118 results about "Pseudocode" patented technology

The invention relates to the technical field of large model application, and discloses a code generation task adaptive reasoning method, device and equipment. The method comprises the following steps: obtaining a problem description of a code generation task, and converting the problem description into a pseudo code based on a large language model; according to the problem description and the corresponding pseudo code, based on a pre-trained complexity evaluation model, performing complexity evaluation on the code generation task; the complexity evaluation model is obtained through training by taking problem description and pseudo codes as input and task complexity as output; a reasoning mode is matched according to the complexity evaluation result, a set reasoning model is adopted, and a code generation task is executed based on the matched reasoning mode; wherein different complexities correspond to reasoning modes of different reasoning depths. According to the method, the dynamic perception of the large language model on the task complexity is realized, so that the reasoning depth and the resource allocation can be adaptively adjusted according to the actual demand of the task.

The invention provides a code generation method and system based on a waterfall model and multi-agent collaboration, and the method comprises the steps: constructing a multi-agent collaboration framework which comprises a problem analysis agent, a solution agent, a pseudo-code agent, a coding agent and a restoration agent based on a software development waterfall model thought; and the interaction process of each agent is coordinated through a dynamic cooperation algorithm. Wherein the problem analysis intelligent body checks similar problems and solutions; the solution intelligent agent generates and evaluates candidate schemes; performing scheme conversion by the pseudo-code intelligent agent; the coding agent generates an executable code; and the repair agent performs fine-grained repair on the code in grammar, runtime and semantic levels through a two-dimensional repair mechanism. According to the method, the limitation of a single agent in a complex programming task is broken through, automatic code generation covering the whole process of demand analysis, scheme design, code implementation and test repair is realized, and the quality of generated codes and the reliability of operation are remarkably improved.

Automatic code generation for robotic process automation (RPA) that uses a cognitive artificial intelligence (AI) layer to automatically generate computer program code, text, or other items based on an input source is disclosed. Expressions and / or code snippets may be generated for RPA workflows that provide code in a programming language or convert code from one language to another. The input source may include natural language sentence(s), source document(s), pseudocode written by a user for a desired task, etc. These inputs may be used to modify an RPA workflow, generate a new RPA workflow, create a document describing a task, generate an application, etc.

The invention relates to a signal equipment fault diagnosis rule automatic generation method, system and equipment, and the method comprises the steps: a demand cleaning processing process: carrying out the semantic analysis of an original fault diagnosis demand of signal equipment, and generating a structured output containing a demand pseudo code and a dependency graph based on a recognition result of the demand; the demand searching process is used for constructing a demand vector and a knowledge graph; performing similarity search on the vectorized query demand to obtain similar demand nodes for a user to select a root demand, and performing deep search in the knowledge graph based on the root demand to generate a fault tree structure and fault tree data; and an intelligent rule automatic generation process: generating prompt words based on the fault tree data construction rule, and generating a signal equipment fault diagnosis rule code by using a large language model. Compared with the prior art, the method has the advantages that the signal equipment fault diagnosis rule is automatically generated, and the development efficiency is improved.

An apparatus comprises at least one processing device configured to select a test case associated with a test script comprising functional code for testing an information technology asset and a test case description. The at least one processing device is also configured to generate, utilizing a first machine learning model, first pseudocode for the test script based at least in part on the functional code of the test script, and to generate, utilizing a second machine learning model, second pseudocode for the test script based at least in part on the test case description. The at least one processing device is further configured to determine a stability of the test script based at least in part on a similarity between the first and second pseudocode, and to automatically control one or more characteristics of the functional code of the test script based at least in part on the determined stability.

The invention discloses a query condition analysis method and device and a medium, and the method comprises the steps: receiving a natural language query statement input by a user for a human resource management system, and extracting a core semantic unit in the natural language query statement; based on a preset knowledge base, mapping the core semantic unit into a semantic structure corresponding to an Elasticsearch database to generate a corresponding key vector diagram; based on a preset pseudo code normal form, generating a pseudo code according to the key vector diagram; wherein the pseudo code comprises a query node set and a logical relationship between query nodes; and performing code verification on the pseudo-code, and converting the pseudo-code into a DSL query statement in an Elasticsearch database under the condition that the pseudo-code passes the code verification, so as to realize semantic analysis on the natural language query statement through the DSL query statement.

The invention discloses an auxiliary programming teaching method and system based on a large model. The method comprises the steps that a programming code test data set is constructed, the large model is selected, and a cue word template is constructed; collecting a user code error correction problem and an error code, generating an intelligent review, correct code or pseudo code cue word, inputting the intelligent review cue word into the first large model to obtain intelligent review information, and inputting the correct code or pseudo code cue word into the second large model to obtain a correct code or pseudo code; generating a detailed guidance cue word, and inputting the first large model to obtain detailed guidance information; generating a key dial cue word, and inputting the key dial cue word into the first large model to obtain key dial information; and generating knowledge point cues, inputting the knowledge point cues into the first large model to obtain knowledge point information, and sequentially pushing intelligent examination questions, key dials, detailed guidance, correct codes or pseudo codes and knowledge points to the user according to user cognitive logic. The invention relates to the field of artificial intelligence, a large model can be applied to digital programming teaching, and the accuracy of the model is improved.

The invention relates to a low-orbit satellite short burst signal high-precision capturing method and device, equipment and a medium. The method comprises the following steps: sequentially carrying out digital down-conversion and L-time signal pseudo code rate down-sampling on a low earth orbit satellite short burst signal sequence sampled by an ADC (Analog to Digital Converter) to obtain a down-sampled signal sequence; performing pre-accumulation processing on the down-sampling signals of every adjacent L points in the down-sampling signal sequence, and performing coarse capture on the pre-accumulated signal sequence by adopting a PMF-FFT algorithm to obtain a coarse pseudo code phase and a coarse frequency offset value; and performing serial code phase search on the down-sampling signal sequence based on the coarse pseudo code phase and the coarse frequency offset value, optimizing to obtain a fine pseudo code phase, and optimizing the estimation precision of the coarse frequency offset value according to the fine pseudo code phase and a frequency offset estimation algorithm based on a phase relation to obtain a fine frequency offset value. By adopting the method, the acquisition precision and speed of the short burst signal of the low-orbit satellite can be improved.

Techniques for generating proprietary software code using large language models (LLMs) are disclosed. An LLM is trained on billions of words, tokens, and code segments to generate non-proprietary software code. A system uses the LLM to generate proprietary software code by generating a set of LLM prompts and a proprietary code mapping. Based on receiving an instruction to generate a set of software code, a system generates a set of LLM prompts. The system prompts the LLM to generate a set of non-proprietary software code. The system further prompts the LLM to generate a set of pseudocode from the non-proprietary software code. The system further prompts the LLM to generate proprietary software code from the pseudocode and the proprietary code mapping.

The invention discloses an automatic mechanism model construction method and system based on a large model, and relates to the technical field of mechanism model construction, and the method comprises the steps: generating a retrieval statement related to a target fault based on an equipment type and extracted fault information; using a mixed retrieval mode to retrieve related maintenance regulations and cluster the related maintenance regulations to obtain a new maintenance regulation set; extracting fault judgment logic and elements in the maintenance regulation set by using the large model, summarizing the judgment logic, and generating a mechanism model pseudo code; and analyzing the mechanism model pseudo code, and converting the mechanism model pseudo code into a mechanism model DSL to form a mechanism model. The method has the beneficial effects that the efficiency, accuracy and correlation of knowledge retrieval can be improved, and the accuracy of extracting fault information and excitation logic by a large model is improved.

The invention relates to the technical field of computer program detection, in particular to a binary program vulnerability detection method and system based on a large decompilation model and EAST features. A vulnerability function data set and a system call function data set are used for performing fine adjustment on a pre-trained large decompilation model LLM4Decompact; performing grammar and semantic optimization on the pseudo code which is output by the anti-compiler Ghidra and is subjected to the decompilation by using the LLM4Decompile; extracting a corresponding enhanced abstract syntax tree EAST from the decompilation pseudo-code after grammar and semantic optimization of the decompilation large model after fine tuning, and performing feature coding on the enhanced abstract syntax tree by using an EASTNN model; and inputting the feature vectors of the coded non-obfuscated binary program to be detected and the vulnerability function data set into a Siamese network for similarity calculation, and evaluating the vulnerability of the non-obfuscated binary program according to a similarity calculation result. According to the method, by using the fine-tuned decompilation large model and the enhanced abstract syntax tree feature coding, the accuracy and efficiency of non-confusion binary program vulnerability detection are improved.

The invention relates to the field of communication, in particular to a spread frequency hopping signal capturing method based on Doppler and spread frequency hopping pseudo code search. Comprising the following steps: step 1, down-conversion; step 2, searching for a large step frequency; step 3, caching the data at regular time; step 4, de-hopping; step 5, downsampling; step 6, searching for a small step frequency; step 7, sliding correlation; step 8, interpolation: performing high-power interpolation processing on the sliding correlation output sequence; step 9, phase rotation: carrying out phase rotation processing on the data segments before coherent accumulation; step 10, performing coherent accumulation during a frequency hopping cycle; step 11, carrying out incoherent accumulation between symbols; and step 12, inter-symbol capture judgment. According to the invention, the bandwidth of the data to be processed is effectively reduced, and the purposes of saving hardware resources and improving the capture accuracy of the spread frequency hopping signals are achieved.

The invention relates to the field of computers, and provides a software security detection method and system based on code lines, and the method comprises the steps: obtaining a binary file of to-be-detected software, and carrying out the preprocessing of the binary file, and obtaining a corresponding pseudo code; performing multi-level code line generation on each function in the pseudo code to obtain a code line vector of each function; based on the code line vector of each function, performing similarity retrieval in a preset code line library, and performing risk assessment and attribution analysis on the binary file of the to-be-detected software according to a retrieval result; and generating a security detection report of the to-be-detected software based on the evaluation result and the analysis result. According to the method, the binary file of the to-be-detected software is converted into the pseudo code, deep analysis can be performed on the pseudo code, and multi-level code line generation is performed on each function in the pseudo code, so that similarity retrieval can be performed in the preset code line library on the basis of the generated code line vector; therefore, the accuracy, efficiency and comprehensiveness of software security detection are improved.

The invention provides an automatic vulnerability detection method based on an intermediate pseudo code and a graph neural network. The method comprises the following steps: converting an assembly code of a binary program into a pseudo code; identifying a sensitive function; constructing a data dependency graph (PDG), and extracting a data dependency sub-graph by taking the key function as a central node; generating a graph semantic embedding vector by combining a pre-training model CodeT5-small and a graph attention network (GAT), and generating a graph structure embedding vector by using a node2vec algorithm; training is carried out through a double-branch feature analysis model based on a graph convolutional neural network (GCN) and a classification model of a multi-layer perceptron (MLP); and detecting vulnerabilities by using the trained model. According to the method, the sensitive function vulnerability of the binary program can be distinguished under the condition that source codes do not exist, the accuracy and efficiency of automatic vulnerability mining are remarkably improved, the limitation of a traditional automatic vulnerability mining method is effectively solved, and the method has important practical application value.

The invention belongs to the technical field of computer security, and particularly relates to a multi-level feature fusion binary code similarity detection method and system.The method comprises the steps that two binary files to be detected are obtained, the two binary files to be detected are disassembled respectively, and code representations of the two binary files are obtained; code representations of the two binary files are preprocessed and then input into the trained binary code similarity detection model, and a similarity score is obtained; the binary code similarity detection model comprises a CFG branch, a pseudo code branch, a structural feature extraction module and a feature fusion module; according to the method, characteristics of assembly instructions and pseudo codes are respectively extracted through CFG and pseudo code branches to capture bottom-layer and high-layer information, the similarity between the assembly instructions and the similarity between the pseudo codes are calculated according to the characteristics, and different levels of information are fused by fusing the similarity between the assembly instructions and the similarity between the pseudo codes. And the robustness and the detection precision of binary code similarity detection are improved.

Methods, apparatuses, and computer-program products are disclosed. The method may include transmitting, to a first generative artificial intelligence (AI) model, a request to convert a natural language description of a generative AI behavioral policy into a pseudo-code expression of the generative AI behavioral policy, where the generative AI behavioral policy describes conditions and actions to be performed based on the conditions; transmitting, to a second generative AI model, a prompt generated based on the pseudo-code expression of the generative AI behavioral policy, a user request, and an instruction that the second generative AI model is to generate a response to the user request; and receiving, from the second generative AI model and based on the prompt, an output of the second generative AI model and the output conforms with the user request and the pseudo-code expression of the generative AI behavioral policy.

The invention provides a key node identification method and device in an industrial control system, equipment and a medium. The key node identification method comprises the steps of performing reverse analysis on firmware codes of control equipment to obtain a control logic configuration file; disassembling the control software firmware code to obtain a software disassembling code; performing code labeling, code slicing and code translation on the software disassembly code to obtain a class C pseudo code; performing code analysis on the class C pseudo code to obtain a software structure configuration file; performing file analysis on the control logic configuration file and the software structure configuration file to obtain a service logic relationship between the control equipment and the control software and a service logic relationship between the control equipment and the field equipment; constructing a business logic map based on the business logic relationship between the control equipment and the control software and the business logic relationship between the control equipment and the field equipment; and identifying a key control link in the service logic map, and carrying out key node positioning on the key control link. Therefore, key nodes are effectively positioned.

The invention relates to an ESG questionnaire scoring method, device and system based on an interpreter. The method comprises the steps of obtaining enterprise label information filled by an enterprise user; according to the enterprise label information, taking a plurality of ESG indexes matched with the enterprise user in an index library as target indexes, and obtaining a target index set; according to the target index set, generating questionnaires, and sending the obtained questionnaires to the user side; analyzing the assignment logic pseudo code bound with the target index set through an interpreter to generate a target list; and obtaining answer sheet information filled by the enterprise user for the questionnaire, executing the pseudo-code scoring script in the target list through an interpreter to score the answer sheet, and obtaining the score of each ESG index in the target index set. Through the dynamic configuration mode of the pseudo codes and the use of the interpreter, the adjustment of the scoring logic is separated from the system development, and the dependence on professional developers is reduced, so that the development and maintenance cost of the system is effectively reduced.

The invention provides a method for detecting security risks of firmware and related equipment. The method comprises the following steps: acquiring an in-firmware partition mirror image of first firmware; according to the partition mirror image in the firmware, obtaining files in the partition mirror image, file type information of the files in the partition mirror image and pseudo code files of the files in the partition mirror image; and determining a first type engine matched with the pseudo-code file based on the file type information, and performing security risk detection on the pseudo-code file through the first type engine. According to the method for detecting the security risk of the firmware and the related equipment, source-code-free deep semantic analysis is realized, so that a more accurate security risk detection result can be obtained.

The present invention proposes a synchronization system for DSSS pseudocode signals, which includes a capture module, a pseudocode positioning module, and a pseudocode tracking module; the synchronization method is as follows: the capture module captures the pseudocode symbols in the DSSS signal; the pseudocode positioning module preliminarily locates the first pseudocode symbol in the first pseudocode signal; and the pseudocode tracking module obtains the synchronization result of the DSSS pseudocode signal. The three spectrum enhancement modules in the pseudocode tracking module of the present invention perform spectrum enhancement on the detection results of the spectrum data after the PMF-FFT operation of the three branches, thereby realizing the correction of the maximum frequency domain modulus values ​​of the three branches, thereby reducing the related gain scallop loss of the maximum frequency domain modulus value during the pseudocode tracking process, and effectively improving the synchronization accuracy; at the same time, the capture module does not require the participation of the FFT operation unit when capturing the pseudocode symbols in the DSSS signal, thereby reducing the delay of pseudocode capture and effectively improving the synchronization efficiency.

Embodiments of this application mainly relate to the field of industrial digitization, and in particular, to a macro script conversion system and method, an electronic device, a medium, and a program product. A receiving module is configured to receive a first macro script, a name of source software, and a name of target software inputted by a user, where the first macro script includes a macro script of the source software. A code translation agent module is configured to convert the first macro script into corresponding natural language and pseudocode. A code generation agent module is configured to create a macro script corresponding to the name of the target software based on the natural language and the pseudocode.

The present invention discloses a method and system for automatically constructing a mechanism model based on a large model, relating to the technical field of mechanism model construction. The method comprises generating a search statement about a target fault based on the equipment type and extracted fault information; using a hybrid search method to retrieve and cluster related maintenance procedures to obtain a new set of maintenance procedures; utilizing the large model to extract the fault judgment logic and elements in the maintenance procedure set, summarizing the judgment logic, and generating mechanism model pseudocode; parsing the mechanism model pseudocode and converting it into a mechanism model DSL to form a mechanism model. The method of the present invention can improve the efficiency, accuracy, and relevance of knowledge retrieval, and enhance the beneficial effect of the large model in extracting fault information and the accuracy of the excitation logic.

The present application relates to a GNSS pseudocode blind estimation method, apparatus, device, and storage medium. The method comprises: obtaining the pseudocode estimation value of the I / Q branch of each sampling point through pseudocode symbol judgment; for any branch, counting the pseudocode estimation values ​​of all sampling points in each code chip of the current branch, and obtaining the initial pseudocode estimation value of each code chip of the current branch through discrete integral operation; constructing two types of local code sequences corresponding to any code chip through positive and negative polarity assumptions and reconstructing and generating two types of local navigation signals; multiplying the received signal of each sampling point with the two types of local navigation signals of any code chip of the current branch point by point and accumulating them to obtain the two types of signal correlation values ​​of any code chip, and performing pseudocode symbol correction judgment on any code chip based on the difference between the two types of signal correlation values ​​to obtain the corrected pseudocode estimation value of any code chip of the current branch. This method can reduce the bit error rate and improve the accuracy of pseudocode estimation.

A machine-tuning system and method for integrating virtual simulations and physical operations are provided. An operation programming interface provides pseudo-codes based on a software framework, where the pseudo-codes include codes processing an interactive event operating process, and the software framework is compatible with virtual machine drivers and physical machine drivers. A processor imports the virtual machine drivers into the pseudo-codes, compiles it to be a virtual machine-tuning program and executes the virtual machine-tuning program to control virtual operations of the virtual machine in a 3D virtual environment, and imports the physical machine drivers into the pseudo-codes and compiles to be a physical machine-tuning program and executes the physical machine-tuning program to control physical operations of the physical machine, so a logic of the physical operations of the physical machine and the virtual operations of the virtual machine in the 3D virtual environment is the same.

The invention relates to the technical field of network security and software reverse engineering, and discloses a binary function symbol recovery method based on LLM fusion interprocess context. The method comprises the following steps: firstly, extracting multi-dimensional feature information from a target binary function, wherein the multi-dimensional feature information comprises a function body pseudo code, a call point context code snippet, call chain information and a character string parameter transmitted by a call point; then, the multi-dimensional information is structured and constructed into prompts, the prompts are input into a large language model which is finely adjusted by a specific task for reasoning, and finally candidate function names are generated. The invention further discloses an LLM fine tuning technology fusing the multi-dimensional context information. According to the LLM fine tuning technology, efficient parameter fine tuning is carried out on the basic model by constructing a training sample set containing a function body, a calling context and character string parameters. According to the method, the inter-process context information is systematically introduced, so that the accuracy and semantic fitness of binary function symbol recovery are remarkably improved.

The present invention relates to the field of image processing technologies, and particularly to an image processing method for Haar wavelet transform using a GPU with a SIMT architecture, which includes a GPU with a SIMT architecture; constructing a virtual data address using virtual addressing, changing the data synchronization method, partitioning the data, and adjusting the logical layout; corresponding the matrix data position to the data position during actual storage; and obtaining a data processing matrix according to the Haar wavelet transform formula and the obtained matrix mapping coordinates, and simultaneously obtaining data storage matrices in two directions of row and column. The present invention is based on the Haar wavelet to calculate the parallel formula for wavelet transform, and the parallel formula can be directly applied at the pseudocode level to complete the writing of pseudocode faster; and for the first time, it is proposed to partition the data, change the synchronization strategy for data parallel processing, and further improve the processing speed of wavelet transform for large-resolution images by changing the addressing method, synchronization method, and logical layout of the GPU.

This invention discloses a method and apparatus for rapidly generating a BMS simulated front-end driver. The method includes: acquiring general configuration information configured by a preset general configuration module, command configuration information preset by a preset command configuration module, function description information preset by a preset function description module, and timing configuration information preset by a preset timing configuration module; and generating a BMS simulated front-end driver based on the general configuration information, command configuration information, function description information, and timing configuration information. The rapid generation method for BMS simulated front-end drivers provided in this application describes each module using a fixed pseudocode template and then uses a code generation module for automatic code generation, significantly shortening the development cycle and ensuring consistent development quality. Furthermore, the pseudocode review process improves review quality, thereby enhancing the overall development quality of the driver.

The invention provides a pseudo-code-based program code generation method and device, electronic equipment and a storage medium, and the method comprises the steps: calling a preset grammar specification to analyze a pre-obtained demand pseudo-code, and obtaining a grammar structure corresponding to the demand pseudo-code; the preset grammar specification comprises the algorithm rule of the application field corresponding to the required pseudo code, so that at least two nodes included in the pseudo code and the conversion relation between the at least two nodes can be obtained through analysis; further, in a preset program code set, screening program code segments matched with the conversion relationship between the nodes in the grammar structures to obtain a plurality of target program code segments corresponding to the grammar structures; based on the nodes of the grammar structure and the multiple target program code segments, the target codes are obtained through combination, automatic implementation of the embedded system in software development is achieved, the development time is shortened, and the number of developers is reduced.

The provided is an anti-jamming processing architecture based on a dual-component fusion application of a satellite navigation signal. The anti-jamming processing architecture includes: a receiver matched reception module, configured to receive a satellite navigation signal and separate the satellite navigation signal into a pilot component and a data component; a data channel anti-jamming module, configured to design a filtering weight vector recursive formula and perform anti-jamming on the received data component based on an updated filtering weight vector; a pilot channel anti-jamming module, configured to design a filter weight update formula and perform anti-jamming on the received pilot component based on an updated filter weight; and a data fusion application module, configured to perform message solving and pseudocode ranging respectively based on an anti-jamming data component and pilot component, fuse a pseudocode ranging result and solved message information, and provide a fusion result for a user.

A method for generating an AI agent by using a large language model, performed by a processor, may comprise the steps of: receiving a request message from a user device; in response to the received request message, generating a pseudocode having a predefined schema and a predetermined data format by using the large language model; verifying the schema of the generated pseudocode; verifying the validity of the generated pseudocode; generating an executable code on the basis of the verified pseudocode; and executing the executable code. The step of verifying the schema of the generated pseudocode may comprise the steps of: identifying whether at least one step field or at least one dependency field is included in the pseudocode; identifying whether an essential field is included in a first step included in the at least one step field; and identifying whether the value of a type field included in the first step is one of predefined types.