326 results about "Intermediate language" patented technology

The invention belongs to the technical field of equipment knowledge engineering and natural language processing, and discloses an LLM-based constraint and self-repairing uniformization equipment knowledge graph automatic construction method. The method comprises the following steps: firstly, acquiring equipment related document data through network collection, document arrangement and database query; then, utilizing an equipment domain ontology and constraints as preposed soft and hard constraints, driving LLM to generate semantic intermediate representation, and obtaining candidate triples through structured compiling; then, a self-repairing closed loop is formed through semantic unit testing, logic consistency detection and evidence binding verification, and triples which violate constraints and have conflicts or illusions are automatically recognized and repaired; and finally, entity standard identification, cross-document duplicate removal combination and conflict resolution are realized through cross-segment unification and incremental alignment. According to the method, the fragile path that the LLM directly generates the triple and blindly stores the triple is avoided, the illusion and inconsistency problems are effectively inhibited, and the correctness, interpretability and maintainability of the equipment knowledge graph are remarkably improved.

The invention provides a file uploading attack interception method based on semantic entropy enhancement, and aims at overcoming the defects of an existing file uploading security protection technology in the face of complex attacks. The method specifically comprises the steps that S1, file format analysis and content extraction are conducted, hidden scripts are mined through nested content recognition, and intermediate representation is generated through grammar cleaning and coding specifications; s2, constructing an abstract syntax tree and semantic entropy calculation, tracking a pollution chain, analyzing a high-risk function, identifying a high-entropy character string, modeling and controlling flow complexity, and generating a semantic entropy vector; s3, dynamic scoring and decision making are carried out, and accurate judgment is carried out in combination with white list perception, feature comparison, multi-modal model scoring, adaptive threshold and sandbox observation; and S4, carrying out real-time interception and feature synchronization, blocking malicious file landing, generating an attack log and synchronizing an attack fingerprint. The method takes the semantic entropy vector as a core, breaks through the limitation of static features, remarkably improves the recognition rate of complex attacks, reduces missed judgment, and guarantees the safety of Web applications.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention relates to code verification, in particular to a code and document consistency reasoning verification system based on a cross-modal logic atlas, which comprises an input layer for receiving source codes and software documents; the feature extraction layer is used for performing feature extraction on the source code and the software document; the abstraction layer is used for converting the extracted features into comparable intermediate representations; the unified modeling layer is used for mapping code logic and document semantics into a unified atlas structure and performing formalized representation by using description logic to obtain a cross-modal logic atlas; the reasoning verification layer is used for performing reasoning verification on the consistency of the code and the document according to the cross-modal logic graph; the result processing layer is used for judging a verification result according to the reasoning verification condition, starting a contradiction positioning mechanism when inconsistency is found, and carrying out contradiction positioning and report generation to obtain an interpretable report; according to the technical scheme provided by the invention, the defects that the consistency between the code and the document is difficult to accurately verify, and accurate contradiction positioning and an interpretable report are difficult to provide can be effectively overcome.

A software package is received and unpacked into multiple components comprising plural functions. Each function is lifted from machine code into static single-assignment intermediate representation and tokenized to produce semantics-preserving embeddings. Intermediate-representation data-flow features are extracted, including detection of constant static variables on a stack, stack reaching definitions, uninitialized variables, and intra-procedural aliases. For each component, the embeddings and features are input to a machine-learning model trained on semantic properties derived from a corpus of software packages to generate a software supply chain risk level. Data characterizing the risk level is provided to a consuming application. When the risk level satisfies a remediation criterion, a remediation action is initiated, including generation of a source-code patch recommendation for an identified root-cause function, insertion of a runtime guard into the component, or issuance of a security advisory for distribution to a security operations dashboard.

The invention relates to a public multi-mode cloud network resource software security enhancement method based on neural symbol fusion reasoning, which comprises the following steps: an intermediate representation generation stage: generating and optimizing an intermediate representation of a program through a fine-tuned large language model, and establishing semantic mapping from a source code to a structured logic representation; in the symbol language conversion stage, the intermediate representation generated by the large language model is converted into a domain-specific language fact set which can be recognized in the symbol logic reasoning stage, and formal and logic expression of program semantics is achieved; and a symbol logic reasoning stage: matching the fact set with the rule base through a symbol logic reasoning engine, performing detection and verification according to the safety rule, generating a structured report, and feeding back a result for optimization. The method is suitable for security enhancement of various core software systems in a public cloud network multi-modal network environment, high-precision security analysis is carried out on cross-modal and cross-subsystem fragmented codes in a compiling-free environment, and verifiable technical support is provided for public cloud network security control.

Data from manufacturing is highly uncontextualized and siloed, requiring expert knowledge of context and substantial data pre-processing to support meaningful queries and visualizations. To address this problem, data for a number of sources in a manufacturing context can be retrieved and converted into an intermediate representation in a natural language or near-natural language form, which can in turn be ingested by a generative AI engine, along with suitable prompts by the user to summarize, analyze, and make recommendations based on the data.

The invention provides a large-model-oriented heterogeneous computing power adaptive compiling method, which comprises the following steps that: a user inputs a trained large model through a system interface, and a system front-end conversion module analyzes a computational graph of the model and converts the computational graph into an intermediate representation based on a unified operator description language (UDL); the system hardware sensing module automatically detects and extracts hardware feature fingerprints of at least one piece of target hardware; based on the unified operator description language UDL intermediate representation and the hardware feature fingerprint, automatically generating an optimization adaptation rule oriented to at least one piece of target hardware; wherein the basis of parameterized filling comprises specific parameters of hardware feature fingerprints and optimized attribute tags carried in an intermediate representation of a unified operator description language (UDL); and generating and deploying multiple back-end codes. The method has the beneficial effects that intelligent compiling based on hardware features can be realized, and the deployment efficiency and the operation performance of a large model in a complex heterogeneous computing power cluster are remarkably improved.

A method, computer system, and a computer program product for data-free knowledge amalgamation are provided. Multiple pre-trained teacher machine learning models are obtained. Each is trained on a respective different set of training data. Pseudo-data samples that mimic original training data of the teacher models are generated. A block-wise amalgamation with a self-regulative strategy to integrate knowledge from the multiple teacher models is implemented by inputting the pseudo-data samples into the teacher models and into a student machine learning model. The implementing also includes aligning intermediate representations of the student model with a unified representation capturing relevant features from the teacher models.

The invention discloses a lightweight cross-platform PLC (Programmable Logic Controller) multi-language code generation method, which relates to the field of industrial process control, and firstly provides a unified intermediate data model, and on the basis, a module for converting each PLC language into the unified intermediate data model is realized; on the basis of a standardized JSON data structure, a modularized unified intermediate language conversion chain is further supported, an analyzer and a generator communicate through a JSON interface, a cross-platform conversion path is supported, and unified intermediate language conversion from PLC to JSON to XML to ST to C is completed; the method comprises the following steps of: integrating an open source compiler and runtime, constructing an open and pluggable automatic execution framework, compiling a generated C code into a platform executable file by using an adaptive GCC tool chain aiming at different target platforms, and deploying a program generated by compiling to a soft PLC runtime system supporting multiple platforms. According to the method, a JSON intermediate data structure which is light in weight, unrelated to languages and uniform in structure and a matched conversion framework of the JSON intermediate data structure are constructed.

The invention relates to the technical field of industrial control, and provides an industrial control logic intelligent generation system and method. The multi-modal input module is used for acquiring multi-modal data for a to-be-controlled process; the domain knowledge graph module is used for storing a pre-constructed knowledge graph; the preprocessing module is used for preprocessing the multi-modal data based on a pre-constructed knowledge graph; the controlled logic generation module is used for generating a logic control intermediate representation for a to-be-controlled process through a large language model according to the preprocessed multi-modal data; the formalized verification and interaction module is used for carrying out formalized verification on the logic control intermediate representation; and the target file generation and output module is used for compiling the logic control intermediate representation which is qualified through formalization verification into a control logic file aiming at the target platform. Under the constraint of the domain knowledge graph, the large language model can generate flexible and high-adaptability industrial control logic according to background knowledge to accurately adapt to different process requirements.

The invention discloses a multi-language PLC configuration software component library expansion method and system based on LLVM, and relates to the technical field of configuration software, the method comprises the following steps: obtaining language source codes of various types of programming languages supporting the LLVM front end; inputting the language source code of each type of programming language into a corresponding LLVM front-end compiler, and converting the language source code into a standard intermediate representation; establishing a general language characteristic analysis framework; establishing a general abstract metadata injection framework; building a mapping rule framework; integrating the general language framework, the general abstract metadata injection framework, the mapping rule framework and the output result, and generating a unified extended intermediate representation; and generating an interface component library conforming to the IEC standard. According to the method, any programming language with an LLVM front end is supported, a developer can directly utilize a mature algorithm library in each language ecosystem to select the programming language most suitable for a specific application scene, and the development period is remarkably shortened.

The invention belongs to the technical field of computer data security, and particularly relates to a data contract-oriented strategy real-time compiling and consistency hot deployment method and system.The method comprises the steps that according to a strategy input by a user, strategy definition and standardized representation are based on formalized semantics, and a data contract-oriented strategy is compiled in real time; constructing a strategy abstract syntax tree with complete semantic information; inputting the abstract syntax tree into a multi-stage compilation optimization pipeline, and generating a high-performance intermediate representation (IR) code by using a cost model driven optimization decision mechanism; a policy distribution architecture based on a publishing-subscribing model is adopted, and an improved Gossip protocol and a Paxos atomic submission algorithm are combined to realize distributed consistency synchronization and real-time hot deployment of a policy; and efficient and safe execution of the strategy is realized through a register type micro virtual machine and a self-adaptive execution mode. The problems that in the prior art, execution performance has bottleneck, strategy updating is delayed, complex strategy supporting capacity is weak, and cluster strategy consistency is difficult to guarantee are solved.

The invention discloses a digital circuit design method and device, equipment and a medium, and relates to the technical field of digital circuits, and the method comprises the steps: determining a target type of a to-be-designed digital circuit, and collecting a first training set corresponding to the target type; each training sample in the first training set comprises an input feature item used for designing a target type of historical digital circuit and the actual result quality of the historical digital circuit, and the input feature item comprises a high-level language source code and an HLS constraint parameter; constructing an initial prediction model based on the multi-layer perceptron mixed structure, training the initial prediction model by using the first training set, and searching and optimizing the model architecture based on the network structure to obtain a target prediction model; and predicting the actual result quality corresponding to each parameter combination in a target design space of a to-be-designed digital circuit by using the model, and designing the digital circuit by using the parameter combination of which the quality meets the Pareto frontier. And predicting an accurate actual quality result by utilizing a prediction model without intermediate representation so as to carry out digital circuit design.

The invention discloses a method and system for automatically generating headless test cases based on behavior description. The method comprises the following steps: obtaining an original behavior description text, and carrying out standardization processing on the original behavior description text to generate a standardized behavior description text; performing semantic analysis on the standardized behavior description text to generate a structured intermediate representation; matching each IR step in the structured intermediate representation with a preset step library, and generating a corresponding target step for each IR step; according to all the target steps, corresponding code templates are selected from a preset template library and combined in sequence, and headless automatic test cases are generated; and for an interface on which the headless automatic test case depends, selecting a matched Mock data version from the Mock snapshot warehouse according to the interface contract hash, and performing contract consistency verification. According to the method, through semantic analysis, step library matching and template combination, BDD description is fully automatically converted into executable scripts, and testers are liberated from repeated coding work.

The invention discloses a report generation agent method, system and device driven by a large language model and a medium, and the method comprises the steps: constructing an agent execution framework independent of a native interface, outputting a decision through a cue word constraint model in a JSON structure, and scheduling a local tool function; analyzing the reference data and constructing a local vector knowledge base supporting semantic retrieval; the intelligent agent autonomously plans and generates a report directory structure based on a local vector knowledge base, stores the report directory structure as a structured intermediate file as a persistent intermediate representation, generates chapter content in parallel by utilizing a concurrent flow control and exponential backoff retry mechanism and combining a mixed retrieval strategy, and serializes and updates the chapter content to the intermediate file in real time; and finally, converting the intermediate file into a target format report according to a mapping rule. The system, the equipment and the medium are used for implementing the method. According to the method, the efficiency and the accuracy of complex report generation are remarkably improved through agent whole-process autonomous driving and combination of persistent intermediate representation, retrieval enhancement and concurrency control technologies.

The invention relates to the technical field of large language models, in particular to a two-stage large model cognition enhancement method, system and device and a storage medium, and the method comprises the steps: receiving a user input request, and analyzing and decomposing the request into sequential cognition step sequences according to set cognition; based on the cognitive step sequence, using a large language model to generate intermediate representations and corresponding text candidates according to corresponding steps; evaluating the text candidates generated in each cognitive step, and when an evaluation result does not meet a preset threshold value, triggering a backtracking mechanism to guide the large language model to rewrite or correct the corresponding step; and storing the intermediate representation passing evaluation and verification and the corresponding final text in a track storage library, and finely adjusting the large language model by using a reinforcement learning method based on strategy optimization by taking data in the track storage library as a training sample. According to the method and the device, instability caused by full-text rewriting is avoided, so that the model can also produce the standard text when no prompt exists.

The invention relates to the technical field of instruction set conversion, and discloses an instruction set conversion method of a GPU heterogeneous environment based on simulation software. According to the method, the key instruction stream in the simulation task is accurately captured through the dynamic instrumentation method, and a solid foundation is provided for subsequent processing; the method comprises the following steps: dividing an original instruction stream into a plurality of instruction blocks to be converted through a classification mechanism according to instruction semantic features and hardware suitability, and creating conditions for parallel conversion; a lightweight front-end translator is adopted to efficiently convert classified instruction blocks into architecture-independent intermediate representations, the degree of parallelism and the data dependency relationship between instructions are reserved, the intermediate representation instructions are deeply optimized, and the method comprises the key steps of instruction selection, register allocation, instruction recombination, SIMT mapping, instruction coding and the like. And generating a native instruction of the target GPU architecture through accelerated translation of the target GPU thread block. According to the method, the instruction conversion period is shortened, and the correctness and the execution efficiency of the conversion result are improved.

Certain aspects of the present disclosure provide techniques for generating knowledge graphs from program source code. An example method generally includes receiving a source code definition of a workflow implemented in an application. The source code definition of the workflow is converted into an intermediate representation of the workflow, the intermediate representation comprising a condensed version of the source code definition. An abstract syntax tree representation of the workflow is generated based on the intermediate representation of the workflow. A structured file is generated by traversing the abstract syntax tree representation of the workflow, wherein the structured file comprises a definition of the workflow in a knowledge graph-specific language. The structured file is deployed to a knowledge graph execution engine.

The invention discloses a compiling method for compiling a C language source code into an RISC-V assembly code. The method comprises the following steps: acquiring a C language source code; performing lexical analysis on the C language source code to generate a mark flow; performing syntactic analysis on the mark flow, and constructing an abstract syntax tree; performing semantic analysis on the abstract syntax tree to generate a target abstract syntax tree; constructing a runtime environment of the RISC-V assembly code; generating an intermediate code and a control flow diagram corresponding to the intermediate code according to a rule in the runtime environment and the target abstract syntax tree; generating a target code by using the intermediate code and the control flow diagram corresponding to the intermediate code; according to the technical scheme, the intermediate representation more adaptive to RISC-V custom instruction mapping can be generated, so that the execution efficiency of assembly codes is improved; and meanwhile, by designing a lightweight runtime environment, the performance overhead is further reduced.

The invention provides a controller program generation method based on semantic planning and quality optimization, and belongs to the technical field of automation. A multi-stage PLC program generation and optimization method oriented to natural language task description is adopted, and semantic retrieval enhanced task planning, structured code synthesis and a quality closed-loop optimization mechanism based on feedback are fused. A natural language task is automatically converted into a verifiable plan with logic constraints by constructing a semantic index, reasoning scheduling and an intermediate representation structure; through module clustering and multi-round prompt guiding type logic reconstruction, generation of a high-quality PLC program meeting the IEC 61131-3 standard is realized. And static analysis and test feedback are combined, and the code quality is evaluated and enhanced by adopting iterative optimization and a scoring mechanism, so that the problems of intelligence, structuring and verification of the whole process from natural language to PLC program are systematically solved, and the development efficiency, the program robustness and the feasibility of industrial deployment are remarkably improved.

The invention relates to the technical field of financial science and technology and artificial intelligence cross fusion, in particular to a financial format file processing method and device based on a large language model, and the method comprises the steps: receiving a file uploading or generating instruction, responding to the uploading instruction, carrying out format recognition and integrity verification, storing, and generating a ready signal; and in response to the generation instruction, constructing a cue word containing financial scene parameters, calling a large language model to generate content and generating a signal. And extracting a text through an adaptive analyzer based on the signal, filtering redundancy, converting into a standard JSON format, and driving a large language model to proofreading in combination with a special prompt word in the financial field to generate a compliant text. And after converting into a structured HTML intermediate representation and checking, analyzing an HTML matching template, filling placeholders, mapping into a Word format of a financial specification, and outputting a file which conforms to the business specification through special checking. Full-process automation is achieved, efficiency is improved, compliance risks are reduced, and the method is suitable for multiple scenes.

The invention provides a large language model operator compiling method oriented to a GPU platform, and belongs to the field of GPU operator compilation. A large language model operator is compiled through a Python interface to obtain a Python code; performing lexical analysis and grammatical analysis on the Python code to generate an abstract syntax tree; mapping the abstract syntax tree into an MMIR extension intermediate representation; tensor layout in the MLIR extension intermediate representation is uniformly converted into a binary linear mapping matrix, and optimization problems of global memory layout, shared memory layout and register block layout are analyzed based on the binary linear mapping matrix; automatically deducing a non-key tensor layout based on a program control flow diagram, generating a global optimal layout scheme, and obtaining an optimized MMIR extension intermediate representation; enabling the optimized MLIR extension intermediate representation to pass through a GGPU (Graphics Graphics Processing Unit) target code generator of the MLIR, and generating a PTX code adaptive to the NVIDIA GPU; the PTX code is loaded to a GPU, and an operator calculation result is output; the invention further provides a compiling system. The problem that the current large language model operator compiling efficiency is low is solved.

The invention provides a hybrid code compiling optimization method and device, electronic equipment and a program product. The method comprises the steps of obtaining a to-be-compiled hybrid code; tile language main body codes and hardware domain codes in the mixed codes to be compiled are recognized and distinguished; calling a hardware domain function in the hardware domain code by the Tile language main body code through a set function calling interface; generating a Tile intermediate representation by analyzing the Tile language main body code; compiling the hardware domain code into a first bottom layer intermediate representation by using a hardware language compiler declared in the to-be-compiled mixed code, and packaging the first bottom layer intermediate representation into the Tile intermediate representation; performing hardware resource mapping on the Tile intermediate representation by the Tile compiler to generate a second bottom intermediate representation; and after the first bottom-layer intermediate representation and the second bottom-layer intermediate representation are inline, standardizing the first bottom-layer intermediate representation and the second bottom-layer intermediate representation into executable codes by the Tile compiler.

The invention relates to the technical field of data processing, and discloses a compiling method and system for generating hardware accelerator executable codes facing a multi-mode large language model, a terminal and a storage medium. The method comprises the following steps: constructing a computational graph according to the structure of a to-be-deployed large language model and operators supported by a hardware platform; traversing the computational graph to deduce output tensor type information of each operator, and verifying the type consistency between adjacent operators; based on the verified type information, executing static memory allocation to determine a static storage address of the weight data, and executing dynamic memory allocation to determine a dynamic storage address of the state data; then combining the verified type information and the static and dynamic storage addresses to generate an intermediate representation instruction sequence for configuring a hardware acceleration platform register; and finally, converting the intermediate representation instruction sequence into a target code file which can be directly loaded and executed by a hardware acceleration platform. The compiling efficiency, the deployment reliability and the resource utilization rate are improved.

The invention discloses a calculation graph memory layout automatic optimization method and system based on double-layer intermediate representation, and relates to the technical field of artificial intelligence model memory layout optimization. Aiming at the defect that the memory layout optimization effect in the existing AI compiler is limited, the scheme adopted by the invention comprises the following steps of: constructing a computational graph based on a given deep learning model; the calculation graph is converted into logic intermediate representation, and graph optimization is executed; converting the optimized logic intermediate representation into a physical intermediate representation, and adding a memory layout descriptor to each tensor; candidate execution configuration is generated for the whole computational graph through a memory optimizer, an optimal scheme is decided, and physical intermediate representation is reconstructed; and analyzing the reconstructed physical intermediate representation, automatically inserting a memory release operation after calculating the final use position of the tensor in the graph, and finally compiling the physical intermediate representation to generate an executable code of the target hardware. The method is used for realizing automatic optimization of the memory layout of the computational graph.

A method for compiling a computational graph, and a related product. The method comprises: acquiring a computational graph to be compiled that is expressed by a second intermediate representation, performing forward inference of a shape, and on the basis of the forward inference and tensor data splitting information, obtaining complete shape information; using the complete shape information to determine whether the tensor data splitting information needs to be adjusted; on the basis of a determination result, determining the tensor data splitting information that meets requirements; on the basis of the tensor data splitting information that meets the requirements, performing memory access pattern derivation on operators in the computational graph; on the basis of a derived memory access pattern, determining address-domain-related parameters of instructions involved in loops in code logic of the computational graph; performing pipeline scheduling on the instructions in the loops; and on the basis of the address-domain-related parameters of the instructions involved in the loops and a pipeline scheduling result of the instructions, compiling the computational graph, so as to obtain a binary file recognizable by an intelligent processor.

The invention provides a code detection and protection method, system and device and a medium, and belongs to the technical field of malicious code detection.The method specifically comprises the steps that WASM byte codes are obtained; converting the byte code into a WAT text and an intermediate representation, and analyzing meta-information; respectively constructing control flow diagrams and program dependency diagrams of the JS and the WASM based on the AST and the intermediate representation, and fusing the control flow diagrams and the program dependency diagrams into a unified dependency diagram through cross-language interface nodes; matching the dependency graph by using a preset malicious rule base, and marking a suspicious level; if not, WASM is injected into a Unikernel mirror image to be executed in an isolation environment, and behavior fingerprints during operation are collected; and outputting a malicious and non-malicious conclusion by combining the static analysis result with dynamic feature rating such as resource consumption and abnormal events. Through cross-language dependency graph fusion and dynamic and static combination analysis, malicious behaviors in WASM and JS interaction are captured, execution and behavior collection are isolated, the misjudgment risk is reduced, the detection accuracy is improved, and efficient and accurate WebAssembly module protection is achieved.

The invention discloses a multi-source information driven self-adaptive interface test script generation method. The method comprises the steps that S1, multi-source input fusion is carried out, one or more kinds of test information are obtained from multiple data sources, cross validation and intention alignment are carried out on the basis of semantic understanding, and multi-source test information is generated; s2, unified protocol conversion: converting the multi-source test information into framework-independent standardized intermediate representation through a general protocol conversion layer, and dynamically generating executable scripts of different test frameworks; s3, intelligent scheduling and execution: executing in a local or distributed cluster according to the task scale intelligent scheduling script, and collecting an execution data set; and S4, performing exception attribution and feedback, analyzing an execution result, performing three-dimensional analysis through a multi-source fusion exception attribution engine if the execution result fails, accurately judging that a failure reason is interface update or system Bug, and triggering a corresponding closed-loop response. According to the invention, intelligent generation, cross-framework multiplexing and adaptive maintenance of the test script are realized.

A computer-implemented method for generating a control program for an automation system includes receiving input data for a control program of an automation system by a development environment, generating the graphical diagram of the graphical programming language based on the input data with the aid of a graphical editor unit, displaying the graphical diagram in the graphical editor unit, translating the graphical diagram into a textual intermediate representation of the graphical diagram by a translation module, displaying the textual intermediate representation of the graphical diagram in a textual editor unit, and generating the control program based on the textual intermediate representation of the graphical diagram, and / or based on the graphical diagram.