduction rule base and a noise reduction small model to obtain an effective alarm; generating a corresponding disposal script through the large script generation model; creating a task allocation agent and a disposal agent; and after the feasibility of the script is manually judged, the task distribution agent delivers the script to the corresponding disposal agent for execution, and the similarity and success rate of historical cases are fused to assist in judging a subsequent disposal mode. According to the invention, the efficiency and accuracy of security alarm processing are effectively improved.

The invention discloses a Qt4 cross compiling environment construction method and system oriented to an aarch64 architecture, and the method comprises the steps: installing a dependency library in a host machine, which comprises a dependency library used for development under a host machine architecture and a runtime dependency library in a target machine operating system, a proper cross compiling chain is installed, a libc library in a target machine is used for replacing a corresponding library in the cross compiling chain, Qt4 source codes are modified, support of atomic operation of an aarch64 architecture processor is added, compiling parameter configuration and compiling are carried out, and generated Qt4 components are installed and deployed in a host machine and the target machine respectively. According to the method, the problem of compiling and deploying a large project constructed by using Qt4 in an aarch64 architecture processor is solved, meanwhile, the programs can be debugged and compiled in a PC (Personal Computer) of a host machine architecture, a target is stacked in a target machine of the aarch64 architecture to run, and great convenience is provided for debugging and running of the programs.

The invention provides a finite-state machine extraction method and device for logic system design and a storage medium, and the method comprises the steps: determining a signal node based on a condition trigger signal and an assignment signal of a process block in a compiling partition, so as to construct a first finite-state machine tree; associating a condition assignment statement for assigning the condition trigger signal to the corresponding signal node in the first finite-state machine tree to obtain a second finite-state machine tree; determining a signal value of the signal node based on a conditional statement corresponding to the conditional trigger signal and the conditional assignment statement, and updating the second finite-state machine tree based on the signal value to obtain a third finite-state machine tree; updating corresponding state transition information between the signal values of the signal nodes in the third finite-state machine tree to obtain a fourth finite-state machine tree; and combining the fourth finite-state machine trees to obtain a global finite-state machine result.

The invention discloses a finite state inquiry method and system based on a large model and a knowledge graph, and relates to the field of business handling, when there is a business handling intention, a finite state machine sets an initial business state, and a large language model analyzes, inputs and extracts keywords and demands to determine a current state; the user naturally converses with the big language model, and the big model is guided to complete the process according to the knowledge graph and the current state; when the user interrupts questions, the large model answers the questions according to the knowledge graph, and then the finite-state machine records the state to help the user to seamlessly return and continue to handle the questions; according to the method, the problem of large model illusion is effectively solved, and the accuracy and stability of user intention judgment are improved; intelligent autonomous business handling without manual intervention is realized; the seamless recovery process after the user interrupts questions at any time is supported, and the flexibility and coherence of business handling are enhanced; comprehensive knowledge coverage is ensured by means of a knowledge graph, and user questions are accurately answered; and meanwhile, the dependence on manual business personnel is reduced, and the labor cost is reduced.

The invention provides a dynamic human-machine interface generation system for a power conversion device, and the system comprises a configuration file which is used for defining interface elements, attributes and layout rules of an HMI in a structured language; the configuration analysis module is used for reading and analyzing the configuration file and converting the configuration file into a structured object model containing control types, attributes and layout information; the control factory module is connected to the configuration analysis module and is used for dynamically creating a corresponding control instance according to the control type information in the structured object model and injecting the analyzed attribute information into the control instance; the layout engine module is connected to the control factory module and is used for automatically arranging and positioning the control instances according to the layout information in the structured object model; and the rendering output module is connected to the layout engine module and is used for rendering and outputting the interface of which the layout is completed to display equipment. According to the method, diversified HMIs are dynamically generated according to different requirements, and the purpose of improving code quality and development efficiency is achieved.

The invention provides a method and a system for dynamically calling a kernel unexported function, which are used for dynamically acquiring a function address of an unexported kernel function and forcibly calling the function through a kProbes mechanism, matching with a function pointer type definition and a mechanism for forcibly converting the function address into a function pointer. The method not only supports the calling of the kernel function which is not exported, but also does not need to carry out hard coding on the address of the called kernel function, supports the dynamic acquisition of the address of the kernel function, avoids the change of the coincidence address caused by the difference of kernel versions, greatly enhances the compatibility of the software package of the kernel function which is not exported, and improves the reliability of the software package. And the packet does not need to be recompiled in each kernel small version, so that the problem that the kernel symbol table is not matched and the kernel module cannot be loaded is avoided.

The invention provides an automatic testing method, device and platform for equipment load function performance, and relates to the technical field of software testing. According to the invention, through setting the plurality of subprogram packages in the process of testing the load function performance of the equipment, automatic programming is carried out according to the equipment type, the load type and the function type, the modularized combination generation of the test item, the test program and the subprogram is realized, the professional threshold is reduced, and the generation speed of the test program is improved. Furthermore, in combination with the task queue of the test accompanying instrument, path optimization is carried out on the test process, the test sequence of a plurality of test items is optimized, and the waiting time is shortened. Finally, based on the testing process and the testing program, automatic testing is achieved, the problem that the testing efficiency is low in the equipment load function performance testing process is solved, and the testing efficiency of the equipment load function performance testing is improved.

A system and method are disclosed for identifying malicious activity on a target device based on behavior analysis of the target device. The system includes a behavioral analyzer run on a virtual machine connected to the target device. The virtual machine collects system events and parameters from the target device and run a script, independent of the target device, to detect a threat. The script is a set of instructions executed to analyze behavior of an object by processing and correlating the events. The script includes a rule structure which stores signatures and expressions of the known malwares. By correlating the selected event parameters with known malware parameters, it is determined whether the event imposes a threat or not. A finite state machine is used for the state transition table.

The invention relates to the technical field of industrial automation control, and provides a method and system for achieving online simulation of an HMI simulator, and the method comprises the steps: monitoring the starting and offline simulation process of an original HMI programming software simulator, and confirming that the original HMI programming software simulator completes the generation of a configuration file; receiving a mode switching instruction of a user through the mode switching interface; in response to the mode switching instruction, terminating the current simulator process; modifying a configuration file generated by the simulator, modifying an operation mode parameter in the configuration file from a first value representing offline simulation to a second value representing online simulation, and dynamically configuring a communication port parameter; the verification information of the configuration file is updated to ensure the legality of the file; and restarting the simulator, and loading the modified configuration file by the simulator so as to establish communication connection with an external PLC (Programmable Logic Controller) or instrument and enter an online simulation mode. On the premise of not depending on entity hardware, online communication and real-time joint debugging of the HMI simulator and the external equipment can be realized.

Provided herein is a PCIe interface device. The PCIe interface device may include a NOP DLLP generator configured to generate a No Operation (NOP) data link layer packet (DLLP) including event information representing an event in response to the occurrence of the event and a transmitter configured to transmit the NOP DLLP to an external device through a link including a plurality of lanes.

Disclosed in the embodiments of the present application are a task execution method, an electronic device and a storage medium, which are used for improving the efficiency of executing a task corresponding to a standard procedure. The task execution method comprises: receiving a task request; on the basis of the task request, acquiring a first standard procedure from a standard procedure information base; on the basis of the task request, acquiring a first sub-standard procedure from the first standard procedure, the first sub-standard procedure being a sub-standard procedure of the first standard procedure, the first sub-standard procedure being used for instructing a plurality of first sub-tasks to be successively executed on the basis of a jump relationship, the first sub-standard procedure comprising a first root node, and the first root node being used for instructing executing a start sub-task among the plurality of first sub-tasks; and, on the basis of the first sub-standard procedure, executing the plurality of first sub-tasks.

A system for processing client requests in an AI ecosystem is provided. The system may receive a client request from a client application, where the client request is based upon a user request. The system may provide a model request, based upon the client request, to a first model (e.g., an LLM), receive, from the first model, a structured response based upon the model request, and cause execution of tool functions based upon the structured response.