85 results about "Pseudocode" patented technology

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.

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.

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 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 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.

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 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.

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.

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.

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.

Systems and methods for restructuring prompts in order to improve accuracy of outputs from models are disclosed herein. The system receives a user prompt indicating a request for data. The system generates a first and second output using a model, the first output generated based on the user prompt and the second output generated based on pseudocode. The system compares the first and second outputs to determine a match accuracy between the two outputs. If the two outputs sufficiently match, the system approves the user prompt. If the two outputs do not sufficiently match, the system initiates a prompt restructuring process, whereby the user prompt is restructured using pseudocode to improve the accuracy of the first output. The process is repeated iteratively until the restructured user prompt generates a first output that sufficiently matches the second output generated based on the pseudocode.

Techniques for processing a natural language query using an SMT solver that includes an LLM. The LLM processes the query text to formalize constraint text into pseudo code, which is processed by an SAT solver to determine logical atoms and propositional model for solving the query. The LLM then acts as a theory solver within the SMT solver to process the logical atoms and determine a valid solution for the natural language query.

This invention discloses a method and apparatus for acquiring medium- and long-code satellite navigation signals. The invention includes acquiring incident intermediate frequency (IF) signals from multiple down-converted satellites and constructing a satellite real matrix based on these signals; performing eigenvalue decomposition on the satellite real matrix using orthogonal trigonometric decomposition to determine the signal noise subspace; determining a Doppler frequency shift estimate based on the signal noise subspace and a preset signal frequency vector; determining a pseudocode phase offset using the extended replication overlap method, the Doppler frequency shift estimate, and a preset local pseudocode; and generating a target satellite navigation IF signal using the pseudocode phase offset and the Doppler frequency shift estimate. This invention solves the technical problem of excessively long acquisition time in existing medium- and long-code satellite navigation signal acquisition technologies.

An automatic software generation tool with improved search and retrieval capabilities for existing code generates, stores, and utilizes separate embeddings for code chunks and code chunk labels. Requirements for a new software application are used to generate a pseudocode for the new software application, and code chunks to be used in the new software application are identified using the pseudocode and the embeddings. The new software application is automatically generated using the identified code chunks.

This application relates to a method, apparatus, device, and medium for high-precision acquisition of short burst signals from low-Earth orbit (LEO) satellites. The method includes: sequentially performing digital down-conversion and... L The signal pseudocode rate is doubled for downsampling to obtain a downsampled signal sequence; each adjacent... L The downsampled signal is pre-accumulated, and the PMF-FFT algorithm is used to coarsely acquire the pre-accumulated signal sequence, obtaining coarse pseudo-code phase and coarse frequency offset. Based on the coarse pseudo-code phase and coarse frequency offset, a serial code phase search is performed on the downsampled signal sequence to optimize and obtain a fine pseudo-code phase. Then, based on the fine pseudo-code phase and a frequency offset estimation algorithm based on phase relationships, the estimation accuracy of the coarse frequency offset is optimized to obtain a fine frequency offset. This method can improve the acquisition accuracy and speed of short burst signals from low-Earth orbit satellites.

This application discloses a method and system for tracking spread spectrum signals, belonging to the field of signal processing. This application generates a spread spectrum digital signal by oversampling the acquired spread spectrum modulated signal. In-phase and quadrature components are generated by mixing, despreading, and coherent integration of this digital signal. Frequency and phase error information are calculated based on the components at continuous time intervals to correct the local carrier. The corrected local carrier is then used to mix the signal to baseband. An adjustable fractional delay is applied, and the instantaneous correlation energy peak is searched to determine the target fractional delay, thereby obtaining code phase error information. After correcting the local pseudocode using this code phase error information, the corrected local carrier and local pseudocode are used to initialize a narrow-bandwidth frequency-locked loop, phase-locked loop, and delay-locked loop, thus initiating precise tracking. This enables high-precision and stable locking of the spread spectrum signal in a short time.

A kind of mathematical formula parsing and code generation method based on abstract syntax tree, for the pseudo code of input file description control algorithm in the process of satellite-borne software development coding, with syntax tree structure as intermediate carrier, carries out independent identification, analysis, code generation, through the identification and analysis of the logic of control algorithm of mixed LaTeX syntax and C language syntax, code identification and processing are carried out with the inconsistent situation of C language function call with C language function call, which is represented by matrix operation, and the operation result is passed into the operation function in the form of pointer.

Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

The invention discloses a compiled binary code recognition method based on a graph network. The method comprises the following steps: acquiring a compiled binary code to be identified in a control system, performing decompilation to obtain a decompiled pseudo code and inheritance relationships of various types in the decompiled pseudo code, and constructing a member function table; constructing a software variable relation table based on the relation between the software variables of the classes; based on the software variable relation table, constructing a directed subgraph of the member function; aggregating the directed sub-graphs into a class level general graph according to the calling relationship; and performing variable semantic inference based on the class hierarchy general graph, and identifying the type and semantics of the compiled binary code so as to determine the function of the compiled binary code in the control system. According to the method, the directed subgraph and the class level general graph of the decompilation pseudo-code can be automatically constructed, then variable type semantic inference is carried out based on a graph network structure, the decompilation pseudo-code recognition problem can be systematically solved, and therefore more efficient and accurate technical support is provided for robot reverse engineering and software analysis.

The invention discloses a multi-agent system and a deep reinforcement learning load balancing method thereof, which reduce the complexity of agent selection action and can more easily achieve the optimal performance on the whole. According to the method, the request distribution is divided into two stages, the central agent selects the data center in the first stage, the local agent selects the node in the second stage, and therefore the problem that the multi-agent environment is unstable is avoided. In addition, request distribution of each agent is modeled into a Markov decision process, and definition of a state space, an action space, a reward function and state transition is described in detail. In the aspect of a network structure, a decision network is adopted, and compared with a single-flow Q network, the decision network has the advantage of being faster and better in learning. In the aspect of exploration and utilization, an improved load balancing-oriented action exploration strategy is used. Finally, a pseudo code and a learning process chart of the algorithm are given, and the training process of the algorithm is explained.

The present disclosure proposes a deep DSL and knowledge base-based large model front-end code generation method and system. The method comprises: receiving a user's front-end interface requirement; analyzing the front-end interface requirement through a requirement semantic analysis engine, and outputting a natural language requirement description; based on the natural language requirement description, calling a code large model and querying a DSL field knowledge base, selecting a high-score first input-output pair as a reference example to return to the code large model, and generating a front-end pseudo code; the code large model queries the code field knowledge base for each pseudo code segment in turn, selects a high-score second input-output pair as a reference example to return to the code large model, and generates a front-end code segment through the code large model; and combining each front-end code segment to generate the final front-end code and render. Through the method, the accuracy of large model front-end code generation is significantly improved, and the code generation is transformed from "vibe coding" to "viable coding".

The disclosure provides a large model-based code generation method, device and equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical field of large models and reverse engineering. The specific implementation scheme of the code generation method is: the pseudo code of the dynamic link library file is divided to obtain a plurality of pseudo code segments and at least one division position; wherein the pseudo code is obtained by disassembling and reverse analyzing the dynamic link library file; errors in the plurality of pseudo code segments are corrected based on a large model to generate a plurality of code segments; and the plurality of code segments are combined according to the at least one division position to generate target code; the target code is used to analyze whether the dynamic link library file is safe when running in a target system.

Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.