89 results about "Macro" patented technology

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

The invention discloses a method for applying linear programming to CDN (Content Delivery Network) scheduling, which relates to the technical field of content delivery networks and comprises the steps of data preparation, strategy layer version smooth configuration, macroscopic layer and microscopic layer linear solution and online execution. Basic data are collected, cleaned and repaired, and a version change rule is set; the macroscopic layer constructs a linear programming model, and the cross-provincial bearing quota is solved with the aim of minimizing the cross-provincial cost; the micro layer takes the quota as a boundary and generates domain name class-node weight vectors in parallel; and adapting a routing request online through weighted rendezvous hashing and request features. According to the method, a dynamic cost matrix and a weight granularity control technology are integrated, the engineering problem of linear programming is solved, second-level response, approximate global optimal scheduling and accurate execution of floating-point-level weight are realized, memory overhead is reduced, smooth updating of a strategy and system stability are guaranteed, and CDN service quality and operation efficiency are improved.

The invention relates to the technical field of macro text processing, in particular to a non-macro identifier processing method in a macro reference row, electronic equipment and a medium, the method comprises the following steps: S1, obtaining storage information corresponding to each macro reference of the macro reference row, the storage information corresponding to the macro reference comprises a macro reference identifier, a line number in a macro expanded text of a macro reference line and an intra-macro identifier list corresponding to the line number in the macro expanded text of the macro reference line; step S2, obtaining non-intra-macro identifier positioning information and a corresponding to-be-added macro reference identifier in a macro expanded text of the macro reference line; and S3, adding the non-intra-macro identifier positioning information to an intra-macro identifier list corresponding to the corresponding to-be-added macro reference identifier. According to the method, the non-intra-macro identifier binding object after row expansion can be accurately and quickly referenced for the macro, and a debugging function is realized based on the binding object.

The invention relates to the technical field of macro text processing, in particular to an intra-macro identifier position information acquisition method, electronic equipment and a medium, and the method comprises the following steps: setting a corresponding macro virtual file for each macro reference in a chip design code original file; loading a chip design code original file, and obtaining initial position information of an identifier in each macro based on a macro virtual file corresponding to each macro reference; expanding each target macro reference in a target code line in the original file of the chip design code to obtain a target macro expanded text; obtaining a column position offset and a row position offset corresponding to each target macro reference based on the target macro expanded text and the macro initial expanded text of each target macro reference; and generating position information of the intra-macro identifier in each target macro reference in the target macro expanded text based on the column position offset and the row position offset corresponding to each target macro reference and the initial position information of the intra-macro identifier in each target macro reference.

A microprocessor includes a prediction unit (PRU) comprising a fetch block (FBlk) predictor (FBP) that predicts a sequence of FBlks, each FBlk having a corresponding fetch block start address (FBSA), and branch predictors; a macro-op (MOP) cache (MOC) includes MOC entries (MEs) including multi-FBlk MOC entries (MF-MEs) for holding MOPs decoded from instructions of multiple FBlks. The PRU detects a hit of a current FBSA on an MF-ME; performs a set of actions K times: looking up the current FBSA in the FBP and branch predictors to obtain outputs, using the outputs to predict a successor FBSA of a successor FBlk; and making the current FBSA the successor FBSA; and predicts that an FBSA of a successor FBlk to the MF-ME is the current FBSA resulting from performing K times the set of actions. K is a number of FBlks built into the MF-ME (alternatively times a loop unroll factor).

The invention provides a script modification iterative optimization method and system and electronic equipment. The method comprises the following steps: analyzing an original script into a structured script data object; performing hierarchical decomposition and feature dimension distribution on the script optimization task; performing macroscopic analysis on the script data object based on a task decomposition scheme, generating a global evaluation result containing an improved strategy, and converting the global evaluation result into scene-level operation guidance; analyzing each scene based on operation guidance to generate modification suggestions, and integrating the modification suggestions to form a unified modification scheme; performing routing classification on the modification suggestions according to content types based on the scheme; executing modification operation for different elements of the script according to the suggestion distribution instruction; and evaluating the quality of the optimized script, and iteratively optimizing the script until a quality evaluation result meets a convergence condition. The problem of low generation quality caused by insufficient complex task processing capability, incoherent narration, difficulty in context consistency maintenance and the like in a single generation process and a normal form of an existing long script generation method is solved.

The invention relates to a malicious software detection method based on multi-feature fusion and interpretability analysis. According to the technical scheme, static features are extracted through'macroscopic-microscopic 'double paths, and a dynamic behavior knowledge graph is constructed in combination with sandbox monitoring, stain analysis and semantic abstraction and converted into feature vectors through graph embedding; multi-modal feature deep interaction and accurate classification are realized through a double-end cross attention-triple fusion-deep classification architecture; a hierarchical interpretable framework is constructed based on cross-modal causal alignment, case reasoning and anti-fact analysis, and a complete decision evidence chain is generated. The method has the advantages that comprehensive representation of the form, semantics and intention of malicious software is achieved, the detection precision and robustness are remarkably improved, meanwhile, the'black box 'dilemma of the model is solved, credible explanation is provided for security analysis, the method is suitable for complex network threat detection scenes, and it is verified through experiments that the method has good application prospects. According to the method, the detection rate of the Windows malicious software is remarkably increased, and the false alarm rate is effectively reduced.

The embodiment of the application provides a kind of iOS classification attribute's adding method and device, electronic equipment and storage medium, belong to software technical field.The method comprises: according to the first macro code file of object type attribute and the second macro code file of basic data type attribute obtained according to preset basic macro code file;Basic macro code file contains the implementation method of variable attribute;According to the first macro code file and the second macro code file, determine the content code file set to be declared;According to the attribute description of classification name and variable attribute, obtain the declaration configuration code file package of iOS classification from the content code file set to be declared;According to declaration configuration code file package, obtain corresponding target macro code file installation package;According to target macro code file installation package, the classification attribute of iOS is added.The embodiment of the application can reduce the installation package size of iOS project development, improve the accuracy of installation package code, facilitate popularization and application.

The embodiment of the invention provides an instruction processing method and device, electronic equipment and a readable storage medium. Under the condition that a compiling environment is a simulator environment, if the actual data bit width of an operand corresponding to a to-be-checked instruction is not matched with the general data bit width corresponding to a target architecture, the to-be-checked instruction is not matched with the general data bit width corresponding to the target architecture; if yes, mapping the operand corresponding to the to-be-checked instruction to a first macro assembly function which corresponds to the to-be-checked instruction and is matched with the actual data bit width of the operand corresponding to the to-be-checked instruction, and generating a target pseudo instruction; the target pseudo instruction is used for performing symbol expansion check on an operand corresponding to the to-be-checked instruction; and under the condition that the instruction execution result of the target pseudo instruction is a first result representing that symbol expansion is incorrect, positioning and symbol expansion optimization are performed on the instruction to be checked. Errors introduced by symbol extension are positioned and solved under the condition that the compiling environment is a simulator environment, it is ensured that all potential risks are eliminated in the simulation debugging compiling stage, errors in actual operation are avoided, and the development efficiency is improved.

The application is suitable for the technical field of network drama auditing, and provides an artificial intelligence full-process auditing method and system for network drama, which comprises the following steps: analyzing a script based on a natural language model, and constructing a script knowledge graph comprising a macro-story layer, a meso-plot layer and a micro-element layer; the macro-story layer is used for representing the core theme and value orientation of the whole drama; the meso-plot layer automatically divides the script into multiple plot units; the micro-element layer is used for representing the entity elements and element development track within each plot unit; based on the script knowledge graph, dynamic risk identification is performed on the script to determine potential risk plot units; the rough cut sample and the script knowledge graph are aligned, and the risk index of the sample segment corresponding to the potential risk plot unit is calculated. The multi-level script knowledge graph constructed based on the natural language model can comprehensively and systematically understand the script content, and improves the accuracy and consistency of the auditing result.

This invention provides a method, apparatus, device, and storage medium for multi-branch parallel simulation and decision optimization based on a large language model. It constructs a dynamically evolving battlefield state space by receiving multi-source real-time situational data. Situational analysis identifies battlefield emergencies, which are then used as triggers to invoke the large language model to generate multiple differentiated decision branches, each containing macro-level objectives and micro-level strategies. For each decision branch, an independent parallel battlefield simulation instance is constructed and allocated computational and simulation resources, driving synchronous parallel simulations to obtain real-time simulation states. Multi-dimensional dynamic evaluation is performed to obtain a dynamic comprehensive score. Based on this, branches with scores continuously below a preset threshold are dynamically pruned, and their released computational and simulation resources are weighted and redistributed to surviving branches to improve their solution accuracy. Finally, high-scoring, high-quality branches are extracted, and the large language model is invoked to perform decision fusion on their key action sequences, outputting the optimal combat path and command sequence.

The invention relates to the technical field of data serialization, and particularly provides a structured storage fixed-length serialization method and system based on a Rust process macro, and the method comprises the following steps: analyzing declarative attributes in a structured data definition through the Rust process macro, and obtaining a preset fixed length of a variable-length field; based on the preset fixed length, automatically configuring the memory capacity for the variable-length field to avoid dynamic capacity expansion; converting the structured data into a byte sequence by adopting a fixed-length serialization algorithm, so that the total length of the byte sequence is fixed and is irrelevant to a field actual value; when the actual data length of the variable-length field exceeds a preset fixed length, intelligent truncation is performed on the actual data, and the data integrity is maintained; performing type verification on the serialization process in a compiling period by utilizing a type checking mechanism of a Rust compiler; the execution logic of the previous steps is generated in the compiling period through the Rust process macro, so that no redundant operation exists in the running process; according to the scheme, the fixed-length serialization processing of the structured data is comprehensively optimized.

The invention provides a design drawing generation system and a design drawing generation method. The design drawing generation system comprises a storage device and a processor. The processor is coupled to the storage device and is used for executing the parameter set generation module, the macro code generation module and the drawing rendering module. And the parameter set generation module analyzes the demand file to obtain design demand information. And the parameter set generation module performs correlation analysis on the design demand information and the sample data to obtain an analysis result, and matches a corresponding drawing type according to the analysis result to obtain a parameter set of the design drawing. And the macro code generation module generates a drawing macro code according to the design logic of the corresponding drawing type and the parameter set of the design drawing. And the drawing rendering module performs drawing rendering according to the drawing macro code to generate a design drawing.

The invention discloses an Office document detection method based on LLMs de-obfuscating macro, and belongs to the technical field of network security, the detection method comprises the following steps: based on a preprocessing module, obtaining an original macro code segment from an input sample; performing obfuscation perception on the original macro code segment to obtain a perceived code; performing structured segmentation on the perceived code to obtain macro code fragments; processing the macro code fragments based on a de-obfuscation module to obtain a de-obfuscation model; and based on the detection module, detecting the document by adopting an analyzed code and feature engineering method according to the de-obfuscation model. The problems that in the document detection technology in the prior art, too many computing resources are consumed, and confusion avoidance is difficult to detect are solved, and therefore the efficiency and accuracy of knowing confusion macros are improved.

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.

This invention relates to the field of macro text processing technology, and more particularly to a method, electronic device, and medium for obtaining the length of a macro-containing cross-module reference identifier. Step S1: Obtain the macro-containing cross-module reference identifier A to be processed. n The corresponding location information set; Step S2, if A n If the starting position is the location information involving macro references, then A10 will be... n Updated to A n The starting position is the location information in the original design file; otherwise, A10 n If A remains unchanged, n If the ending position is the location information involving macro references, then A20 will be... n Updated to A n The end position information in the original design file; otherwise, A20 n Keep it unchanged; Step S3, determine as A n Length R in the original design file n Step S4: Determine B n The length W in the macro expansion file n This invention can accurately and quickly obtain the length of cross-module reference identifiers containing macros.

The invention relates to a building engineering scheme natural language interactive editing and auditing method oriented to multi-role cooperation. The method comprises the steps of receiving natural language project description, generating a structured task workflow and allocating an initial responsibility role; receiving a multi-modal natural language instruction, and performing domain intention recognition and entity extraction; analyzing a to-be-executed construction engineering scheme editing and auditing task from the task workflow; generating an interactive response message containing the collaborative decision; receiving an interaction instruction, executing a compiling and auditing task corresponding to the interaction instruction, generating a task result, and performing dynamic updating and rendering; and synchronously loading and visualizing task workflow snapshots at moments corresponding to the historical dialogue records. The method has the advantages that microscopic task flow and responsibility division can be intelligently deduced from a macroscopic project target, the initial planning efficiency of scheme compilation is improved by one order of magnitude, and project management is fundamentally converted from experience driving to data and AI driving.

The invention discloses a method for encoding and decoding a field with unlimited length based on nanoopb, and belongs to the technical field of computers. According to the method, after a nanoopb tool automatically generates a code according to a. Proto file, a coding and decoding process is improved through the following steps: firstly, identifying all fields with unlimited lengths in a message, and replacing the data type of the fields with a self-defined dynamic array type; then replacing an initial value in the initialization macro and a callback function macro; and finally, realizing a unified custom callback function to process coding and decoding operations of all the fields with unlimited lengths. According to the method disclosed by the present invention, by packaging bottom layer Protobuf coding and decoding details, developers can complete serialization / deserialization operation without mastering complex coding rules and callback APIs, so that the learning cost is effectively reduced, the readability and maintainability of codes are significantly improved, and the method is suitable for resource-limited embedded system development.

The invention relates to the technical field of electronic design automation, in particular to a target text searching method supporting macro expansion, electronic equipment and a medium. And recording a line number mapping relation between the macro-expansion code line in the original code file and the macro-expansion code line in the macro-expansion code file, and in a target text searching process, obtaining a first searching position which may be the position of the target text in the original code file; and acquiring a second search position which may be the position of the target text in the macro expansion code file, then selecting a search position closest to the search initial position in the search direction from the first search position and the second search position as a target search position, and determining the to-be-searched text corresponding to the target search position as the target text. According to the method and the device, the target text searching efficiency supporting macro expansion is improved.

The invention discloses a multi-view fusion cloud native malicious software detection method and system based on high-speed introspection of a virtual machine, and the method comprises the steps: 1), deploying a high-speed introspection module in a monitoring layer of the virtual machine, capturing an API call sequence of an internal process of a target virtual machine from the outside in a safe and low-invasion manner, and structuring the API call sequence into a runtime log; (2) the API calling sequence is regarded as a sentence, and a Word2Vec model is used for training to generate a structure embedding vector of the API; then, constructing a directed heterogeneous graph containing a file, a thread and API calling for each sample, taking the structure embedded vector as an initial feature of an API node, encoding the graph by using a graph attention network, and extracting a structure context feature vector; 3) extracting an API official function description text by utilizing the pre-training language model to generate a semantic embedding vector; constructing a directed heterogeneous graph for each sample, replacing the initial features of the API nodes in the graph with the semantic embedding vector, coding the graph by using the graph attention network again, and extracting a functional semantic feature vector; 4) firstly performing function classification on the APIs, and performing dimensionality reduction on the complete API calling sequence to obtain a limited function state sequence; constructing a Markov transition probability matrix for the state sequence of each sample, and selectively stacking a multi-order transition matrix to form a multi-channel feature tensor; inputting the feature tensor into a convolutional neural network for coding, and extracting a macroscopic behavior evolution feature vector; 5) splicing the structure context feature vector, the function semantic feature vector and the behavior evolution feature vector to form a final comprehensive feature vector; and inputting the comprehensive feature vector into a multi-layer perceptron classifier, and training the classifier in an end-to-end manner to enable an output sample of the classifier to be a classification result of malicious software or benign software.

This invention relates to the field of macro text processing technology, and more particularly to a method, electronic device, and medium for processing macro identifiers. The method includes: obtaining macro identifier storage information corresponding to a currently enabled code region in a chip design code source file; the macro identifier storage information corresponding to the currently enabled code region includes the binding object and position information corresponding to each macro identifier in the currently enabled code region; searching for the macro identifier storage information corresponding to the currently enabled code region based on the position of the target macro identifier in the currently enabled code region, and determining the target binding object corresponding to the target macro identifier; and performing debugging operations based on the target binding object in the chip design code source file. This invention can accurately and quickly bind objects to expanded macro identifiers and implement debugging functions based on the bound objects.

This invention discloses a knowledge graph-based method for generating macro scripts for striped tube simulation and related products, belonging to the field of electromagnetic simulation automation technology. The knowledge graph-based method for generating macro scripts for striped tube simulation provided by this invention constructs a directed knowledge graph containing directed associations representing the calling order constraints between nodes. This allows simulation knowledge to go beyond semantic associations and possess explicit topological dependency logic. After retrieving the target constraint path, the extracted nodes are sorted according to the directed associations to form constraint prompts that conform to the calling order. This minimizes the probability of missing preliminary steps and incorrect method calling order, thereby guiding a pre-set large language model to generate executable electromagnetic simulation macro scripts with correct calling order and complete configuration chains. This improves the reliability, engineering applicability, and actual execution stability of macro script generation in simulation tasks.

The invention discloses a batch CFD data analysis method and device and a storage medium, and belongs to the technical field of computer-aided engineering. According to the method, based on a preset standardized naming rule, standardized naming is conducted on model components and boundary conditions of a geometric model, and the geometric model obtained after standardized naming is exported as a grid file capable of being recognized by a CFD solver; obtaining a CFD solving macro command template containing replaceable variables, dynamically modifying template variables according to the grid file and naming of the grid file, generating a special macro command, performing batch calculation, and exporting a result file according to a set format; and automatically reading all result files, summarizing and analyzing the data according to a naming analysis model and working condition information, and automatically generating a visual chart and an analysis report. According to the method, through standardized naming and scripted control, unattended operation automation of the whole process from the geometric model to the analysis report is achieved, and CFD simulation efficiency and traceability are remarkably improved.

The application relates to a method for performing memory safety dynamic analysis on a program containing specific sentences. The method comprises the following steps: selecting a project directory or a single source code file to be instrumented; preprocessing the source code, replacing macro calls with the content in the macro definition, and commenting out the original macro call; generating a symbol table and an abstract syntax tree of the source code by using a compiler; traversing all nodes in the abstract syntax tree, performing static analysis on the source code, and modifying the source code for sentences that cannot be processed, and then re-instrumenting the source code; traversing all nodes in the abstract syntax tree, performing different instrumentations on the source code according to different node types, compiling the instrumented project directory or file by using a compiler, generating an executable file on a target system, and running the executable file, performing memory error detection on the program containing specific sentences, and reporting the position of the source code corresponding to the error. The method avoids the problems of instrumentation failure and the failure of correctly compiling the program after instrumentation.

The application discloses a modeling method and device of a traction power supply system. The method comprises the following steps: using energy macro-expression modules to represent each component in the traction power supply system, so as to build a main circuit model, wherein each energy macro-expression module has an input port, an output port and a causal relationship; determining an adjustment path in the main circuit model according to a control target; and performing reverse step-by-step deduction according to the control target and the adjustment path, so as to deduce a control instruction required for realizing the control target. The application can accurately clarify the interaction relationship between each component, clearly represent the energy flow path of the system, and improve the simulation speed.

A multifunctional keyboard may include a platform. A multifunctional keyboard may include at least one mechanical actuator disposed on the platform, the at least one mechanical actuator configured to move between a first position and a second position, when the at least one mechanical actuator is in the first position, the keyboard is in a macro configuration and when the at least one mechanical actuator is in the second position, the keyboard is in a text input configuration. A multifunctional keyboard may include a first plurality of keys disposed on the platform, each of the first plurality of keys corresponding to a different macro layer when the keyboard is in the macro configuration. A multifunctional keyboard may include a second plurality of keys disposed on the platform, each of the second plurality of keys corresponding to a different macro for each macro layer selected by the first plurality of keys.

The invention relates to the technical field of scientific simulation, in particular to a finite element model parameter optimization method and system based on an intelligent algorithm, equipment and a medium, a traditional manual iterative optimization process is converted into an automatic process through parametric modeling and automatic generation of a Python macro command, the manual operation time is remarkably shortened, and the efficiency is improved. And meanwhile, seamless connection from parameterized modeling of the finite element model to optimization result output is realized, so that the dependence on a user algorithm and programming capability is reduced, and full-process automation is realized. Secondly, multi-objective optimization and nonlinear constraint are supported, common non-convex and high-dimensional optimization problems in engineering can be flexibly processed, and the limitation of a traditional gradient algorithm on the continuity of an objective function is broken through; the response surface agent model replaces high-time-consuming finite element calculation through chaos polynomial expansion, a black box problem is converted into an explicit mathematical relationship, the dependence of an optimization algorithm on actual simulation is reduced, and the iteration efficiency is improved.

The invention discloses an embedded multi-user cooperation development method based on RTOS, and the method comprises the steps: a linker specifies a code segment of a code link, and the code segment comprises a peripheral module and a task module; defining a specific macro, putting the declaration function address into a specific code segment in the code segments by adopting the specific macro, and sorting; defining callback functions of the peripheral module and the task module in the main function; and compiling a driver of the peripheral module and / or a driver of the task module according to the preset task function demand information. When the driver of the peripheral module and the driver of the task module are compiled, only the driver is created in the own module, and the main function does not need to be directly modified, so that the conflict risk caused by intensively modifying the main function in a traditional development mode is avoided; different functions are not directly coupled, so that later-stage codes are easier to test, reuse and migrate, and meanwhile later-stage function expansion and debugging positioning are facilitated.

The invention discloses a large language model collaborative decision-making method and system based on reinforcement learning driving, and belongs to the technical field of strategic game decision-making, and the method comprises the steps: building a large language model strategy: converting a game state into a structured text prompt, and generating an executable micro-action sequence; constructing a tool strategy: generating a predefined macro action based on the micro action sequence; and collaborative decision-making: cooperatively scheduling the large language model strategy and the tool strategy by adopting a near-end strategy optimization algorithm. An action self-adaptive adjustment mechanism is introduced, and the execution length of the micro action and the macro action is dynamically adjusted according to the game stage; and a layered reward function is designed, and dense and effective training signals are provided in combination with the overall winning rate reward and the local performance reward. According to the method, the problems of semantic conflicts, resource conflicts and attention offset existing in tool calling of a large language model are effectively solved, and the decision-making efficiency, the strategy coordination and the training stability are improved.

The application provides a point cloud coding method, device and storage medium. The method comprises the following steps: acquiring a point cloud, and performing block division on the point cloud to obtain a current block; acquiring first information of the current block, and determining whether to use a macro block coding mode to code the current block according to the first information of the current block. For example, according to at least one of the division depth of the current block, the size of the bounding box of the current block, the number of points in the current block and the point density in the current block, it can be accurately judged whether the current block is coded by using the macro block coding mode, thereby improving the coding efficiency of the current block.