45 results about "Pseudocode" patented technology

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 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.

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.

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.

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 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 invention discloses a navigation signal compression and capture method based on a local pseudo code measurement matrix, and the method comprises the steps: firstly carrying out the cyclic shift of a navigation signal and a pseudo code, and respectively constructing a navigation signal sampling point matrix and a code phase matrix; and sparse transformation is carried out on the navigation signal through matrix multiplication between the navigation signal sampling point matrix and the code phase matrix to obtain a correlation matrix. Then, a greedy strategy is used for selecting M rows of vectors with the maximum code matrix and the lowest correlation coefficient to construct a measurement matrix, and compression measurement is carried out on the correlation matrix through the measurement matrix constructed through the greedy strategy; secondly, reconstructing the correlation matrix by using a subspace tracking algorithm and a least square operator; and finally, selecting the ratio of the primary peak value to the secondary peak value as a threshold value, and when the ratio of the primary peak value to the secondary peak value of the reconstructed correlation matrix is greater than the threshold value, outputting Doppler frequency offset and code phase offset. Otherwise, the navigation signals are captured again. According to the method, the maximum correlation coefficient of the measurement matrix can be reduced, the navigation signal capturing sensitivity is improved, and hardware resources in the signal capturing process in the navigation receiver can be reduced.

The invention provides a code generation method and device, equipment, a storage medium and a program product, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: inputting a pseudo code into a code generation model to generate a first candidate code; the error type of the first candidate code is determined, and the error type comprises a grammar error, a code logic error and a code intention error; according to the error type, adopting a corresponding correction strategy to correct the first candidate code to obtain a second candidate code; the second candidate codes are scored based on code quality attributes, and the code quality attributes comprise the conciseness degree of the second candidate codes and the adaptation degree of the second candidate codes and service scenes corresponding to the pseudo codes; and determining a finally generated code according to the scoring result of the second candidate code.

A visual programming platform can leverage a machine learning-based coding system to generate an initial set of programming-language code for further graphical editing by a human user. As an example, the visual programming platform can obtain a natural language description of a task to be performed by a computational pipeline. The visual programming platform can process the natural language description of the task with a machine learning coding system that includes one or more machine-learned models to generate, as an output of the machine learning coding system, a set of pseudocode that describes performance of the task. The platform can process the set of pseudocode that describes performance of the task with a compiler to generate a set of programming-language code that defines the computational pipeline for performing the task. The visual programming platform can generate a graphical visualization of the computational pipeline defined by the set of programming-language code.

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.

Ultra-low-latency video encoding and rate control methods and systems are configured to minimize latency in applications such as cloud gaming and real-time communication. The methods and systems enhance complexity estimation and buffer management. A dynamic rate controller architecture adjusts encoding based on complexity and buffer status, ensuring consistent bitrate and quality. Deep learning models predict optimal encoder instances and quantization parameter (QP) distributions, improving encoding efficiency. System architectures, pseudocode for bitrate and QP calculations, and training and testing phases for deep learning models are also provided.

Disclosed is an AI agent generation method using a large language model, which is performed by a processor. The method includes receiving a request message from a user device, generating a pseudo-code, which has a predefined schema and a predetermined data form, by using the large language model, in response to the request message thus received, verifying the schema of the pseudo-code thus generated, verifying validity of the pseudo-code thus generated, generating an executable code based on the pseudo-code thus verified, and executing the executable code.

A user may express their intent for a program using informal code representations, such as natural language and pseudocode, optionally combined with source code. An artificial intelligence runtime interprets the informal code representation to generate executable code. The user may be prompted by clarifying questions about the program if the artificial intelligence runtime infers that the program is a partial implementation or if ambiguity is present in the program. The informal code representation and the source code may be automatically revised based on the user's responses to the clarifying questions. Once the user has indicated that the program is performing as intended, the executable code associated with the program may be stored as validated executable code without the need to be represented in a higher-level language (e.g., Python, C#). The validated executable code can be used by the artificial intelligence runtime in subsequent executions of the program.

The invention relates to a multi-discriminator cooperative anti-interference pseudo code tracking method based on fuzzy control, and belongs to the technical field of satellite navigation and spaceflight measurement and control. The method specifically comprises the following steps: step 1, carrying out de-spreading and integral clearing processing on a received signal; step 2, using two bilateral slope anti-interference discriminator groups with different correlator distances to carry out pseudo code phase discrimination on the signal output in the step 1 to obtain a phase error estimation value; step 3, evaluating the dynamic size of the current carrier and the multi-access interference intensity; step 4, performing adaptive weighted fusion on the identification result based on fuzzy logic control; and 5, performing filtering processing by using the final identification result, and generating a pseudo code sequence.

The invention discloses a semantic retrieval method and device. The method comprises the steps that retrieval content is input into an atomic condition recognition model to generate an atomic condition information table; inputting the retrieval content semantic information into an external general large model to obtain an expression pseudo code; inputting the atomic conditions, the retrieval content and the expression pseudo code into an external general large model to obtain atomic conditions with classification types, and extracting and assembling according to semantic information to obtain a relationship among the atomic conditions; extracting retrieval elements from the semantic information of the retrieval content; classifying the expanded entity elements to obtain classification limiting conditions, and fusing the classification limiting conditions into an atomic condition information table to obtain an updated atomic condition relationship again; according to different application requirements and the expanded retrieval entity elements, a triple model, a quadruple model or a multi-tuple model is established to form a vector model; and according to a relationship between the vector model and the updated atomic condition, performing fusion retrieval to obtain a retrieval result. According to the method, semantic retrieval can be efficiently and accurately carried out.

The application provides a binary security patch detection method and system, electronic equipment and storage medium, and relates to the technical field of network security.The application can effectively reduce the differential noise introduced by variable renaming, compiler optimization and other factors by processing the pseudo code of the binary program through the iterative variable mapping and differential alignment algorithm, generate the high-quality pseudo code differential text after alignment, and provide clear and accurate input basis for subsequent security analysis;Further, through the large language model agent analysis mechanism, the program semantic information across functions and processes can be intelligently identified and utilized, the security impact of the patch is deeply reasoned, and finally the accurate detection and judgment of the binary security patch are realized.The application provides a security patch detection scheme which does not depend on the source code, can effectively process the binary program difference noise, and can comprehensively utilize the context information for accurate judgment.

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.

Ultra-low-latency video encoding and rate control methods and systems are configured to minimize latency in applications such as cloud gaming and real-time communication. The methods and systems enhance complexity estimation and buffer management. A dynamic rate controller architecture adjusts encoding based on complexity and buffer status, ensuring consistent bitrate and quality. Deep learning models predict optimal encoder instances and quantization parameter (QP) distributions, improving encoding efficiency. System architectures, pseudocode for bitrate and QP calculations, and training and testing phases for deep learning models are also provided.

This invention proposes a data processing method based on a reconfigurable processor with out-of-order iterative execution. The method includes: constructing a dynamically reconfigurable processor based on out-of-order iterative execution; annotating the memory access operators L0, L1, and L2 in the application pseudocode; where L0 represents the load operator A[i][j], L1 represents the load operator A[k][i], and L2 represents the load operator A[k][j]; executing the three operators L0, L1, and L2 serially and respectively deploying them on the memory access units LSU0, LSU1, and LSU2 of the dynamically reconfigurable processor; and performing out-of-order iteration based on the dynamically reconfigurable processor, wherein the out-of-order iteration operates in a pipelined manner, including four stages: fetch iteration, issue, execute, and write back. This method solves the problem of memory partitioning in non-template computation at its source, thereby improving the parallel data memory access capability of the reconfigurable processor.

Ultra-low-latency video encoding and rate control methods and systems are configured to minimize latency in applications such as cloud gaming and real-time communication. The methods and systems enhance complexity estimation and buffer management. A dynamic rate controller architecture adjusts encoding based on complexity and buffer status, ensuring consistent bitrate and quality. Deep learning models predict optimal encoder instances and quantization parameter (QP) distributions, improving encoding efficiency. System architectures, pseudocode for bitrate and QP calculations, and training and testing phases for deep learning models are also provided.

Systems and methods transform a user-defined program flowchart into executable code. A graphical authoring / import interface persists the flowchart as a typed graph of nodes and edges. A knowledge base is built by ingesting user-authorized schemas, code repositories, and API specifications, normalizing discovered elements as term objects with types and aliases. For nodes containing natural-language or pseudocode text, a domain-specific selection model, constrained to the knowledge base, selects canonical domain terms. The typed graph and selections are lowered to an intermediate representation and compiled into target-specific code including parameterized data-access statements and API invocations. The system synthesizes and executes unit and path tests, then assembles a deployable runtime package with a configuration manifest identifying dependencies, secrets, endpoints, and health probes. The package may be deployed as an rApp in a Service Management and Orchestration environment to analyze RAN KPIs and issue closed-loop control actions.

The present disclosure provides a log data verification method and device, electronic equipment, storage medium and product, relates to the field of automation, in particular to the field of data processing. The specific implementation scheme is: obtaining parameters for log point of a target application; generating pseudo code from the parameters, the pseudo code being used for recording log data by a client; and verifying the log data in response to receiving the log data sent by the client. The present disclosure can save the cost of log data verification.